CN111882907A - Navigation early warning method, device, equipment and storage medium for vehicle - Google Patents

Abstract

The present application relates to a navigation warning method, device, computer equipment and storage medium for a vehicle. After receiving the navigation warning analysis instruction carrying the navigation path sent by the client, the server obtains the driving information of the vehicles traveling on the navigation path within the preset time period, and analyzes the driving information of each vehicle to determine the driving information on the navigation path. The abnormal road section information including the abnormal road section type and the abnormal road section position is carried, and then the abnormal road section information is carried in the warning prompt information and sent to the user terminal. The navigation early warning method realizes the function of the user side navigating and giving early warning, that is, when the user starts the navigation function on the user side, the relevant early warning prompt information can be obtained, so that the user can predict the abnormal situation on the path ahead, which improves the user experience. The safety of driving vehicles.

Description

Navigation warning method, device, device and storage medium for vehicle

technical field

The present application relates to the field of travel technology, and in particular, to a navigation and early warning method, device, device and storage medium for vehicles.

Background technique

With the emerging development of the shared vehicle industry, people's demand for shared vehicles is increasing. In the process of providing shared vehicles and related services for people on the shared vehicle platform, how can it bring convenience to people's lives, and at the same time To better ensure the safety of people driving shared vehicles has become a concern in the current shared vehicle market.

Nowadays, when a user drives a shared vehicle, some shared vehicles provide a driving path for the user during the driving process, so that the user can quickly reach the destination. For example, the user can use the app on the user terminal to search for the driving route during driving, and then view the searched driving route on the display screen of the user terminal, or, the shared vehicle has a navigation function, and the user directly enables the navigation function on the shared vehicle , enter the destination information on the shared vehicle to learn the driving route.

However, the above-mentioned method of determining the travel route has a problem of low safety.

SUMMARY OF THE INVENTION

Based on this, it is necessary to provide a navigation warning method, device, device and storage medium for a vehicle that can effectively improve the driving safety of the vehicle in view of the above technical problems.

In a first aspect, an embodiment of the present disclosure provides a navigation warning method for a vehicle, the method comprising:

receiving a navigation early warning analysis instruction sent by the user terminal; the navigation early warning analysis instruction includes the navigation path to be analyzed;

acquiring the travel information of the vehicle traveling on the navigation path within a preset time period;

By analyzing the driving information of each vehicle, the abnormal road section information on the navigation path is determined; the abnormal road section information includes the abnormal road section type and the abnormal road section position;

The abnormal road section information is carried in the warning prompt information and sent to the user terminal.

In a second aspect, an embodiment of the present disclosure provides a navigation warning method for a vehicle, the method comprising:

Sending a navigation early warning analysis instruction to the server; the navigation early warning analysis instruction includes the navigation path to be analyzed;

Receive early warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the travel information of the vehicle traveling on the navigation path within a preset time period; The abnormal road segment information includes abnormal road segment type and abnormal road segment location;

Output the warning prompt information.

In a third aspect, an embodiment of the present disclosure provides a navigation warning device for a vehicle, the device comprising:

a receiving module, configured to receive a navigation warning analysis instruction sent by a user terminal; the navigation warning analysis instruction includes a navigation path to be analyzed;

an acquisition module, configured to acquire the travel information of the vehicle traveling on the navigation path within a preset time period;

a determining module, configured to determine the abnormal road section information on the navigation path by analyzing the driving information of each of the vehicles; the abnormal road section information includes the abnormal road section type and the abnormal road section position; the sending module is used to send the abnormal road section information The road section information is carried in the warning prompt information and sent to the user terminal.

In a fourth aspect, an embodiment of the present disclosure provides a navigation warning device for a vehicle, the device comprising:

a sending module, configured to send a navigation warning analysis instruction to the server; the navigation warning analysis instruction includes the navigation path to be analyzed;

The receiving module is configured to receive the warning prompt information sent by the server; the warning prompt information carries abnormal road section information, and the abnormal road section information is the information of the server according to the vehicle traveling on the navigation path within a preset time period The driving information is determined; the abnormal road segment information includes the abnormal road segment type and the abnormal road segment location;

The output module is used for outputting the warning prompt information.

In a fifth aspect, an embodiment of the present disclosure provides a user terminal, including: a transmitter, a receiver, a processor, an output device, a memory, and a computer program stored on the memory and executable on the processor; When the processor runs the computer program, it is used to control the operations of the transmitter, the receiver, and the output device;

The transmitter is configured to send a navigation early warning analysis instruction to the server under the control of the processor, where the navigation early warning analysis instruction includes the navigation path to be analyzed;

The receiver is configured to receive, under the control of the processor, early warning information that carries abnormal road section information sent by the server; the abnormal road section information includes the abnormal road section type and the abnormal road section location;

The processor is configured to control the output device to output the warning prompt information.

In a sixth aspect, an embodiment of the present disclosure provides a server, including: a receiver, a transmitter, a processor, a memory, and a computer program stored on the memory and executable on the processor; the processor is When running the computer program, it is used to control the operation of the transmitter, the receiver, and the processor;

The receiver is configured to receive, under the control of the processor, a navigation early warning analysis instruction sent by a user terminal, where the navigation early warning analysis instruction includes a navigation path to be analyzed;

The transmitter is configured to, under the control of the processor, send early warning information carrying abnormal road section information to the user terminal; the abnormal road section information is configured at a preset time according to the vehicle traveling on the navigation path The abnormal road segment information determined by the driving information in the segment; the abnormal road segment information includes the abnormal road segment type and the abnormal road segment location;

The processor is configured to acquire travel information of vehicles traveling on the navigation path within a preset time period, and determine abnormal road section information on the navigation path by analyzing the travel information of each vehicle.

In a seventh aspect, an embodiment of the present disclosure provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements the method described in the first aspect or the second aspect.

The navigation warning method, device, computer equipment and storage medium of the above-mentioned vehicle, after receiving the navigation warning analysis instruction carrying the navigation path sent by the user terminal, the server obtains the travel of the vehicle traveling on the navigation path within the preset time period By analyzing the driving information of each vehicle, the abnormal road section information including the abnormal road section type and the abnormal road section position on the navigation path is determined, and then the abnormal road section information is carried in the warning prompt information and sent to the user terminal. The navigation early warning method realizes the function of the user side navigating and giving early warning, that is, when the user starts the navigation function on the user side, the relevant early warning prompt information can be obtained, so that the user can predict the abnormal situation on the path ahead, which improves the user experience. The safety of driving vehicles. Moreover, since the warning prompt information includes the type and location of the abnormal road section, the warning prompt information not only plays a role of reminding the user to proceed with caution, but also specifically informs the user of the type and location of the abnormal road section, so that the user can understand the abnormal situation more intuitively. , and then flexibly choose whether to continue moving forward according to the abnormal situation, which greatly meets the needs of users.

Description of drawings

Fig. 1 is the application environment diagram of the navigation warning method of vehicle in one embodiment;

2 is a schematic flowchart of a navigation warning method for a vehicle in one embodiment;

3 is a schematic flowchart of an implementation manner of S103 in the embodiment of FIG. 2;

4 is a schematic flowchart of a navigation warning method for a vehicle in one embodiment;

5 is a schematic flowchart of a training method in one embodiment;

6 is a schematic structural diagram of an abnormal road section analysis network in one embodiment;

7 is a schematic flowchart of a training method in one embodiment;

8 is a schematic flowchart of a navigation warning method for a vehicle in one embodiment;

9 is a schematic flowchart of a navigation warning method for a vehicle in one embodiment;

10 is a schematic flowchart of a navigation warning method for a vehicle in one embodiment;

11 is a schematic flowchart of a navigation warning method for a vehicle in one embodiment;

12 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

13 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

14 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

15 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

16 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

17 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

18 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

19 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

20 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

21 is a structural block diagram of a navigation warning device for a vehicle in one embodiment;

Fig. 22 is the internal structure diagram of the user terminal in one embodiment;

Figure 23 is a diagram of the internal structure of the server in one embodiment.

Detailed ways

In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application will be described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, but not to limit the present application.

First, before introducing the technical solutions of the embodiments of the present disclosure in detail, the technical background or technical evolution context on which the embodiments of the present disclosure are based is introduced. Usually, in the field of shared vehicle travel, especially when shared bicycle travel, the current technical background (taking shared bicycle as an example) is: when a user rides a shared bicycle on the road, the riding user often encounters various A variety of abnormal scenarios, such as road damage, road potholes, road construction, etc. In this case, it is usually because the cycling users cannot predict various abnormal scenes on the riding road, which brings great safety hazards to the riding users, and the impact of the abnormal scenes on the riding road is also greatly reduced. Cycling user's riding comfort, for example, the riding user will feel very uncomfortable when the vehicle bumps due to potholes during riding. Based on the above technical problems, in the current shared vehicle platform market, how to ensure the convenience of users to use shared vehicles while also improving the safety and comfort of users driving shared vehicles has become an urgent problem to be solved. In addition, it should be noted that the applicant has devoted a lot of creative work to the process of obtaining the warning information and the technical solutions introduced in the following embodiments.

The following describes the technical solutions involved in the embodiments of the present disclosure in combination with the scenarios to which the embodiments of the present disclosure are applied.

The vehicle navigation warning method provided by the embodiment of the present disclosure can be applied to the application environment shown in FIG. 1 . The client 102 communicates with the server 104 through the network. The user terminal 102 may be a user terminal for controlling the use process of the vehicle, or may be a vehicle, and may also include a user terminal and a vehicle. User terminals can be, but are not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and vehicles can be, but are not limited to, various types of bicycles, electric vehicles, cars, etc. Take a bicycle as an example). The user terminal may be an independent device or an electronic device integrated on the vehicle; the vehicle may be provided with a communication component, which may communicate with the user terminal 102 and/or the server 104 in a wireless manner; the server 104 may It can be implemented with an independent server or a server cluster composed of multiple servers. The embodiment of the present disclosure does not limit the communication manner between the client 102 and the server 104 .

In one embodiment, as shown in FIG. 2 , a navigation warning method for a vehicle is provided. The method is applied to the server in FIG. 1 as an example to illustrate, including the following steps:

S101: Receive a navigation early warning analysis instruction sent by a user terminal; the navigation early warning analysis instruction includes a navigation path to be analyzed.

The navigation path to be analyzed is the path that the user needs to travel when driving the vehicle. The navigation warning analysis instruction is used to instruct the server to obtain the abnormal conditions that the vehicle may encounter when driving on the navigation path, and generate early warning information according to the abnormal conditions and send it to the client for early warning. The navigation path may be a path constructed by the user terminal in real time according to the user's navigation requirements and combined with the geographic map, or may also be a planned path obtained by the user terminal from the database according to the user's navigation requirements, which is not limited in the embodiments of the present disclosure.

Specifically, in practical applications, when the user drives a vehicle on the road, the user can activate the navigation function on the user end, and the user end obtains the navigation path of the vehicle after the user activates the navigation function, and generates navigation according to the navigation path. The warning analysis command is sent to the server. For example, the user may open a navigation application (Application, App) on the client side, input a travel destination, and click a navigation button. When the user clicks the navigation button, the user terminal can obtain the navigation path matching the driving destination, and immediately generate a navigation warning analysis instruction and send it to the server; for another example, the user can also input a voice password on the user terminal. After that, the user terminal automatically obtains the navigation path matching the driving destination, and immediately generates a navigation warning analysis instruction and sends it to the server.

S102: Acquire travel information of a vehicle traveling on a navigation path within a preset time period.

The preset time period may be a time period predetermined by the server according to the timeliness or accuracy of the acquired driving information, for example, the preset time period may be different time periods such as the current day, the last two days, and the last week. The driving information can represent the driving state of the vehicle, for example, whether the vehicle is driving at a high speed; it can also represent the driving route of the vehicle; it can also represent the environmental information around the vehicle, for example, the distance between the vehicle and the vehicle ahead . Wherein, the driving information includes the driving information of the vehicle that is currently driving on the navigation route, and may also include the historical driving information of the vehicle that has traveled on the navigation route.

Specifically, when the server receives the navigation early warning analysis instruction sent by the client, it can further extract the navigation path to be analyzed from the navigation early warning analysis instruction, and then obtain the preset time period from the database, on the navigation path Driving information reported by all vehicles or a preset number of vehicles that have been driven. Certainly, when the server obtains the driving information of the preset number of vehicles, the preset number of vehicles may be randomly selected, or the preset number of vehicles may be selected according to certain rules, so as to improve the accuracy of the server in predicting abnormal road sections. For example, the server may select a vehicle whose vehicle type matches the current vehicle type according to the vehicle type, and may also select a vehicle whose volume is the same as that of the current vehicle according to the volume of the vehicle, and so on.

S103 , by analyzing the driving information of each vehicle, determine the abnormal road section information on the navigation path; the abnormal road section information includes the abnormal road section type and the abnormal road section position.

The abnormal road segment information is used to indicate whether there is a road segment that cannot be driven or a road segment that is difficult to drive on the navigation route pre-traveled by the user, as well as the type and location of the abnormal road segment. The abnormal road segment types may include road segments of various abnormal scenarios such as pothole types, construction types, road damage types, accident types, and congestion types, and the like. The location of the abnormal road section may be the coordinate (latitude and longitude) position of the abnormal road section on the map, or may be the geographic location of the abnormal road section on the map, for example, the intersection of Jianguo Road, which is not limited here.

Specifically, when the server obtains the driving information of the vehicle traveling on the navigation path, it can further analyze the driving information of the vehicle, obtain the driving state of the vehicle, and then judge that the vehicle is traveling in the driving state according to the driving state of the vehicle. Whether an abnormal situation is encountered when navigating the route, if an abnormal situation is encountered, it means that the road section where the abnormal situation occurs is an abnormal road section, and the location of the abnormal road section can be determined, and then the server further determines the abnormal road section type according to the specific state of the vehicle. For example, the server can judge whether the vehicle is in a state of sudden braking on the navigation path based on the driving speed of the vehicle. If it does, it means that the road section in front of the vehicle braking is abnormal, that is, the corresponding road section in front of the braking is abnormal. , then the type of the road section in front of the brake is very likely to be a construction road section or an accident road section. Optionally, the server may also obtain the travel trajectory of the vehicle from the travel information of the vehicle, and then judge whether the vehicle travels according to the normal trajectory when traveling on the navigation path according to the travel trajectory of the vehicle, and if it does not travel according to the normal trajectory, It means that the vehicle has encountered an abnormal situation. Similarly, if an abnormal situation is encountered, it means that the road section where the abnormal situation occurs is an abnormal road section, and then the server further determines the type and location of the abnormal road section according to the driving trajectory of the vehicle. For example, if the navigation path is a straight path, the server can determine whether the vehicle is driving in a straight line from the driving track of the vehicle. An exception has been reached.

S104, the abnormal road section information is carried in the early warning prompt information and sent to the user terminal.

Among them, the warning prompt information is used to remind the user whether there is a road section that cannot be driven, or a road section that is difficult to drive, and the type and location of the road section that cannot be driven or the road section that is difficult to travel, so that the user can follow the warning prompt information. Judge for yourself whether to continue driving, or if you continue to drive, which sections of road you need to pay attention to and drive carefully. The above-mentioned road sections that cannot be driven, or road sections that are difficult to travel, are abnormal road sections.

Specifically, when the server determines the abnormal road section information on the navigation path by analyzing the driving information of each vehicle, the abnormal road section information can be directly carried in the early warning prompt information and sent to the user terminal for early warning; optionally, The server can also obtain other information related to the abnormal road section from the database (for example, image information of the abnormal road section, etc.), and then the server carries the information together with the abnormal road section information in the early warning information and sends it to the client.

In the above vehicle navigation warning method, after receiving the navigation warning analysis instruction carrying the navigation path sent by the user terminal, the server obtains the driving information of the vehicle traveling on the navigation path within the preset time period, and analyzes each traffic The driving information of the tool is used to determine the abnormal road section information including the abnormal road section type and the abnormal road section position on the navigation path, and then the abnormal road section information is carried in the warning prompt information and sent to the user terminal. The navigation early warning method realizes the function of the user side navigating and giving early warning, that is, when the user starts the navigation function on the user side, the relevant early warning prompt information can be obtained, so that the user can predict the abnormal situation on the path ahead, which improves the user experience. The safety of driving vehicles. Moreover, since the warning prompt information includes the type and location of the abnormal road section, the warning prompt information not only plays a role of reminding the user to proceed with caution, but also specifically informs the user of the type and location of the abnormal road section, so that the user can understand the abnormal situation more intuitively. , and then flexibly choose whether to continue moving forward according to the abnormal situation, which greatly meets the needs of users.

Optionally, the travel information in the foregoing embodiments specifically includes the travel path and/or travel state information of the vehicle.

The travel path represents the actual travel trajectory of the vehicle when the vehicle travels on the navigation path, which can be represented by a straight line or a curve. The driving status information includes various information that can represent the status of the vehicle during the driving process. For example, the driving status information may include the speed, acceleration, etc. of the driving speed of the vehicle; The degree of swing, etc.; the curvature of the route, the deflection angle, etc., which indicate the driving direction of the vehicle; the driving state information may include the vibration frequency and vibration intensity of the vehicle driving fluctuation; the driving state information may include the position and displacement of the vehicle driving track.

Optionally, the travel path of the vehicle in the foregoing embodiment is based on the Global Positioning System (Global Positioning System, GPS) positioning information reported by the vehicle, and/or, the positioning data output by the positioning sensor installed on each vehicle. Determined driving path.

Wherein, the GPS positioning information includes the moving position or moving coordinates of the vehicle. The positioning sensor is used to detect the moving position of the vehicle, and output positioning data including the moving position or moving coordinates of the vehicle.

Specifically, when the user drives the vehicle, the positioning device on the vehicle will report its own positioning information to the server in real time, and the server can obtain the vehicle according to the moving position or moving coordinates of the vehicle in the positioning information. The driving path during driving. When the vehicle is equipped with a positioning sensor for detecting the moving position or moving coordinates of the vehicle, the vehicle will also report the positioning data output by the positioning sensor to the server in real time during the driving process, and the server can use the positioning data in the traffic according to the positioning data. The moving position or moving coordinates of the tool obtains the traveling path of the vehicle during traveling. After the server determines the travel path of each vehicle, the travel path of each vehicle can be stored in the corresponding database as the travel information of each vehicle, so that the server needs to analyze the abnormal road section information on the navigation path later. The above-mentioned method for determining the driving path according to the positioning information and/or positioning data can realize the accurate positioning of the vehicle, thereby improving the accuracy of determining the driving path, and providing a reliable data source for the subsequent method of determining the abnormal road section information according to the driving path , thereby improving the accuracy of determining the abnormal road segment information.

Optionally, the driving state information in the foregoing embodiment is information obtained according to data collected by sensors installed on each vehicle.

Among them, the sensors installed on the vehicle may be various types of sensors, such as positioning sensors, speed sensors, acceleration sensors, angle sensors, curvature sensors, balance sensors, and vibration sensors. Also, the sensors on different vehicles may or may not be the same.

Specifically, the vehicle will also report the data output by various types of sensors installed to the server in real time during the driving process, and the server can obtain the driving state information of the vehicle according to the data. After the server determines the driving status information of each vehicle, the driving status information of each vehicle can be stored in the corresponding database as the driving information of each vehicle, so that the server needs to analyze the abnormal road section information on the navigation path later. . Since the above-mentioned driving status information includes data output by various types of sensors, the driving status of the vehicle can be reflected from multiple angles, so that the server can more accurately determine the driving status of the vehicle. The method of abnormal road section information provides a reliable data source, which can improve the accuracy of the determination of abnormal road section information.

It can be understood that the server can simultaneously obtain the GPS positioning information reported by the vehicle during the driving process and the data output by various types of sensors, and determine the corresponding navigation path and driving status information, and then simultaneously based on the navigation path and driving status information. , analyze the abnormal road section information on the navigation path, and improve the accuracy of judging the abnormal road section information.

Optionally, the embodiment of FIG. 3 provides a specific implementation manner of the foregoing S103. As shown in FIG. 3 , the implementation manner includes:

S201 , by analyzing the driving information of each vehicle, determine an abnormal vehicle; the abnormal vehicle has an abnormal driving path and/or an abnormal driving state.

The abnormal vehicle may include a vehicle with an abnormal driving trajectory on the navigation path. For example, if the navigation path is a straight path, and the actual driving trajectory of the vehicle on the navigation path is a curve, it means that the vehicle is traveling. If the path is abnormal, the vehicle is an abnormal vehicle. Abnormal vehicles may also include vehicles with abnormal driving conditions on the navigation path. For example, the speed of the vehicle when driving normally on the navigation path should be 60km/h, but in fact, the vehicle travels on the navigation path at a speed of 60 km/h. If the speed has changed, and the speed will be reduced from 60km/h until it stops, it means that the driving state of the vehicle is abnormal, then the vehicle is an abnormal vehicle.

Specifically, when the server obtains the driving information of each vehicle based on the aforementioned step S102, it can further analyze the driving path in the driving information of each vehicle to determine whether the driving path of each vehicle is abnormal. Half of the route on the navigation route has been traveled, and the travel route of the vehicle has a small inflection point compared to the navigation route. Optionally, it is also possible to further analyze the driving status information in the driving information of each vehicle to determine whether the driving status of each vehicle is abnormal. While driving on the navigation route, its direction is shifted, etc. Finally, vehicles with abnormal driving paths and/or abnormal driving states are screened out and determined as abnormal vehicles.

S202, according to the number of abnormal vehicles and a preset number threshold, determine the abnormal road section information on the navigation path.

The preset number threshold is determined by the server in advance according to the number of collected vehicles and the accuracy of the prediction. For example, if the server collects the travel information of 10 vehicles on the navigation route, the preset number threshold can be set to a value greater than 7, which is only an example and not a limitation.

Specifically, when the server determines the abnormal vehicles traveling on the navigation path, it can compare the number of abnormal vehicles with the preset number threshold. The probability of occurrence of abnormal conditions is extremely high, and there are abnormal sections on the navigation path; if the number of abnormal vehicles is less than or equal to the preset number threshold, it means that the probability of abnormal conditions occurring on the navigation path is low, and there are no abnormal sections on the navigation path. When the server determines that there is an abnormal road section on the navigation path, the abnormal road section information on the navigation path can be determined by analyzing the driving path and/or the driving state of the abnormal vehicle.

The above-mentioned method for determining the abnormal road section information on the navigation path is a probability statistics method, that is, by counting the number of abnormal vehicles to predict whether an abnormal situation occurs on the navigation path. This method collects a large amount of data for analysis and judgment, and has high accuracy.

Specifically, when the server executes the foregoing step S102 and the obtained travel information of the vehicle contains different contents, the server determines the abnormal vehicle in different ways, and the corresponding server determines the abnormal road section information in different ways. The following examples will be exemplified.

Example 1, when the driving information of the vehicles acquired by the server includes the driving paths of each vehicle, the server determines the abnormal vehicle mode (a specific implementation of the above S201), which specifically includes: comparing the driving paths of each vehicle with the navigation The paths are compared, and a vehicle whose driving path is inconsistent with the navigation path is determined as an abnormal vehicle.

The inconsistency between the driving path and the navigation path may indicate that the shape of the driving path and the navigation path are inconsistent, or that the direction of the driving path and the direction of the navigation path are inconsistent, or that the lengths of the driving path and the navigation path are inconsistent, and so on. For example, the navigation path is straight and the driving path is curved; the navigation path is from south to north, and the driving path is from north to south; the navigation path is long and the driving path is short.

Specifically, when the server obtains the travel path of each vehicle, it can further determine whether the travel path of each vehicle is abnormal by comparing the shape, length, or direction of the travel path and the navigation path. It means that the vehicle is an abnormal vehicle. The above method for determining an abnormal vehicle can be realized only by comparing the navigation path and the driving path, the process is simple, and the speed of the server to identify the abnormal vehicle is improved, which in turn can improve the speed of the server to identify the abnormal road section on the navigation road section.

Correspondingly, the method for the server to determine the abnormal road section information (a specific implementation of the above-mentioned S202) specifically includes: if the number of abnormal vehicles is greater than a preset number threshold, determining the location of the abnormal road section as the driving path and the navigation path of the abnormal vehicle. Inconsistent road segment locations, and determine the abnormal road segment type as construction type or accident type.

Among them, the road section of the construction type represents an impassable road section, or a road section that cannot be passed in a straight line and needs to be detoured.

Specifically, when the server determines that the driving path of the abnormal vehicle is inconsistent with the navigation path, it can further determine the position of the abnormal road section on the navigation path by determining the inconsistent road section position on the navigation path. For example, the shape of the driving path and the navigation path are inconsistent. , the driving path has an inflection point compared with the navigation path, then the road segment corresponding to this inflection point is an inconsistent road segment location, that is, an abnormal road segment location. When the driving path is inconsistent with the navigation path, the abnormal situation that usually occurs is that there is a construction section on the navigation path or a section where an accident occurs, causing the vehicle to detour or change the route when driving to the construction road section, which will cause the vehicle to travel. The driving path is inconsistent with the navigation path, then in this case, the road segment type whose driving path is inconsistent with the navigation path is directly determined as a construction type or an accident type. Obviously, the above method for determining abnormal road section information is realized by comparing the driving path and the navigation path, and can quickly and accurately identify abnormal road sections with construction types or accident types, and the location of each abnormal road section.

Example 2, when the driving information of the vehicles acquired by the server includes the driving state information of each vehicle, the server determines the abnormal vehicle mode (a specific implementation of the above S201), which specifically includes: The value is compared with a preset state threshold to determine the abnormal vehicle.

The value of the state information is quantified data representing the state of the vehicle, for example, the value of the state information may be a value of speed, a value of acceleration, a vibration frequency, and the like. The preset state threshold is a value of state information that the vehicle has when the vehicle is in a normal driving state on the navigation path, which can be used to evaluate whether the driving state of the vehicle on the navigation path is abnormal.

Specifically, when the server obtains the state information of each vehicle, it can further determine whether the driving state of each vehicle is abnormal by comparing the value of the state information with the preset state threshold. If an abnormality occurs, it means that the vehicle is running When abnormal driving occurs on the navigation path, the vehicle is an abnormal vehicle. In the above method for determining an abnormal vehicle, since the driving state information can directly and accurately reflect the state of the vehicle, the method for determining the abnormal vehicle by analyzing the driving state information of the vehicle can improve the accuracy of the server's identification of the abnormal vehicle. degree, thereby improving the accuracy of identifying abnormal road segment information later.

Correspondingly, the method for the server to determine the abnormal road section information (a specific implementation of the above-mentioned S202) specifically includes: if the number of abnormal vehicles is greater than the preset number threshold, then according to the driving state information of the abnormal vehicles, determine the navigation path. Abnormal road section information.

In detail, if the information contained in the driving state information of the abnormal vehicle is different, the methods of determining the abnormal vehicle mode (a specific implementation of the above S201)) and the abnormal road section information (a specific implementation of the above S202)) are also different. , the following embodiments describe different ways of determining abnormal vehicles and ways of determining abnormal road section information according to different driving state information.

Example 3: When the driving state information of each vehicle acquired by the server includes the vibration frequency and/or the balance, the server determines the abnormal vehicle mode (a specific implementation of the above-mentioned S201), which specifically includes: if the vibration frequency is greater than the preset If the vibration threshold, and/or the balance degree is less than the preset balance degree threshold, it is determined that the vehicle is an abnormal vehicle.

The preset vibration threshold is the vibration frequency that the vehicle has when it is in a normal driving state on the navigation path, and the preset balance threshold is the balance degree that the vehicle has when the vehicle is in a normal driving state on the navigation path, which can be used for Evaluate whether the driving state of the vehicle on the navigation path is abnormal.

Specifically, when the server obtains the vibration frequency of each vehicle, it compares the vibration frequency with the preset vibration threshold. If the vibration frequency is greater than the preset vibration threshold, it means that there is an abnormality in the road section when the vehicle is driving on the navigation path (for example, , bumps), resulting in an increase in the vibration frequency of the vehicle, thereby determining that the driving state of the vehicle is abnormal, and determining that the vehicle is an abnormal vehicle. When the server obtains the balance degree of each vehicle, it compares the balance degree with the preset balance degree threshold. If the balance degree is greater than the preset balance degree threshold, it means that there is an abnormality in the road section when the vehicle is driving on the navigation path, causing traffic The balance of the tool is lowered, so that it is determined that the running state of the vehicle is abnormal, and the vehicle is determined to be an abnormal vehicle. In the above method for determining an abnormal vehicle, since the vibration frequency and the balance degree can be acquired by using an existing sensor with high precision for data collection, the method for determining the abnormal vehicle by analyzing the vibration frequency and the balance degree of the vehicle can be used. The recognition accuracy of the server for identifying abnormal vehicles is improved, thereby improving the accuracy of subsequent identification of abnormal road section information.

Correspondingly, the method for the server to determine the abnormal road section information (a specific implementation of the above-mentioned S202) specifically includes: if the number of abnormal vehicles is greater than a preset number threshold, determining that the location of the abnormal road section is the target road section in the driving path of the abnormal vehicle. location, and determine that the abnormal road segment type is a pothole type; the target road segment location is the location of the road segment where the vibration frequency is greater than the preset vibration threshold in the driving path of the abnormal vehicle, and/or the location of the road segment whose balance degree is less than the preset balance degree threshold value .

Among them, the road section of the pothole type represents the road section that is difficult to pass. When the vehicle travels through the road section, the vibration frequency of the vehicle will increase and the balance will decrease.

Specifically, when the server determines the abnormal vehicle, it may further determine the position of the road section where the vibration frequency of the abnormal vehicle is greater than the preset vibration threshold in the driving path, and directly determine the position of the road section as the abnormal road section position, or determine the abnormal position. The position of the road section where the balance degree of the vehicle is smaller than the preset balance degree threshold value in the driving path is directly determined as the abnormal road section position. In practical applications, when a vehicle is driven on a bumpy or bumpy road, the vibration frequency of the vehicle is usually increased and the balance of the vehicle is reduced. In this case, the vibration frequency or balance is directly increased. The type of abnormal road section on which the lowered vehicle travels is determined to be a pothole type. Obviously, since the vibration frequency or the balance degree can accurately reflect the bumpiness of the vehicle, the above-mentioned method of determining the abnormal road section information by analyzing the vibration frequency or the balance degree can accurately identify the abnormal road sections with potholes, and The location of each abnormal road segment.

Example 4: When the driving state information of each vehicle obtained by the server includes curvature and/or angle, the server determines the abnormal vehicle mode (a specific implementation of the above S201), which specifically includes: if the curvature is greater than the preset curvature threshold, And/or, if the angle is greater than the preset angle threshold, it is determined that the vehicle is an abnormal vehicle.

Wherein, the preset curvature threshold is the curvature of the travel track when the vehicle is in a normal driving state on the navigation path, and the preset angle threshold is the angle of the driving direction of the vehicle when the vehicle is in a normal driving state on the navigation path, which can be used for Evaluate whether the driving state of the vehicle on the navigation path is abnormal.

Specifically, when the server obtains the curvature of the travel trajectory of each vehicle, it compares the curvature with the preset curvature threshold. If the curvature is greater than the preset curvature threshold, it means that there is an abnormality in the road section when the vehicle is traveling on the navigation path. , resulting in an increase in the curvature of the traveling trajectory of the vehicle, thereby determining that the traveling state of the vehicle is abnormal, and determining that the vehicle is an abnormal vehicle. When the server obtains the angle of the driving direction of each vehicle, it compares the angle with the preset angle threshold. If the angle is greater than the preset angle threshold, it means that there is an abnormality in the road section when the vehicle is traveling on the navigation path, causing traffic The angle of the traveling direction of the tool increases, so that it is determined that the traveling state of the vehicle is abnormal, and the vehicle is determined to be an abnormal vehicle. In the above method for determining an abnormal vehicle, since the curvature and angle can be acquired by using existing sensors with high accuracy for data collection, the method for determining an abnormal vehicle by analyzing the curvature or angle of the vehicle can improve the server's ability to identify abnormal vehicles. The identification accuracy of the vehicle is improved, thereby improving the accuracy of identifying abnormal road section information later.

Correspondingly, the method for the server to determine the abnormal road section information (a specific implementation of the above-mentioned S202) specifically includes: if the number of abnormal vehicles is greater than a preset number threshold, determining that the location of the abnormal road section is the target road section in the driving path of the abnormal vehicle. The location of the abnormal road section is determined to be a construction type; the target road segment location is the location of the road segment with the curvature greater than the preset curvature threshold in the driving path of the abnormal vehicle, and/or the location of the road segment with the angle greater than the preset angle threshold.

Specifically, when the server determines the abnormal vehicle, it may further determine the position of the road segment where the curvature of the traveling trajectory of the abnormal vehicle is greater than the preset curvature threshold, and directly determine the location of the road segment as the location of the abnormal road segment. Determine the position of the road section where the angle of the driving direction of the abnormal vehicle is greater than the preset angle threshold in the travel path, and directly determine the position of the road section as the abnormal road section position. In practical applications, when the curvature of the vehicle's driving trajectory on the navigation path increases or the angle of the driving direction increases, the abnormal situation that usually occurs is that there are road sections with construction or accidents on the navigation path, resulting in traffic accidents. If the curvature of the travel track of the tool increases or the angle of the travel direction increases, in this case, the abnormal road segment type on which the vehicle travels with the increased curvature or the increased angle is directly determined as the construction type. Obviously, since the curvature or the angle can accurately reflect the state of the vehicle detouring or the sudden change of direction, the above-mentioned method of determining the abnormal road section information by analyzing the curvature or angle can accurately identify the abnormality of the construction type or accident type. road segments, and the location of each abnormal road segment.

Example 5: When the driving state information of each vehicle acquired by the server includes speed and/or acceleration, the server determines an abnormal vehicle mode (a specific implementation of the above S201), which specifically includes: if the speed is less than the preset speed threshold, And/or, if the acceleration is less than the preset acceleration threshold, it is determined that the vehicle is an abnormal vehicle.

The preset speed threshold is the lowest speed when the vehicle is in a normal driving state on the navigation path, and the preset acceleration threshold is the lowest acceleration when the vehicle is in a normal driving state on the navigation path, which can be used to evaluate traffic Whether the driving state of the tool on the navigation path is abnormal.

Specifically, when the server obtains the speed of each vehicle traveling on the navigation path, it compares the speed with the preset speed threshold. If the speed is less than the preset speed threshold, it means that there is a road segment when the vehicle is traveling on the navigation path. If the abnormality is detected, the speed of the vehicle is reduced, so that it is determined that the driving state of the vehicle is abnormal, and the vehicle is determined to be an abnormal vehicle. When the server obtains the acceleration of each vehicle traveling on the navigation path, it compares the acceleration with the preset acceleration threshold. If the acceleration is less than the preset acceleration threshold, it means that there is an abnormality in the road section when the vehicle is traveling on the navigation path. The acceleration that causes the vehicle to travel is reduced, so that it is determined that the driving state of the vehicle is abnormal, and the vehicle is determined to be an abnormal vehicle. In the above method for determining an abnormal vehicle, since the speed and acceleration can be acquired by using an existing sensor with high accuracy for data collection, the method for determining an abnormal vehicle by analyzing the speed or acceleration of the vehicle can improve the server's ability to identify abnormal vehicles. The identification accuracy of the vehicle is improved, thereby improving the accuracy of identifying abnormal road section information later.

Correspondingly, the method for the server to determine the abnormal road section information (a specific implementation of the above-mentioned S202) specifically includes: if the number of abnormal vehicles is greater than a preset number threshold, determining that the location of the abnormal road section is the target road section in the driving path of the abnormal vehicle. location, and the abnormal road segment type is determined to be a narrow type; the target road segment location is the location of the road segment where the speed of the abnormal vehicle is less than the preset speed threshold, and/or the location of the segment where the acceleration is less than the preset acceleration threshold.

Among them, a narrow type of road section represents a road section that is prone to congestion, and when a vehicle travels through the road section, the speed or acceleration of the vehicle will decrease.

Specifically, when the server determines the abnormal vehicle, it may further determine the position of the road section where the traveling speed of the abnormal vehicle is less than the preset speed threshold in the driving path, and directly determine the position of the road section as the abnormal road section position, or determine the abnormal position. The position of the road section where the speed of the vehicle is lower than the preset speed threshold value in the travel path is directly determined as the abnormal road section position. In practical applications, when a vehicle travels on a narrow road section, the speed and acceleration of the vehicle are usually reduced. In this case, the abnormal road segment type of the vehicle with reduced speed and acceleration is directly determined as narrow type. Obviously, since both the speed and the acceleration can accurately reflect the state of the vehicle detouring or the direction of sudden change, the above-mentioned method for determining the abnormal road section information by analyzing the speed or acceleration can accurately identify the abnormal road section with narrow type, and The location of each abnormal road segment.

In one of the embodiments, the embodiment of the present disclosure also provides a specific implementation manner of S103, which includes: inputting the driving state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain navigation Information about abnormal sections on the route.

Among them, the abnormal road section analysis network is a pre-trained network, which is used to identify the road sections with abnormal conditions that may occur on the navigation path according to the driving state information of the vehicles, and at the same time, identify the location and type of the road sections where the abnormal conditions occur. . The abnormal road section analysis network may specifically adopt various types of neural networks or machine learning networks, which are not limited here.

Specifically, various types of sensors are installed on the vehicle to collect various driving state information when the vehicle travels on the navigation path in real time, and periodically report the driving state information to the server. Store it down. When the server receives the navigation warning analysis sent by the client, it immediately obtains the stored driving status information of a preset number of vehicles within a preset time period, and inputs the driving status information of each vehicle into the trained vehicle. The abnormal road section analysis network based on the network is analyzed to obtain the analysis result, and the analysis result can indicate whether there is an abnormal road section on the navigation path, as well as the abnormal road section location and abnormal road section type.

In the above method, the abnormal road section on the navigation path is identified by using the trained abnormal road section analysis network. Compared with the method of determining the abnormal road section by comparing the driving path and the navigation path of a large number of vehicles, the above method not only simplifies the server The process of identifying abnormal road sections, and because the abnormal road section analysis network is pre-trained based on a large amount of data, the recognition accuracy is very high, thereby improving the accuracy of post-warning.

Optionally, based on the above-mentioned application background, the above-mentioned "input the driving state information of each vehicle into a preset abnormal road section analysis network for analysis, and obtain abnormal road section information on the navigation path", as shown in FIG. 4 , which specifically includes: :

S401. Divide the navigation route into a plurality of unit road segments.

Wherein, the length of the unit road segment may be pre-determined by the server according to the actual identification accuracy. When the server needs to use the preset abnormal road segment analysis network to analyze the abnormal situation on the navigation path, the server can first divide the navigation path into multiple unit road segments, so as to obtain the driving status information on each unit road segment later, and analyze the abnormal conditions on each unit road segment according to each unit road segment. The driving status information on the road section is used to analyze the abnormal situation of each unit road section.

S402: Acquire the driving state information of each vehicle on each unit road section.

Specifically, after the server divides the navigation path into a plurality of unit road segments, it can filter out all vehicles or a preset number of vehicles within the preset time period from the driving status information of each vehicle on the stored navigation path. driving status information. Optionally, the server may also acquire the driving status information of all vehicles currently traveling on the navigation route in real time. For example, the server may filter out the driving status information (eg, driving speed) of 10 vehicles on the navigation route in the past week from the stored driving status information of the vehicles.

S403 , input the driving state information corresponding to each unit road section into the abnormal road section analysis network for analysis, and obtain an analysis result; the analysis result indicates whether the unit road section is an abnormal road section, and the type and location of the abnormal road section.

When the server obtains the driving status information of each vehicle on each unit road section based on the above steps, it can analyze the abnormal conditions on each unit road section, that is, directly input the driving status information into the abnormal road section analysis network for analysis, and obtain From the analysis result, the server can learn from the analysis result whether each unit road segment is an abnormal road segment, as well as the location of the abnormal road segment on the navigation path and the abnormal road segment type. For example, the navigation route includes 5 unit road segments. After inputting the driving status information on these 5 unit road segments into the abnormal road segment analysis network for analysis, it can be obtained that the third unit road segment among the 5 unit road segments is an abnormal road segment. , and the location of the unit road segment is 1000 meters away on the navigation path, and the type of the unit road segment is the construction type.

S404, according to the analysis result, obtain abnormal road section information on the navigation path.

After the server obtains the analysis results of each unit road section through the above steps, it can determine whether there is an abnormal road section on the navigation path, and the location and type of the abnormal road section according to the analysis results. For details, please refer to the above-mentioned examples.

In one embodiment, an embodiment of the present disclosure further provides a method for training the above-mentioned abnormal road segment analysis network. As shown in FIG. 5 , the training method includes:

S501: Obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road section corresponding to the sample state information is an abnormal road section, and the type and location of the abnormal road section.

The type of the sample state information is the same as that of the driving state information in the foregoing embodiment. For details, please refer to the foregoing content, which will not be repeated here. Specifically, when the server needs to train the abnormal road section analysis network, the server can obtain the data output by the sensors of a large number of existing vehicles, and obtain the sample state information. And the server can obtain whether the road section corresponding to the data is an abnormal road section and determine the position of the abnormal road section by identifying the data output by the sensor, and determine the type of the abnormal road section by identifying the type of data output by the sensor, that is, obtain the sample status label; , the server can also find the abnormal road section by offline step, that is, the user arrives at the on-site road section, and classifies the abnormal road section to obtain the sample status label.

S502: Input the sample state information into the initial abnormal road section analysis network to obtain an output result.

The initial abnormal road section analysis network is the abnormal road section analysis network to be trained, and the output result includes abnormal road section information. When the server obtains the sample state information, the sample state information can be input into the abnormal road section analysis network to be trained for analysis, and an output result is obtained.

S503: Calculate the loss value according to the output result and the sample state label.

The server performs the loss operation on the output result and the sample state label to obtain the loss value. The specific loss operation may use the existing commonly used cross-entropy loss operation, and may also use other loss operations, which is not limited here.

S504: Adjust the parameters of the initial abnormal road section analysis network according to the size of the loss value, until the loss value satisfies the preset training condition, and obtain the abnormal road section analysis network.

The preset training conditions may be determined by the server in advance according to actual training requirements, for example, when the loss value reaches a preset standard value, or reaches the maximum number of iterations. After the server obtains the loss value, it can adjust the parameters of the initial abnormal road segment analysis network according to the size of the loss value, and then input the sample state information into the abnormal road segment analysis network after adjusting the parameters, obtain the output result, calculate the loss value, and then Adjust the parameters of the abnormal road section analysis network according to the size of the loss value, and repeat this cycle until the loss value satisfies the preset training conditions. At this time, the parameters of the final abnormal road section analysis network are obtained, that is, the abnormal road section analysis used in the foregoing embodiment is obtained. network.

In one embodiment, an embodiment of the present disclosure further provides a structure of an abnormal road segment analysis network, as shown in FIG. 6 , the structure includes: at least two parallel branch networks; different branch networks are used to analyze different types of Driving status information.

Based on the structure of the abnormal road section analysis network described in the embodiment of FIG. 6 , the embodiment of the present disclosure also provides a specific implementation manner of S103 corresponding to the abnormal road section analysis network described above. The driving state information is input into the corresponding branch network for analysis, and the abnormal road section information on the navigation path is obtained.

Specifically, when the server obtains various driving state information reported by the vehicle on the navigation path, it can input each driving state information into the corresponding branch network for analysis according to the type of each driving state information. , obtain the analysis results output by each branch network, and then integrate the analysis results to obtain the abnormal road section information on the navigation path. In the above method, since each branch network corresponds to each type of driving state information, that is, each branch network only needs to identify the corresponding type of driving state information, and it is not necessary to first classify the driving state information, and then classify each type of driving state information. It can greatly simplify the analysis process of the abnormal road section analysis network, thereby greatly improving the recognition speed and recognition accuracy of the abnormal road section analysis network.

In one embodiment, an embodiment of the present disclosure further provides a method for training the above-mentioned abnormal road segment analysis network. As shown in FIG. 7 , the training method includes:

S601: Obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road section corresponding to the sample state information is an abnormal road section, and the type and location of the abnormal road section.

This step is the same as the method described in the foregoing step S501. For details, please refer to the foregoing description, which will not be repeated here.

S602: According to the type of the sample state information, the sample state information is respectively input into the corresponding initial branch network, and the output result of each branch network is obtained.

The initial branch network is the branch network to be trained, and the output result of each branch network includes abnormal road section information corresponding to the type of driving state information. For example, if the driving state information includes two types of data, speed and vibration frequency, the output result of the branch network corresponding to the speed is the abnormal road section and location of the construction type, and the output result of the branch network corresponding to the vibration frequency is the abnormal road section of the pothole type. and location.

Specifically, when the server collects a large amount of sample state information, it can first classify the sample state information to obtain various types of sample state information; the server can also directly collect different types of sample state information. Then, the server inputs the collected sample state information into the corresponding initial branch network according to the type of the sample state information, and obtains the output result of each branch network. In this process, the server can input different types of driving status information into the corresponding branch networks in sequence, or input different types of driving status information into the corresponding branch networks at the same time, so as to obtain the output results of each branch network , which can be used later when analyzing various types of abnormal paths.

S603: Calculate the loss value of each branch network according to the output result of each branch network and the corresponding sample state label.

The method for calculating the loss value of each branch network in this step is similar to the method described in the foregoing step S503 . For details, please refer to the foregoing description, which will not be repeated here.

S604: Adjust the parameters of each branch network according to the loss value of each branch network to obtain an abnormal road section analysis network.

The method for adjusting the network parameters of each branch in this step is similar to the method described in the foregoing step S503 . For details, please refer to the foregoing description, which will not be repeated here.

In one embodiment, the above-mentioned vehicle may be a two-wheeled vehicle, that is, a bicycle, an electric vehicle, or the like. Existing bicycles or electric vehicles, especially shared bicycles or shared electric vehicles, take shared bicycles as an example. Because they do not have navigation functions and early warning functions, users often ride on the navigation path after searching for the navigation path through the user terminal. The bicycle arrives at the destination. In this case, the user can easily damage the bicycle due to uneven road surface when riding the bicycle, or bring a poor riding experience to the user, and bring certain economic losses to the unit to which the shared bicycle belongs. At the same time, it will also reduce the safety of the user's riding. Based on such a technical problem, the navigation warning method for a vehicle provided by the embodiment of the present disclosure enables the shared bicycle to have an early warning function when riding on the navigation path, so that the user can advance the abnormal road section on the front path in advance and choose a detour by himself. Ride or push the vehicle through an abnormal road section before riding. The safety of the user's riding is improved, and the service life of the vehicle is improved.

It can be understood that, when the vehicle driven by the user is a two-wheeled vehicle, the navigation path sent by the user terminal to the server is the path on the non-motorized vehicle lane. Non-motorized vehicle lanes often have abnormal conditions such as road potholes, road damage, and road construction, and there are often many pedestrians on non-motorized vehicle lanes. Then, when the user rides the vehicle on the non-motorized lane, he will often encounter the above situation. If the warning of the above abnormal situation can be obtained in time, the user can avoid riding in the above abnormal situation when riding the vehicle, or be careful. Cycling improves the safety of users cycling and pedestrians on non-motorized lanes.

In some embodiments, the user terminal is a vehicle or a user terminal that initiates a usage process for the vehicle. That is to say, the client can be a vehicle, such as a bicycle, an electric vehicle, a car, etc.; when the client is a vehicle, the vehicle can send the navigation path selected by the user to the server, and when the user drives the vehicle on the navigation path , the server sends early warning information to the vehicle, and the vehicle outputs the warning information to remind the user; the user terminal can also be the user terminal that initiates the use process on the vehicle, that is, the user terminal can interact with the vehicle data. , to instruct the user terminal of the vehicle to perform the corresponding operation. When the user terminal is the user terminal that initiates the use process on the vehicle, the user can determine the navigation path on the user terminal, and send the navigation path to the server, and the corresponding server sends the warning prompt on the navigation path to the user terminal information to prompt the user; optionally, when the user performs the operation of determining the navigation path on the user terminal, the server may also send early warning information on the navigation path to the vehicle. It is explained here that the above-mentioned user terminal may be an electronic device independent of the vehicle, for example, the user terminal may be a mobile phone or an IPAD, etc., and the user terminal may also be an electronic device or device integrated on the vehicle.

The user terminal can be either a vehicle or a user terminal that initiates the use process of the vehicle, that is, the user can trigger the navigation warning analysis instruction through the vehicle, and can also trigger the navigation warning analysis instruction through the user terminal, and the user can either The early warning prompt information can be output through the vehicle, and the early warning prompt information can also be output through the user terminal. The user can flexibly choose the way of receiving the warning prompt information according to the specific driving situation of the vehicle, which improves the safety and convenience of the user driving the vehicle. For example, when the user terminal is a bicycle, the user can understand the warning information output by the bicycle while riding, without stopping the bicycle and use other devices to get the warning information.

The above embodiment is described by applying the vehicle navigation warning method to the server in FIG. 1 as an example, and the following embodiment is described by applying the vehicle navigation warning method to the client in FIG. 1 as an example.

In one embodiment, as shown in FIG. 8 , a navigation warning method for a vehicle is provided, and the method is applied to the user terminal in FIG. 1 as an example to illustrate, including the following steps:

S701: Send a navigation warning analysis instruction to a server; the navigation warning analysis instruction includes a navigation path.

The steps described in this embodiment correspond to the contents included in step S101 described in the foregoing embodiment of FIG. 2 . For a detailed description, please refer to the foregoing description, which will not be repeated here.

S702: Receive early warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the travel information of vehicles traveling on the navigation path within a preset time period; the abnormal road section information includes abnormal road sections Type and location of abnormal road segments.

The steps described in this embodiment correspond to the contents included in steps S102 to S104 described in the foregoing embodiment of FIG. 2 . For a detailed description, please refer to the foregoing description, which will not be repeated here.

S703, outputting warning prompt information.

When the user terminal receives the warning prompt information sent by the server, it can further output the warning prompt information, so that the user can know which abnormal sections are on the pre-driving navigation path through the warning prompt information, and the location and type of the abnormal road sections, so as to select Whether to follow this navigation route, or drive carefully while driving. It should be noted that the user terminal can output warning prompt information in various ways. For example, the user terminal can display the abnormal road section location and abnormal road section type on the navigation path by displaying images or icons; the user terminal can display it dynamically or statically. For the abnormal road section on the navigation path, this embodiment does not limit the manner of outputting the warning prompt information.

In the above vehicle navigation early warning method, the client sends a navigation early warning analysis instruction carrying the navigation path to the server, and correspondingly receives the early warning prompt information containing abnormal road section information sent by the server to remind the user of abnormal conditions on the navigation path. The navigation early warning method realizes the function of the user side navigating and giving early warning, that is, when the user starts the navigation function on the user side, the relevant early warning prompt information can be obtained, so that the user can predict the abnormal situation on the path ahead, which improves the user experience. The safety of driving vehicles. Moreover, since the warning prompt information includes the type and location of the abnormal road section, the warning prompt information not only plays a role of reminding the user to proceed with caution, but also specifically informs the user of the type and location of the abnormal road section, so that the user can understand the abnormal situation more intuitively. , and then flexibly choose whether to continue moving forward according to the abnormal situation, which greatly meets the needs of users.

In one embodiment, based on the above-mentioned embodiment of FIG. 8 , before the user terminal performs step S701, the vehicle navigation warning method, as shown in FIG. 9 , further includes:

S801. Detect a user's path selection operation on a path selection interface of the user terminal.

Wherein, the path selection interface may be an interface when an application program in the user terminal is opened, and the application program has a navigation function. The path selection operation may include different selection operations such as drop-down menu, input of text, input of password, etc., which are not limited here.

Specifically, when the user needs to perform a navigation operation, the user can open a corresponding navigation application program on the user terminal, and perform a path selection operation on the selection interface of the application program, for example, the user inputs the address of the navigation destination, or the user Directly search for the route between the departure location and the destination location, etc. When the user completes the path selection operation, the user terminal can immediately detect the user's path selection operation, so that the user terminal can then respond according to the path selection operation.

S802, in response to the path selection operation, determine the selected navigation path as the navigation path to be analyzed.

When the user terminal detects the user's path selection operation, it can further obtain the user's pre-travel navigation path according to the user's path selection operation, and use the navigation path as the navigation path to be analyzed, and carry it in the navigation warning analysis instruction. to instruct the server to analyze the abnormal situation on the navigation path and feed back the abnormal situation to the user.

Since the navigation warning analysis instruction determined by the above method is determined by the user's selection, in combination with the methods described in the foregoing embodiments, the vehicle navigation warning method provided by the embodiment of the present disclosure can warn of abnormal conditions on any navigation path, so that the The applicability of the user terminal using the navigation warning method is stronger.

In one embodiment, when the user terminal executes S103 "output warning prompt information" in the embodiment of FIG. 8 , the user terminal outputs early warning prompt information while the vehicle is traveling on the navigation path to be analyzed. That is to say, while driving the vehicle on the navigation path, the user can obtain the warning prompt information output by the user terminal, learn which sections of the navigation path are abnormal sections, and also know the type of abnormal sections (for example, potholes), so as to drive carefully, or drive on detours to improve driving safety.

Optionally, the travel information in the foregoing embodiments specifically includes the travel path and/or travel state information of the vehicle. The content of the driving information in this embodiment is the same as the content of the driving information described on the server side. For a detailed description, please refer to the foregoing description, which will not be repeated here.

In one embodiment, the vehicle involved above may be a two-wheeled vehicle, and the corresponding navigation path is a path on a non-motorized vehicle lane. The vehicle involved in this embodiment is the same as the vehicle described on the aforementioned server side, and the navigation path is also the same. For a specific introduction, please refer to the aforementioned description, which will not be repeated here.

In one embodiment, various types of sensors are provided on the above-mentioned vehicles, specifically including: at least one of a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor, and a vibration sensor The driving information of the vehicle obtained by the corresponding user terminal according to these sensors includes at least one of the driving path, speed, acceleration, angle, curvature, balance, vibration frequency and vibration amplitude. It should be noted that the above-mentioned various types of sensors can use existing sensing devices, as long as the collection of corresponding types of data can be achieved, and the models and specifications of each sensor are not limited here. One type of sensor may be installed on a vehicle, or multiple types of sensors may be installed, and different vehicles may be installed with the same type of sensor or different types of sensors.

The vehicle navigation warning method provided by the embodiments of the present disclosure can detect various types of abnormal road sections. Specifically, the types of detected abnormal road sections include: pothole road sections, construction road sections, accident road sections, and narrow road sections. at least one of. That is to say, the above-mentioned user terminal can provide early warning prompts for one type of abnormal path, and can also provide early warning prompts for multiple types of abnormal road sections at the same time. The warning prompt information is more specific, and provides more sufficient data for the user to decide whether to continue driving the vehicle on the navigation path.

In one embodiment, when the user terminal completes the above step S702, it can further display the navigation route. Based on this, the embodiment of the present disclosure provides three specific implementation manners for the user terminal to output early warning prompt information. The first one is: Display the warning prompt information in the form of pictures on the display screen of the user terminal; the second is to broadcast the early warning information through voice broadcast; the third is to display the early warning information on the display screen of the user terminal in the form of pictures, At the same time, the warning prompt information is broadcast by voice broadcast.

In the first method above, when the client receives the warning information sent by the server, it can display the warning information on the display screen in the form of a static or dynamic picture, and the display screen can display the abnormal sections on the navigation path. , the location of the abnormal road section and the type of the abnormal road section, where the abnormal road section can be represented by an icon; the location of the abnormal road section can be represented by a coordinate value, a longitude and latitude value, or a distance value from the current position of the vehicle (for example, the distance from the current There is an abnormal road section 100 meters away); the type of abnormal road section can be represented by icons, text, etc. There is no limitation on the display method of the warning prompt information on the display comment. The above-mentioned method of outputting warning display information can enable the user to intuitively see the abnormal road section, the location of the abnormal road section and the type of the abnormal road section on the display screen, so as to facilitate the user to familiarize himself with the abnormal situation on the navigation path, thereby facilitating the user to quickly make a decision. Responses.

In the second method above, when the user terminal receives the warning information sent by the server, it plays the warning information in the form of voice broadcast. For example, the user terminal can broadcast: There is a construction road section 100 meters ahead. It should be noted that, when the user terminal broadcasts the warning prompt information in the form of voice broadcast, a corresponding voice external device, such as a speaker, a voice player, etc., may be installed on the user terminal. Obviously, the second display method enables the user to drive the vehicle on the navigation path while listening to the warning prompt information broadcast by the user terminal. Without affecting the user's driving operation, the user can also obtain the information on the navigation path in advance. Abnormal conditions, improve the safety of users driving vehicles.

In one embodiment, the above-mentioned user terminal includes a vehicle or a user terminal that initiates a use process for the vehicle. The client involved in this embodiment is the same as the client on the foregoing server side. For a specific introduction, please refer to the foregoing description, which will not be repeated here.

In one embodiment, based on the above-mentioned embodiment of FIG. 8 , when the user terminal performs any of the steps, the vehicle navigation warning method, as shown in FIG. 10 , further includes:

S901, acquiring sensing data output by at least one type of sensor installed on the vehicle.

When the vehicle is driving, whether it is driving on the navigation path or on other paths, the user terminal will obtain the sensor data output by at least one type of sensor installed on the vehicle in real time, so as to detect the traffic in an all-round way. The driving status of the tool.

S902: Generate travel information according to the sensor data and send it to the server.

When the user terminal obtains the sensor data of the vehicle, it will immediately carry the sensor data in the driving information and report it to the server, so that the server can store the driving information of these vehicles so that it can be used when the abnormal road section needs to be analyzed later.

In the method described in the above embodiment, various types of sensing data are reported to the server by the user terminal anytime and anywhere, so as to provide a substantial data reserve for the server, improve the accuracy of the early warning information determined by the server in the later stage, and further improve the user experience. Accuracy of end warning.

In one embodiment, an embodiment of the present disclosure further provides a navigation warning method for a vehicle. The method is applied to the client and server in FIG. 1 . As shown in FIG. 11 , the method includes:

S1001, a client sends a navigation warning analysis instruction to a server; the navigation warning analysis instruction includes a navigation path.

S1002, the server receives a navigation warning analysis instruction sent by the client.

S1003, the server obtains travel information of vehicles traveling on the navigation path within a preset time period.

S1004, the server determines the abnormal road section information on the navigation path by analyzing the driving information of each vehicle; the abnormal road section information includes the abnormal road section type and the abnormal road section position.

S1005, the server carries the abnormal road section information in the warning prompt information and sends it to the client.

S1006, the client receives the warning prompt information sent by the server.

S1007, the user terminal outputs warning prompt information.

The methods described in S1001 to S1007 are the same as the methods described in the foregoing server-side embodiment and client-side embodiment. For detailed introduction, please refer to the foregoing description, which is not repeated here.

The navigation warning method of the vehicle described in the above embodiment realizes the function that the user obtains the warning prompt information on the navigation path while navigating through the data interaction between the client and the server. Therefore, the navigation warning method for a vehicle provided by the embodiment of the present disclosure can improve the safety of the user driving the vehicle while providing the user with the convenience of driving the vehicle.

It should be understood that although the steps in the flowcharts of FIGS. 2-11 are shown in sequence according to the arrows, these steps are not necessarily executed in the sequence shown by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order, and these steps may be performed in other orders. Moreover, at least a part of the steps in FIGS. 2-11 may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution of these steps or stages The order is also not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the steps or phases within the other steps.

In one embodiment, as shown in FIG. 12 , a navigation warning device for a vehicle is provided, including: a receiving module 11, an obtaining module 12, a determining module 13 and a sending module 14, wherein:

The receiving module 11 is used for receiving the navigation warning analysis instruction sent by the user terminal; the navigation warning analysis instruction includes the navigation path to be analyzed;

an obtaining module 12, configured to obtain the travel information of the vehicle traveling on the navigation path within a preset time period;

A determination module 13, configured to determine the abnormal road section information on the navigation path by analyzing the driving information of each vehicle; the abnormal road section information includes the abnormal road section type and the abnormal road section position;

The sending module 14 is configured to carry the abnormal road section information in the warning prompt information and send it to the user terminal.

In one embodiment, the travel information includes travel path and/or travel state information of the vehicle.

In one embodiment, the travel path of the vehicle is a travel path determined according to GPS positioning information reported by the vehicle and/or positioning data output by a positioning sensor installed on each of the vehicles;

The driving state information is information obtained according to data collected by sensors installed on each of the vehicles.

In one embodiment, the above determination module 13, as shown in FIG. 13, includes:

a first determining unit 131, configured to determine an abnormal vehicle by analyzing the driving information of each of the vehicles; the abnormal vehicle has an abnormal driving path and/or an abnormal driving state;

The second determining unit 132 is configured to determine the abnormal road section information on the navigation path according to the number of the abnormal vehicles and a preset number threshold.

In one embodiment, the first determining unit 131 is further configured to compare the travel path of each vehicle with the navigation path, and determine a vehicle whose travel path is inconsistent with the navigation path as the abnormality Transportation;

The second determining unit 132 is further configured to, when the number of the abnormal vehicles is greater than a preset number threshold, determine that the location of the abnormal road section is the location of the road segment where the driving path of the abnormal vehicle is inconsistent with the navigation path, and determine The abnormal road section type is a construction type or an accident type.

In one embodiment, the first determining unit 131 is further configured to compare the value of the driving state information of each vehicle with a preset state threshold to determine the abnormal vehicle;

The second determining unit 132 is further configured to determine abnormal road section information on the navigation path according to the driving state information of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold.

In one embodiment, if the driving state information includes a vibration frequency and/or a degree of balance, the first determining unit 131 is specifically configured to, when the vibration frequency is greater than a preset vibration threshold, and/or, the degree of balance When it is less than the preset balance degree threshold, it is determined that the vehicle is an abnormal vehicle.

In one embodiment, the second determining unit 132 is specifically configured to, when the number of the abnormal vehicles is greater than a preset number threshold, determine the location of the abnormal road segment as the location of the target road segment in the travel path of the abnormal vehicle , and determine that the abnormal road segment type is a pothole type; the target road segment location is the location of the road segment where the vibration frequency is greater than the preset vibration threshold in the driving path of the abnormal vehicle, and/or the balance degree is less than the preset balance The segment location for the degree threshold.

In one embodiment, if the state information includes a curvature and/or an angle, the first determining unit 131 is specifically configured to, when the curvature is greater than a preset curvature threshold, and/or, the angle is greater than a preset angle threshold When , it is determined that the vehicle is an abnormal vehicle.

In one embodiment, the second determining unit 132 is specifically configured to, when the number of the abnormal vehicles is greater than a preset number threshold, determine the location of the abnormal road segment as the location of the target road segment in the travel path of the abnormal vehicle , and determine that the abnormal road segment type is a construction type; the target road segment location is the location of the road segment with a curvature greater than a preset curvature threshold in the driving path of the abnormal vehicle, and/or a road segment with an angle greater than a preset angle threshold Location.

In one embodiment, if the state information includes speed and/or acceleration, the first determining unit 131 is specifically configured to be less than a preset speed threshold when the speed is less than a preset speed threshold, and/or, the acceleration is less than a preset acceleration threshold When , it is determined that the vehicle is an abnormal vehicle.

In one embodiment, the second determining unit 132 is specifically configured to, when the number of the abnormal vehicles is greater than a preset number threshold, determine the location of the abnormal road segment as the location of the target road segment in the travel path of the abnormal vehicle , and determine that the abnormal road segment type is a narrow type; the target road segment location is the location of the road segment in the travel path of the abnormal vehicle where the speed is less than the preset speed threshold, and/or the acceleration is less than the preset acceleration threshold. Location.

In one embodiment, as shown in FIG. 14 , the determining module 13 includes:

The network analysis unit 133 is configured to input the driving state information of each vehicle into a preset abnormal road section analysis network for analysis to obtain abnormal road section information on the navigation path.

In one embodiment, as shown in FIG. 15 , the network analysis unit 133 includes:

a splitting subunit 1331, for dividing the navigation route into a plurality of unit road segments;

an acquisition subunit 1332, configured to acquire the driving state information of each of the vehicles on each of the unit road sections;

The analysis subunit 1333 is used to input the driving state information corresponding to each of the unit road sections into the abnormal road section analysis network for analysis to obtain an analysis result; the analysis result indicates whether the unit road section is an abnormal road section, and the Describe the type and location of the abnormal road section;

The output subunit 1334 is configured to obtain abnormal road section information on the navigation path according to the analysis result.

In one embodiment, the navigation warning device further includes: a first training module 10, and the training module, as shown in FIG. 16 , includes:

The first sample obtaining unit 101 is used to obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road segment corresponding to the sample state information is an abnormal road segment, and the type and location of the abnormal road segment;

a first network analysis unit 102, configured to input the sample state information into an initial abnormal road section analysis network to obtain an output result;

a first calculation unit 103, configured to calculate a loss value according to the output result and the sample state label;

The first training unit 104 is configured to adjust the parameters of the initial abnormal road segment analysis network according to the size of the loss value, until the loss value satisfies a preset training condition, and obtain the abnormal road segment analysis network.

In one embodiment, the abnormal road segment analysis network includes: at least two parallel branch networks; different branch networks are used to analyze different types of driving state information;

The network analysis unit 133 is configured to input the driving status information into the corresponding branch network for analysis according to the type of the driving status information, and obtain abnormal road section information on the navigation path.

In one embodiment, the navigation warning device further includes a second training module 20, and the second training module 20, as shown in FIG. 17, includes:

The second sample obtaining unit 201 is used to obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road segment corresponding to the sample state information is an abnormal road segment, and the type and location of the abnormal road segment ;

The second network analysis unit 202 is configured to respectively input the sample state information into the corresponding initial branch network according to the type of the sample state information, and obtain the output result of each branch network;

The second calculation unit 203 is configured to calculate the loss value of each branch network according to the output result of each branch network and the corresponding sample state label;

The second training unit 204 is configured to adjust the parameters of each branch network according to the loss value of each branch network, so as to obtain the abnormal road segment analysis network.

In one embodiment, the vehicle is a two-wheeled vehicle.

In one embodiment, the navigation path is a path on a non-motorized lane.

In one embodiment, the user terminal includes the vehicle or a user terminal that initiates a usage process for the vehicle.

In one embodiment, as shown in FIG. 18, a navigation warning device for a vehicle is provided, including: a sending module 21, a receiving module 22 and an output module 23, wherein:

The sending module 21 is used for sending a navigation warning analysis instruction to the server; the navigation warning analysis instruction includes the navigation path to be analyzed;

The receiving module 22 is configured to receive the early warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is the server according to the vehicle traveling on the navigation path within a preset time period The abnormal road section information includes the abnormal road section type and the abnormal road section position;

The output module 23 is used for outputting the warning prompt information.

In one embodiment, the travel information includes travel path and/or travel state information of the vehicle.

In one embodiment, before the sending module 21 in the navigation warning device, as shown in FIG. 19 , further includes:

The detection module 24 is used to detect the user's path selection operation on the path selection interface of the user terminal;

The determination module 25 is configured to determine the selected navigation path as the navigation path to be analyzed in response to the path selection operation.

In one embodiment, the output module 23 is further configured to output the warning prompt information when the vehicle is traveling on the navigation path to be analyzed.

In one embodiment, the vehicle is a two-wheeled vehicle.

In one embodiment, the navigation path is a path on a non-motorized lane.

In one embodiment, the navigation warning device, as shown in Figure 20, further includes:

an acquisition module 26, configured to acquire sensing data output by at least one type of sensor installed on the vehicle;

The generating module 27 is configured to generate the driving information according to the sensing data and send it to the server.

In one embodiment, the vehicle is provided with at least one of a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor and a vibration sensor; the driving information includes a driving path, speed, At least one of acceleration, angle, curvature, balance, vibration frequency, and vibration amplitude.

In one embodiment, the abnormal road section type includes at least one of a pothole road section, a construction road section, an accident road section, and a narrow road section.

In one embodiment, the output module 23, as shown in FIG. 21, includes:

The display unit 231 is used to display the warning prompt information in the form of a picture on the display screen of the user terminal;

And/or, the broadcasting unit 232 is configured to announce the warning prompt information by way of voice broadcasting.

In one embodiment, the user terminal includes the vehicle or a user terminal that initiates a usage process for the vehicle.

For the specific limitation of the navigation warning device of the vehicle, reference may be made to the limitation of the navigation warning method of the vehicle above, which will not be repeated here. Each module in the above-mentioned vehicle navigation warning device can be implemented in whole or in part by software, hardware and combinations thereof. The above modules can be embedded in or independent of the processor in the user terminal in the form of hardware, and can also be stored in the memory in the user terminal in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

FIG. 22 is a block diagram of a user terminal 1300 according to an exemplary embodiment. When the user terminal is a user terminal, the user terminal may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a tablet device, a personal digital assistant, and the like.

22, the client 1300 may include one or more of the following components: a processing component 1302, a memory 1304, a power supply component 1306, a multimedia component 1308, an audio component 1310, an input/output (I/O) interface 1312, a sensor component 1314 , and the communication component 1316. The memory stores computer programs or instructions running on the processor.

The processing component 1302 generally controls the overall operations of the client 1300, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 1302 can include one or more processors 1320 to execute instructions to perform all or some of the steps of the above-described methods. Additionally, processing component 1302 may include one or more modules that facilitate interaction between processing component 1302 and other components. For example, processing component 1302 may include a multimedia module to facilitate interaction between multimedia component 1308 and processing component 1302.

The memory 1304 is configured to store various types of data to support operations at the client 1300 . Examples of such data include instructions for any application or method operating on the client 1300, contact data, phonebook data, messages, pictures, videos, and the like. Memory 1304 may be implemented by any type of volatile or nonvolatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read only memory (EEPROM), erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.

Power component 1306 provides power to various components of client 1300 . Power components 1306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to client 1300 .

The multimedia component 1308 includes a touch screen display that provides an output interface between the client 1300 and the user. In some embodiments, the touch display may include a liquid crystal display (LCD) and a touch panel (TP). The touch panel includes one or more touch sensors to sense touch, swipe, and gestures on the touch panel. The touch sensor may not only sense the boundaries of a touch or swipe action, but also detect the duration and pressure associated with the touch or swipe action. In some embodiments, the multimedia component 1308 includes a front-facing camera and/or a rear-facing camera. When the client 1300 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera may receive external multimedia data. Each of the front and rear cameras can be a fixed optical lens system or have focal length and optical zoom capability.

Audio component 1310 is configured to output and/or input audio signals. For example, audio component 1310 includes a microphone (MIC) that is configured to receive external audio signals when client 1300 is in operating modes, such as call mode, recording mode, and voice recognition mode. The received audio signal may be further stored in memory 1304 or transmitted via communication component 1316 . In some embodiments, audio component 1310 also includes a speaker for outputting audio signals.

The I/O interface 1312 provides an interface between the processing component 1302 and a peripheral interface module, which may be a keyboard, a click wheel, a button, or the like. These buttons may include, but are not limited to: home button, volume buttons, start button, and lock button.

Sensor assembly 1314 includes one or more sensors for providing various aspects of status assessment for client 1300 . For example, the sensor component 1314 can detect the open/closed state of the client 1300, the relative positioning of the components, for example, the components are the display and the keypad of the client 1300, and the sensor component 1314 can also detect the client 1300 or one of the client 1300 The location of components changes, the presence or absence of user contact with the client 1300, the orientation or acceleration/deceleration of the client 1300, and the temperature of the client 1300 changes. Sensor assembly 1314 may include a proximity sensor configured to detect the presence of nearby objects in the absence of any physical contact. Sensor assembly 1314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1314 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

Communication component 1316 is configured to facilitate wired or wireless communications between client 1300 and other devices. The client 1300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In one exemplary embodiment, the communication component 1316 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

In an exemplary embodiment, the user terminal described above may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable A programmed gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components are implemented for implementing the above-mentioned vehicle navigation warning method.

In an exemplary embodiment, the above-mentioned user terminal may be a user terminal or a vehicle. When the user terminal is a user terminal, the user terminal can be equipped with mechanical controls, virtual controls, voice devices and other controls for triggering navigation instructions with one key. When the user terminal is a vehicle, the vehicle can also be deployed with controls for a Mechanical buttons, virtual controls, voice devices and other controls for key navigation are used to trigger navigation commands with one key.

FIG. 23 is a block diagram of a server 1400 according to an exemplary embodiment. 23, server 1400 includes processing component 1420, which further includes one or more processors, and a memory resource represented by memory 1422 for storing instructions or computer programs, such as application programs, executable by processing component 1420. An application program stored in memory 1422 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1420 is configured to execute the instructions to perform the above-described method of navigation warning of a vehicle.

The server 1400 may also include a power component 1424 configured to perform power management of the device 1400, a wired or wireless network interface 1426 configured to connect the device 1400 to a network, and an input output (I/O) interface 1428. Server 1400 may operate based on an operating system stored in memory 1422, such as Window14 14erverTM, Mac O14XTM, UnixTM, LinuxTM, FreeB14DTM or the like.

In an exemplary embodiment, a storage medium including instructions, such as a memory 1422 including instructions, is also provided, and the instructions are executable by the processor of the server 1400 to accomplish the above method. The storage medium may be a non-transitory computer-readable storage medium such as ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, and the like.

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage In the medium, when the computer program is executed, it may include the processes of the above-mentioned method embodiments. Wherein, any reference to memory, storage, database, or other media used in the various embodiments provided by the embodiments of the present disclosure may include at least one of non-volatile and volatile memory. The non-volatile memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, and the like. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, the RAM may be in various forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM).

The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described. However, as long as there is no contradiction in the combination of these technical features It is considered to be the range described in this specification.

The above-mentioned embodiments only represent several implementations of the embodiments of the present disclosure, and the descriptions thereof are relatively specific and detailed, but should not be construed as a limitation on the scope of the invention patent. It should be noted that for those skilled in the art, without departing from the concept of the embodiments of the present disclosure, several modifications and improvements can be made, which all belong to the protection scope of the embodiments of the present disclosure. Therefore, the protection scope of the patent of the embodiments of the present disclosure should be subject to the appended claims.

Claims (67)

1. A navigation warning method for a vehicle, wherein the method comprises: receiving a navigation early warning analysis instruction sent by the user terminal; the navigation early warning analysis instruction includes the navigation path to be analyzed; acquiring the travel information of the vehicle traveling on the navigation path within a preset time period; By analyzing the driving information of each vehicle, the abnormal road section information on the navigation path is determined; the abnormal road section information includes the abnormal road section type and the abnormal road section position; The abnormal road section information is carried in the warning prompt information and sent to the user terminal. 2 . The method according to claim 1 , wherein the travel information includes travel path and/or travel state information of the vehicle. 3 . 3. The method according to claim 2, wherein the travel path of the vehicle is the GPS positioning information reported by the vehicle, and/or the output of a positioning sensor installed on each of the vehicles The driving path determined by the positioning data; The driving state information is information obtained according to data collected by sensors installed on each of the vehicles. 4 . The method according to claim 2 , wherein, determining the abnormal road section information on the navigation path by analyzing the driving information of each vehicle, comprising: 5 . By analyzing the driving information of each of the vehicles, an abnormal vehicle is determined; the abnormal vehicle has an abnormal driving path and/or an abnormal driving state; According to the number of the abnormal vehicles and a preset number threshold, the abnormal road section information on the navigation path is determined. 5 . The method according to claim 4 , wherein the driving information includes a driving path of each of the vehicles, and the determining of an abnormal vehicle by analyzing the driving information of each of the vehicles comprises: 5 . comparing the travel path of each vehicle with the navigation path, and determining a vehicle whose travel path is inconsistent with the navigation path as the abnormal vehicle; The determining of the abnormal road section information on the navigation path according to the number of the abnormal vehicles and the preset number threshold includes: If the number of the abnormal vehicles is greater than the preset number threshold, the abnormal road section position is determined to be a road section position where the driving path of the abnormal vehicle is inconsistent with the navigation path, and the abnormal road section type is determined to be a construction type or accident type. 6 . The method according to claim 4 , wherein the driving information includes the driving state information, and the determining an abnormal vehicle by analyzing the driving information of each of the vehicles comprises: 6 . comparing the value of the driving state information of each of the vehicles with a preset state threshold to determine the abnormal vehicle; The determining of the abnormal road section information on the navigation path according to the number of the abnormal vehicles and the preset number threshold includes: If the number of the abnormal vehicles is greater than a preset number threshold, the abnormal road section information on the navigation path is determined according to the driving state information of the abnormal vehicles. 7. The method according to claim 6, wherein, if the driving state information includes vibration frequency and/or balance, the value of the state information of each vehicle and a preset state threshold are used as Compare and determine the abnormal vehicle, including: If the vibration frequency is greater than a preset vibration threshold, and/or the balance degree is less than a preset balance degree threshold, it is determined that the vehicle is an abnormal vehicle. 8 . The method according to claim 7 , wherein if the number of the abnormal vehicles is greater than a preset number threshold, determining the number of vehicles on the navigation path according to the driving state information of the abnormal vehicles. 9 . Abnormal road section information, including: If the number of the abnormal vehicles is greater than the preset number threshold, determine that the location of the abnormal road section is the position of the target road segment in the driving path of the abnormal vehicle, and determine that the type of the abnormal road segment is a pothole type; the The target road segment location is a road segment location in the driving path of the abnormal vehicle where the vibration frequency is greater than a preset vibration threshold, and/or a road segment location where the balance degree is less than the preset balance degree threshold. 9 . The method according to claim 6 , wherein if the state information includes curvature and/or angle, the value of the driving state information of each vehicle is compared with a preset state threshold to determine the The abnormal vehicle includes: If the curvature is greater than a preset curvature threshold, and/or the angle is greater than a preset angle threshold, it is determined that the vehicle is an abnormal vehicle. 10 . The method according to claim 9 , wherein if the number of the abnormal vehicles is greater than a preset number threshold, the abnormality on the navigation path is determined according to the state information of the abnormal vehicles. 11 . Road segment information, including: If the number of the abnormal vehicles is greater than the preset number threshold, the abnormal road section position is determined to be the target road section position in the driving path of the abnormal vehicle, and the abnormal road section type is determined to be a construction type; the target road section is determined to be a construction type; The road segment location is the location of the road segment whose curvature is greater than the preset curvature threshold in the traveling path of the abnormal vehicle, and/or the location of the segment whose angle is greater than the preset angle threshold. 11. The method according to claim 6, wherein, if the state information includes speed and/or acceleration, the value of the state information of each vehicle is compared with a preset state threshold, and the determination is made to determine The abnormal vehicle includes: If the speed is less than a preset speed threshold, and/or the acceleration is less than a preset acceleration threshold, it is determined that the vehicle is an abnormal vehicle. 12 . The method according to claim 11 , wherein if the number of the abnormal vehicles is greater than a preset number threshold, the abnormality on the navigation path is determined according to the state information of the abnormal vehicles. 13 . Road segment information, including: If the number of the abnormal vehicles is greater than a preset number threshold, the abnormal road section position is determined as the target road section position in the driving path of the abnormal vehicle, and the abnormal road section type is determined to be a narrow type; the target road section is determined to be a narrow type; The road segment location is the location of the road segment where the speed of the abnormal vehicle is less than the preset speed threshold, and/or the location of the segment where the acceleration is less than the preset acceleration threshold. 13 . The method according to claim 2 , wherein the driving information includes the state information, and the abnormal road section information on the navigation path is determined by analyzing the driving information of each of the vehicles, comprising: 13 . : The driving state information of each vehicle is input into a preset abnormal road section analysis network for analysis to obtain abnormal road section information on the navigation path. 14 . The method according to claim 13 , wherein the driving state information of each vehicle is input into a preset abnormal road section analysis network for analysis to obtain abnormal road section information on the navigation path. 15 . ,include: dividing the navigation route into a plurality of unit road segments; acquiring the driving status information of each of the vehicles on each of the unit road sections; Input the driving state information corresponding to each of the unit road sections into the abnormal road section analysis network for analysis to obtain an analysis result; the analysis result indicates whether the unit road section is an abnormal road section, and the type and location of the abnormal road section ; According to the analysis result, abnormal road section information on the navigation path is obtained. 15. The method according to claim 14, wherein the method for training the abnormal road segment analysis network comprises: Obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road segment corresponding to the sample state information is an abnormal road segment, and the type and location of the abnormal road segment; Inputting the sample state information into the initial abnormal road section analysis network to obtain an output result; Calculate a loss value according to the output result and the sample state label; The parameters of the initial abnormal road segment analysis network are adjusted according to the size of the loss value until the loss value satisfies a preset training condition, and the abnormal road segment analysis network is obtained. 16. The method according to claim 13, wherein the abnormal road section analysis network comprises: at least two parallel branch networks; different branch networks are used to analyze different types of driving state information; The driving state information of each vehicle is input into a preset abnormal road section analysis network for analysis, and the abnormal road section information on the navigation path is obtained, including: According to the type of the driving state information, the driving state information is input into the corresponding branch network for analysis, and the abnormal road section information on the navigation path is obtained. 17. The method according to claim 16, wherein the method for training the abnormal road segment analysis network comprises: Obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road segment corresponding to the sample state information is an abnormal road segment, and the type and location of the abnormal road segment; According to the type of the sample state information, the sample state information is respectively input into the corresponding initial branch network, and the output result of each branch network is obtained; Calculate the loss value of each branch network according to the output result of each branch network and the corresponding sample state label; The parameters of each branch network are adjusted according to the loss value of each branch network to obtain the abnormal road section analysis network. 18. The method of claim 1, wherein the vehicle is a two-wheeled vehicle. 19. The method of claim 18, wherein the navigation path is a path on a non-motorized vehicle lane. 20. The method according to claim 1, wherein the user terminal comprises the vehicle or a user terminal that initiates a use process for the vehicle. 21. A navigation warning method for a vehicle, characterized in that the method comprises: Sending a navigation early warning analysis instruction to the server; the navigation early warning analysis instruction includes the navigation path to be analyzed; Receive early warning prompt information sent by the server; the early warning prompt information carries abnormal road section information, and the abnormal road section information is determined by the server according to the travel information of the vehicle traveling on the navigation path within a preset time period; The abnormal road segment information includes abnormal road segment type and abnormal road segment location; Output the warning prompt information. 22. The method according to claim 21, wherein the travel information comprises travel path and/or travel state information of the vehicle. 23. The method according to claim 21, characterized in that before sending the navigation warning analysis instruction to the server, the method further comprises: Detecting the user's path selection operation on the path selection interface of the client; In response to the route selection operation, the selected navigation route is determined as the navigation route to be analyzed. 24. The method according to claim 21, wherein the outputting the warning prompt information comprises: When the vehicle is traveling on the navigation path to be analyzed, the warning prompt information is output. 25. The method of claim 21, wherein the vehicle is a two-wheeled vehicle. 26. The method of claim 25, wherein the navigation path is a path on a non-motorized vehicle lane. 27. The method of claim 21, wherein the method further comprises: acquiring sensory data output by at least one type of sensor installed on the vehicle; The driving information is generated according to the sensing data and sent to the server. 28. The method according to claim 21, wherein at least one of a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor and a vibration sensor is provided on the vehicle; the The travel information includes at least one of travel path, speed, acceleration, angle, curvature, balance, vibration frequency and vibration amplitude. 29. The method according to any one of claims 21-28, wherein the abnormal road section types include at least one of pothole road sections, construction road sections, accident road sections, and narrow road sections. 30. The method according to claim 21, wherein the outputting the warning prompt information comprises: Display the warning prompt information in the form of a picture on the display screen of the user terminal; And/or, the early warning prompt information is broadcast by means of voice broadcast. 31. The method according to claim 21, wherein the user terminal comprises the vehicle or a user terminal that initiates a use process for the vehicle. 32. A navigation warning device for a vehicle, characterized in that the navigation warning device comprises: a receiving module, configured to receive a navigation warning analysis instruction sent by a user terminal; the navigation warning analysis instruction includes a navigation path to be analyzed; an acquisition module, configured to acquire the travel information of the vehicle traveling on the navigation path within a preset time period; a determining module, configured to determine the abnormal road section information on the navigation path by analyzing the driving information of each vehicle; the abnormal road section information includes the abnormal road section type and the abnormal road section position; A sending module, configured to carry the abnormal road section information in the early warning prompt information and send it to the user terminal. 33. The navigation warning device according to claim 32, wherein the driving information comprises a driving path and/or driving state information of the vehicle. 34. The navigation early warning device according to claim 33, wherein the travel path of the vehicle is GPS positioning information reported by the vehicle, and/or the positioning installed on each of the vehicles The driving path determined by the positioning data output by the sensor; The driving state information is information obtained according to data collected by sensors installed on each of the vehicles. 35. The navigation warning device according to claim 33, wherein the determining module comprises: a first determining unit, configured to determine an abnormal vehicle by analyzing the driving information of each of the vehicles; the abnormal vehicle has an abnormal driving path and/or an abnormal driving state; The second determining unit is configured to determine the abnormal road section information on the navigation path according to the number of the abnormal vehicles and a preset number threshold. 36. The navigation warning device according to claim 35, wherein, The first determining unit is further configured to compare the travel path of each vehicle with the navigation path, and determine a vehicle whose travel path is inconsistent with the navigation path as the abnormal vehicle; The second determining unit is further configured to, when the number of the abnormal vehicles is greater than a preset number threshold, determine that the location of the abnormal road section is the location of the road segment where the driving path of the abnormal vehicle is inconsistent with the navigation path, and determine the location of the abnormal road section. The abnormal road section type described above is a construction type or an accident type. 37. The navigation warning device according to claim 35, wherein, The first determining unit is further configured to compare the value of the driving state information of each vehicle with a preset state threshold to determine the abnormal vehicle; The second determining unit is further configured to determine abnormal road section information on the navigation path according to the driving state information of the abnormal vehicle when the number of the abnormal vehicles is greater than a preset number threshold. 38. The navigation warning device according to claim 37, wherein if the driving state information includes a vibration frequency and/or a degree of balance, the first determining unit is specifically configured to be greater than a preset vibration frequency when the vibration frequency is greater than a preset vibration frequency. threshold, and/or, when the balance degree is less than a preset balance degree threshold, it is determined that the vehicle is an abnormal vehicle. 39 . The navigation warning device according to claim 38 , wherein the second determining unit is specifically configured to determine the location of the abnormal road section as the The position of the target road section in the driving path of the abnormal vehicle, and the type of the abnormal road section is determined to be a pothole type; the target road section position is the position of the road section whose vibration frequency is greater than the preset vibration threshold in the driving path of the abnormal vehicle, And/or, the position of the road section where the balance degree is less than the preset balance degree threshold. 40. The navigation early warning device according to claim 37, wherein if the state information includes curvature and/or angle, the first determining unit is specifically configured to, when the curvature is greater than a preset curvature threshold, and/or Or, when the angle is greater than a preset angle threshold, it is determined that the vehicle is an abnormal vehicle. 41 . The navigation warning device according to claim 40 , wherein the second determining unit is specifically configured to determine the location of the abnormal road section as the The position of the target road section in the driving path of the abnormal vehicle, and it is determined that the type of the abnormal road section is a construction type; the target road section position is the position of the road section in the driving path of the abnormal vehicle whose curvature is greater than the preset curvature threshold, and/ Or, the position of the road segment whose angle is greater than the preset angle threshold. 42. The navigation early warning device according to claim 37, wherein if the state information includes speed and/or acceleration, the first determination unit is specifically configured to be less than a preset speed threshold when the speed is less than a preset speed threshold, and/or Or, when the acceleration is less than a preset acceleration threshold, it is determined that the vehicle is an abnormal vehicle. 43 . The navigation warning device according to claim 42 , wherein the second determining unit is specifically configured to determine that the location of the abnormal road section is the location of the abnormal road section when the number of the abnormal vehicles is greater than a preset number threshold The position of the target road section in the driving path of the abnormal vehicle, and the type of the abnormal road section is determined to be a narrow type; the target road section position is the position of the road section in the driving path of the abnormal vehicle whose speed is less than the preset speed threshold, and/ Or, the position of the road segment where the acceleration is less than the preset acceleration threshold. 44. The navigation warning device according to claim 33, wherein the determining module comprises: A network analysis unit, configured to input the driving state information of each vehicle into a preset abnormal road section analysis network for analysis, and obtain abnormal road section information on the navigation path. 45. The navigation warning device according to claim 44, wherein the network analysis unit comprises: splitting subunits, for dividing the navigation route into a plurality of unit road segments; an acquisition sub-unit for acquiring the driving state information of each of the vehicles on each of the unit road sections; An analysis subunit, configured to input the driving state information corresponding to each of the unit road sections into the abnormal road section analysis network for analysis to obtain an analysis result; the analysis result indicates whether the unit road section is an abnormal road section, and the The type and location of the abnormal road segment; An output subunit, configured to obtain abnormal road section information on the navigation path according to the analysis result. 46. The navigation early warning device according to claim 45, wherein the navigation early warning device further comprises: a first training module, the training module comprising: a first sample obtaining unit, used to obtain sample state information and a corresponding sample state label; the sample state label is used to identify whether the road segment corresponding to the sample state information is an abnormal road segment, and the type and location of the abnormal road segment; a first network analysis unit, configured to input the sample state information into an initial abnormal road section analysis network to obtain an output result; a first calculation unit, configured to calculate a loss value according to the output result and the sample state label; A first training unit, configured to adjust the parameters of the initial abnormal road segment analysis network according to the size of the loss value, until the loss value satisfies a preset training condition, and obtain the abnormal road segment analysis network. 47. The navigation warning device according to claim 44, wherein the abnormal road section analysis network comprises: at least two parallel branch networks; different branch networks are used to analyze different types of driving state information; The network analysis unit is configured to input the driving status information into the corresponding branch network for analysis according to the type of the driving status information, and obtain abnormal road section information on the navigation path. 48. The navigation early warning device according to claim 47, wherein the navigation early warning device further comprises a second training module, and the second training module comprises: a second sample acquisition unit, configured to acquire sample state information and a corresponding sample state label; the sample state label is used to identify whether the road segment corresponding to the sample state information is an abnormal road segment, and the type and location of the abnormal road segment; a second network analysis unit, configured to respectively input the sample state information into the corresponding initial branch network according to the type of the sample state information, and obtain the output results of each branch network; The second calculation unit is used to calculate the loss value of each branch network according to the output result of each branch network and the corresponding sample state label; The second training unit is configured to adjust the parameters of each branch network according to the loss value of each branch network, so as to obtain the abnormal road section analysis network. 49. The navigation warning device according to claim 32, wherein the vehicle is a two-wheeled vehicle. 50. The navigation warning device according to claim 48, wherein the navigation path is a path on a non-motorized vehicle lane. 51. The navigation early warning device according to claim 32, wherein the user terminal comprises the vehicle or a user terminal that initiates a use process for the vehicle. 52. A navigation early warning device for a vehicle, characterized in that the navigation early warning device comprises: a sending module, configured to send a navigation warning analysis instruction to the server; the navigation warning analysis instruction includes the navigation path to be analyzed; The receiving module is configured to receive the warning prompt information sent by the server; the warning prompt information carries abnormal road section information, and the abnormal road section information is the information of the server according to the vehicle traveling on the navigation path within a preset time period The driving information is determined; the abnormal road segment information includes the abnormal road segment type and the abnormal road segment location; The output module is used for outputting the warning prompt information. 53. The navigation warning device according to claim 52, wherein the driving information comprises a driving path and/or driving state information of the vehicle. 54. The navigation early warning device according to claim 52, wherein before the sending module in the navigation early warning device, further comprising: The detection module is used to detect the user's path selection operation on the path selection interface of the user terminal; The determining module is used for determining the selected navigation path as the navigation path to be analyzed in response to the path selection operation. 55. The navigation warning device according to claim 52, wherein the output module is further configured to output the warning prompt information when the vehicle is traveling on the navigation path to be analyzed. 56. The navigation warning device according to claim 52, wherein the vehicle is a two-wheeled vehicle. 57. The navigation warning device according to claim 52, wherein the navigation path is a path on a non-motorized vehicle lane. 58. The navigation early warning device according to claim 52, wherein the navigation early warning device further comprises: an acquisition module for acquiring sensing data output by at least one type of sensor installed on the vehicle; A generating module, configured to generate the driving information according to the sensing data and send it to the server. 59. The navigation warning device according to claim 52, wherein at least one of a positioning sensor, a speed sensor, an acceleration sensor, an angle sensor, a curvature sensor, a balance sensor and a vibration sensor is provided on the vehicle ; The travel information includes at least one of travel path, speed, acceleration, angle, curvature, balance, vibration frequency and vibration amplitude. 60. The navigation warning device according to any one of claims 52-59, wherein the abnormal road section types include at least one of pothole road sections, construction road sections, accident road sections, and narrow road sections. 61. The navigation warning device according to claim 52, wherein the output module comprises: a display unit, used for displaying the warning prompt information in the form of a picture on the display screen of the user terminal; And/or, a broadcasting unit, configured to announce the early warning prompt information by means of voice broadcasting. 62. The navigation early warning device according to claim 52, wherein the user terminal comprises the vehicle or a user terminal that initiates a use process for the vehicle. 63. A user terminal, characterized by comprising: a transmitter, a receiver, a processor, an output device, a memory, and a computer program stored on the memory and executable on the processor; the processor When running the computer program, for controlling the operation of the transmitter, the receiver, and the output device; The transmitter is configured to send a navigation early warning analysis instruction to the server under the control of the processor, where the navigation early warning analysis instruction includes the navigation path to be analyzed; The receiver is configured to receive, under the control of the processor, early warning information that carries abnormal road section information sent by the server; the abnormal road section information includes the abnormal road section type and the abnormal road section location; The processor is configured to control the output device to output the warning prompt information. 64. The client according to claim 63, wherein the processor implements the steps of the method according to any one of claims 2 to 20 when running the computer program. 65. A server, comprising: a receiver, a transmitter, a processor, a memory, and a computer program stored on the memory and executable on the processor; the processor is running the In the case of a computer program, it is used to control the operation of the transmitter, the receiver, and the processor; The receiver is configured to receive, under the control of the processor, a navigation early warning analysis instruction sent by a user terminal, where the navigation early warning analysis instruction includes a navigation path to be analyzed; The transmitter is configured to, under the control of the processor, send early warning information carrying abnormal road section information to the user terminal; the abnormal road section information is configured at a preset time according to the vehicle traveling on the navigation path The abnormal road segment information determined by the driving information in the segment; the abnormal road segment information includes the abnormal road segment type and the abnormal road segment location; The processor is configured to acquire travel information of vehicles traveling on the navigation path within a preset time period, and determine abnormal road section information on the navigation path by analyzing the travel information of each vehicle. 66. The server of claim 63, wherein the processor, when running the computer program, implements the steps of the method of any one of claims 22 to 31. 67. A storage medium on which a computer program is stored, wherein the computer program implements the steps of the method according to any one of claims 1 to 31 when the computer program is executed by a processor.

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