CN112131325A - Track determination method, device and equipment and storage medium - Google Patents

Abstract

The invention provides a track determination method, a device and equipment and a storage medium, wherein the track determination method comprises the following steps: extracting data attributes from different data sources, the data attributes including at least: the method comprises the following steps of (1) identifying an object ID of an object, acquiring time information of the object and position information of a data source; storing each extracted data attribute to a designated data storage system; receiving an inquiry condition input from the outside, inquiring a target data attribute meeting the inquiry condition from the specified data storage system according to the inquiry condition, and sequencing data source position information in the target data attribute comprising the same object ID according to time information in the target data attribute comprising the same object ID to obtain a moving track corresponding to the object ID. The mobile track is generated by using the position information of the data source, and the method is suitable for scenes without GPS data receiving equipment, such as a garden.

Description

Trajectory determination method, device and device, storage medium

technical field

The present invention relates to the technical field of data processing, and in particular, to a method, device and equipment for determining a trajectory, and a storage medium.

Background technique

In some closed scenes such as parks, in order to regulate the access management of objects and prevent unsafe factors caused by lawbreakers, the objects in the park are tracked throughout the day, and the object's movement trajectory in the park can be accurately grasped.

In the related trajectory determination method, the trajectory of the object is generated mainly based on the GPS data of the object reported by the user (for example, reported by a mobile phone), and then the trajectory is stored in a file or a relational database. However, in some special scenarios such as a park, there is generally no receiving device for receiving the GPS data reported by the user, and the trajectory cannot be generated by the above method.

SUMMARY OF THE INVENTION

In view of this, the present invention provides a trajectory determination method, device and device, and storage medium, which uses the location information of a data source to generate a moving trajectory, which can be applied to scenarios such as parks without GPS data receiving equipment.

A first aspect of the present invention provides a trajectory determination method, comprising:

Extracting data attributes from different data sources, the data attributes at least include: the object identification ID of the object, the time information when the object was collected, and the location information of the data source;

Store each extracted data attribute to the specified data storage system;

Receive externally input query conditions, query target data attributes that satisfy the query conditions from the specified data storage system according to the query conditions, and search the target data attributes including the same object ID according to the time information in the target data attributes including the same object ID. The data source location information in the target data attribute of the ID is sorted to obtain the movement track corresponding to the object ID.

According to an embodiment of the present invention, each data attribute stored in the designated data storage system has a corresponding index, and the index corresponding to the data attribute includes time information contained in the data attribute;

The query conditions include: a target time period; according to the query conditions, query the target data attributes that satisfy the query conditions from the specified data storage system, including:

querying a target index in a specified data storage system according to the target time period, where the time information in the target index is within the target time period;

Determining the data attribute corresponding to the target index as the reference data attribute;

The target data attribute is determined from the reference data attributes.

According to an embodiment of the present invention, determining the target data attribute from the reference data attribute includes:

Selecting a target object ID from the object IDs included in each reference data attribute, wherein the number of reference data attributes including the target object ID in all reference data attributes is greater than a preset number;

The reference data attribute including the target object ID is pulled from the designated data storage system in a scroll manner as the target data attribute.

According to an embodiment of the present invention, the designated data storage system includes at least two index storage segments, each index storage segment has a corresponding time segment, and each index storage segment is used to store time information within the corresponding time segment. index;

Querying the target index in the specified data storage system according to the target time period, including:

Determine a target index storage segment from the specified data storage system, and the time segment corresponding to the target index storage segment has an intersection with the target time segment;

The target index is queried from the target index bucket.

According to an embodiment of the present invention,

The method further includes:

Obtain the reference object ID input from the outside, determine the reference movement track corresponding to the reference object ID, and calculate the similarity between the movement track corresponding to each object ID and the reference movement track;

At least one movement trajectory with the greatest similarity to the reference movement trajectory is selected from the movement trajectories corresponding to each object ID.

According to an embodiment of the present invention, the calculating the similarity between the movement trajectory corresponding to each object ID and the reference movement trajectory includes:

For the movement track corresponding to each object ID, determine the data attribute pair and the data attribute sum N according to the movement track and the reference movement track, calculate the ratio of the number of data attribute pairs to the N, and determine the movement track and the N according to the ratio. The similarity between the reference movement trajectories;

Wherein, the data source location information included in one data attribute in each pair of data attributes is in the movement track, the data source location information included in the other data attribute is in the reference movement track, and the time of each pair of data attributes is in the movement track. The difference value of the information is less than or equal to the specified time information, and the region identifiers corresponding to the data source location information in each pair of data attribute pairs are the same; the sum of the data attributes is the movement track and the data where the position information of each data source in the reference movement track is located. The sum of the number of attributes.

A second aspect of the present invention provides a trajectory determination device, comprising:

A data attribute extraction module, used for extracting data attributes from different data sources, the data attributes at least include: the object identification ID of the object, the time information of the collected object, and the location information of the data source;

a data attribute storage module for storing each extracted data attribute to a specified data storage system;

The movement trajectory generation module is used for receiving externally input query conditions, and according to the query conditions, from the designated data storage system to query the target data attributes that satisfy the query conditions, according to the time in the target data attributes including the same object ID The information sorts the data source location information in the target data attribute including the same object ID to obtain a movement track corresponding to the object ID.

According to an embodiment of the present invention, each data attribute stored in the designated data storage system has a corresponding index, and the index corresponding to the data attribute includes time information contained in the data attribute;

The query conditions include: target time period; the movement trajectory generation module includes:

a target index query unit, configured to query a target index in a specified data storage system according to the target time period, and the time information in the target index is within the target time period;

a reference data attribute determining unit, configured to determine the data attribute corresponding to the target index as a reference data attribute;

The target data attribute determining unit is used for determining the target data attribute from each reference data attribute.

According to an embodiment of the present invention, the target data attribute determination unit includes:

A selection subunit for selecting a target object ID from the object IDs included in each reference data attribute, wherein the number of reference data attributes that include the target object ID in all reference data attributes is greater than a preset number;

The pulling subunit is used for pulling the reference data attribute including the target object ID from the designated data storage system in the scroll mode as the target data attribute.

According to an embodiment of the present invention, the designated data storage system includes at least two index storage segments, each index storage segment has a corresponding time segment, and each index storage segment is used to store time information within the corresponding time segment. index;

The target index query unit includes:

an index storage segment determination subunit, configured to determine a target index storage segment from the specified data storage system, and the time segment corresponding to the target index storage segment has an intersection with the target time segment;

A target index query subunit, configured to query the target index from the target index storage segment.

According to an embodiment of the present invention,

The device further includes:

A similarity calculation module, used to obtain the reference object ID input from the outside, determine the reference movement track corresponding to the reference object ID, and calculate the similarity between the movement track corresponding to each object ID and the reference movement track;

A movement trajectory selection module, configured to select at least one movement trajectory with the greatest similarity to the reference movement trajectory from the movement trajectories corresponding to each object ID.

According to an embodiment of the present invention, the similarity calculation module includes:

The similarity calculation unit is used to determine the data attribute pair and the data attribute sum N according to the movement trajectory and the reference movement trajectory for the movement trajectory corresponding to each object ID, calculate the ratio of the number of data attribute pairs and the N, according to the The ratio determines the similarity between the movement trajectory and the reference movement trajectory;

Wherein, the data source location information included in one data attribute in each pair of data attributes is in the movement track, the data source location information included in the other data attribute is in the reference movement track, and the time of each pair of data attributes is in the movement track. The difference value of the information is less than or equal to the specified time information, and the region identifiers corresponding to the data source location information in each pair of data attribute pairs are the same; the sum of the data attributes is the movement track and the data where the position information of each data source in the reference movement track is located. The sum of the number of attributes.

A third aspect of the present invention provides an electronic device, including a processor and a memory; the memory stores a program that can be called by the processor; wherein, when the processor executes the program, the above-described embodiments are implemented. trajectory determination method.

A fourth aspect of the present invention provides a machine-readable storage medium on which a program is stored, and when the program is executed by a processor, implements the trajectory determination method described in the foregoing embodiments.

The embodiment of the present invention has the following beneficial effects:

In this embodiment of the present invention, data attributes extracted from different data sources are stored in a designated data storage system. When receiving externally input query conditions, the target data attributes can be searched out from the designated data storage system. The position information in the target data attribute of the ID can be sorted according to the time information to generate the movement trajectory corresponding to the object ID. Since the position information of the data source in the scene is known, when the data source collects the object, it means that the object has passed the data source. , so the location information of the data source can be regarded as a trajectory point of the object, which realizes the use of the location information of the data source to generate the movement trajectory of the object, which can be applied to scenarios such as parks without GPS data receiving equipment.

Description of drawings

1 is a schematic flowchart of a method for determining a trajectory according to an embodiment of the present invention;

2 is a structural block diagram of a trajectory determination device according to an embodiment of the present invention;

FIG. 3 is a structural block diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. Where the following description refers to the drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the illustrative examples below are not intended to represent all implementations consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with some aspects of the invention as recited in the appended claims.

The terminology used in the present invention is for the purpose of describing particular embodiments only and is not intended to limit the present invention. As used in this specification and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It will also be understood that the term "and/or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items.

It should be understood that although the terms first, second, third, etc. may be used herein to describe various devices, this information should not be limited by these terms. These terms are only used to distinguish devices of the same type from one another. For example, a first device could be termed a second device, and similarly, a second device could be termed a first device, without departing from the scope of the present invention. Depending on the context, the word "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determining."

In order to make the description of the present invention clearer and concise, some technical terms in the present invention are explained below:

ES: The full name is ElasticSearch. It is an open source, real-time distributed search and analysis engine. The index in Elasticsearch is a non-relational database for storing data.

LCSS: The full name is Longest-Common-Subsequence, the longest common subsequence algorithm.

Kafka: A high-throughput, distributed publish-subscribe messaging system.

Scroll method: A method used for large-scale data traversal in ElasticSearch, similar to the cursor of the database, and the traversal speed is significantly faster than the paging method.

The method for determining a trajectory according to the embodiment of the present invention will be described in more detail below, but it should not be limited thereto.

The trajectory determination method of the embodiment of the present invention can be used in closed scenes such as parks. In these scenes, a collection device for collecting objects is originally set at a fixed point. When the collection device collects an object, it will generate corresponding data attributes. , the trajectory of the object can be generated by the location information of different collection devices that have collected the same object, making full use of the old resources in the park, parking lot, traffic section and other scenes, and without the participation of any GPS-related equipment.

Taking parks as an example, parks are the most common geographical category for production and office work by social organizations. With the continuous deepening of smart city construction, the concept of "smart parks" has also entered the public's field of vision, and the number of parks in various forms has exploded. In recent years, with the continuous development of video surveillance technology and the continuous improvement of people's security awareness, the application of park monitoring has also continued to increase.

Generally, there is no device in the park to receive the GPS data sent by the user through the mobile phone, and it is impossible to generate the user's trajectory based on the GPS data reported by the user. Monitoring equipment and other acquisition equipment, these equipment usually have fixed points, that is, the location information of the equipment is fixed. movement track.

In one embodiment, referring to FIG. 1, a trajectory determination method includes the following steps:

S100: Extract data attributes from different data sources, where the data attributes at least include: the object identification ID of the object, the time information of the collected object, and the location information of the data source;

S200: Store each extracted data attribute in a designated data storage system;

S300: Receive an externally input query condition, query the specified data storage system for a target data attribute that satisfies the query condition according to the query condition, and search the target data attribute including the same object ID according to the time information in the target data attribute including the same object ID. The data source location information in the target data attribute of the same object ID is sorted to obtain the movement track corresponding to the object ID.

The execution body of the trajectory determination method in the embodiment of the present invention may be an electronic device, for example, a server composed of a computer device, and the performance of the computer device may be configured as required.

In step S100, data attributes are extracted from different data sources, and the data attributes at least include: the object identification ID of the object, the time information when the object is collected, and the location information of the data source.

For example, the data source is the collection equipment in the closed scene such as the park. The collection equipment can include access control equipment, face capture cameras, video monitoring equipment, etc., specifically, access control card readers, cameras, etc. Since the data source has fixed points in the scene, the location information of the data source can be pre-configured in the data source, and the data source will generate corresponding data when the object is collected, and the data attributes can be extracted from it.

The data formats of different data sources may be different. In other words, the data generated by different data sources may be heterogeneous data, and the required data attributes can be extracted from these heterogeneous data through Kafka. The data attribute includes at least the object identification ID of the object, the time information when the object is collected, and the location information of the data source. Of course, it is not limited to this, and more information may be included as needed.

The object may refer to a person, a motor vehicle, a non-motor vehicle, etc. The object ID is used to represent the object. Taking a person as an example, the object ID may be the person's name, ID number, etc., but is not limited to this. The time information may include information indicating year, month, day, hour, minute, and second. The location information of the data source may be a point identification of the data source in the scene, or may be a geographic location.

The object is collected by the data source, indicating that the object has passed the data source, so the location information of the data source can be regarded as the location information of the object at that time, and the object can be constructed by collecting the location information of different data sources of the same object. movement trajectory.

In step S200, each extracted data attribute is stored in a designated data storage system.

The specified data storage system is preferably ES, which can realize data access in a distributed environment. Of course, this is just an example, and other data storage systems may also be used. In one way, after extracting data attributes from the data source through Kafka, the data attributes can be sent to ES in batches to realize timely and fast storage of data attributes.

When the data attribute is stored in the ES, an index can be assigned to the data attribute in the ES, and the data attribute can be subsequently queried according to the index of the data attribute. Storing data attributes in ES can realize fast storage of data attributes, and can cope with large-scale data volume scenarios, thereby ensuring the efficiency of trajectory extraction.

In step S300, the query condition input from the outside is received, the target data attribute that satisfies the query condition is queried from the specified data storage system according to the query condition, and the target data attribute including the same object ID is queried according to the time information in the target data attribute. The data source position information in the target data attribute including the same object ID is sorted to obtain the movement track corresponding to the object ID.

The query conditions can be carried in the externally input query request. According to the query conditions, the required data attributes can be queried, for example, the data attributes within a period of time can be queried, or the data attributes corresponding to certain object IDs can be queried, etc. Wait.

The movement track corresponding to the same object ID in the target data attribute is generated by using the queried target data attribute, and the data source location information in the target data attribute including the same object ID is calculated according to the time information in the target data attribute including the same object ID. Sort to get the movement track corresponding to the object ID.

The position information in the target data attribute of the same object ID is in the same movement track, and the position information is in the order of time information. The order may be from morning to night, or from late to early, and the specific order is not limited.

The movement track corresponding to each object ID can be used as the movement track of the object identified by the object ID. In this way, the position information of the data source is used to generate the movement track of the object.

In this embodiment of the present invention, data attributes extracted from different data sources are stored in a designated data storage system. When receiving externally input query conditions, the target data attributes can be searched out from the designated data storage system. The position information in the target data attribute of the ID can be sorted according to the time information to generate the movement trajectory corresponding to the object ID. Since the position information of the data source in the scene is known, when the data source collects the object, it means that the object has passed the data source. , so the location information of the data source can be regarded as a trajectory point of the object, which realizes the use of the location information of the data source to generate the movement trajectory of the object, which can be applied to scenarios such as parks without GPS data receiving equipment.

In one embodiment, each data attribute stored in the specified data storage system has a corresponding index, and the index corresponding to the data attribute includes time information contained in the data attribute;

The query conditions include: a target time period; in step S300, query the specified data storage system for target data attributes that satisfy the query conditions according to the query conditions, including:

S301: query a target index in a specified data storage system according to the target time period, where the time information in the target index is within the target time period;

S302: Determine the data attribute corresponding to the target index as a reference data attribute;

S303: Determine the target data attribute from each reference data attribute.

For example, the query condition is: start_time<time<end_time; start_time is the start time of the target time period, and end_time is the end time of the target time period.

In this way, according to the query conditions, the index whose time information is in the target time period from the start time to the end time can be found, and the target index can be found by traversing all the indexes in the specified data storage system, for example, judging the traversed Whether the time information in the index is in the target time period, if so, the traversed index is the target index.

The data attribute corresponding to the target index is determined as the reference data attribute, and the reference data attribute can be further screened, and the target data attribute that is more suitable for the needs can be determined from each reference data attribute. Of course, the data attribute corresponding to the target index can also be directly determined as the target data attribute. Using the index to query data attributes, the query speed is faster.

In one embodiment, in step S303, the target data attribute is determined from the reference data attribute, including:

S3031: Select a target object ID from the object IDs included in each reference data attribute, wherein the number of reference data attributes including the target object ID in all reference data attributes is greater than a preset number;

S3032: Pull a reference data attribute including the target object ID from the designated data storage system in a scroll manner as the target data attribute.

In step S3031, the preset number can be determined according to the situation. Taking the preset number of 1 as an example, if the number of reference data attributes of the same object ID is only 1, then the object ID is not the target object ID. If the same object ID The number of reference data attributes is greater than 1, then the object ID is the target object ID.

The operation of selecting the target object ID can be controlled to be performed by the specified data storage system. The reference data attributes with fewer reference data attributes of the same object ID are discarded, and the number of data attributes actually pulled from the specified data storage system can be reduced. Reduce the amount of processing required by the server.

In step S3032, after the target object ID is determined, the data can be pulled in the scroll mode, and the reference data attribute including the target object ID is pulled to the server as the target data attribute.

Using the Scroll method to pull data can avoid the problem of deep paging, which is very suitable for situations with large amounts of data. Of course, after the data pull is completed, the memory data used to implement the Scroll function in the server can be deleted to avoid occupying memory.

In one embodiment, the designated data storage system includes at least two index storage segments, each index storage segment has a corresponding time segment, and each index storage segment is used to store an index whose time information is within the corresponding time segment;

In step S301, query the target index in the specified data storage system according to the target time period, including:

S3011: Determine a target index storage segment from the specified data storage system, where a time segment corresponding to the target index storage segment and the target time segment have an intersection;

S3012: Query the target index from the target index storage segment.

In order to prevent the performance of a single data attribute index from being degraded due to excessive data, you can create a new index storage segment (which can be distributed on multiple shards) at regular intervals, such as 7 days, and store the newly acquired data attributes in the new index storage segment. , which maximizes data storage and improves performance.

Each index storage segment has a corresponding time segment, which can store the data attributes obtained in the corresponding time segment, and the index storage segment where the data attribute is located is determined according to the time segment.

In step S3011, the target index storage segment may be determined from the specified data storage system according to the target time period.

For example, the time period corresponding to an index storage segment is 2018-08-28-00 to 2018-09-04-00 (the index storage segment stores the data attributes obtained during this time period), if the target time period From 2018-08-30-00 to 2018-09-06-00, the index storage segment is the target index segment. Of course, another target index storage segment can also be found, and the time period corresponding to the other target index storage segment is 2018-09-04-00 to 2018-09-11-00, so that the specified data storage system can be found. All target index buckets whose corresponding time period intersects with the target time period.

In step S3012, the target index is queried from the target index storage segment. Each target index in each target index storage segment can be traversed to determine whether the time information in the traversed index is in the target time period, and if so, the traversed index is the target index.

In one embodiment, the method further comprises the following steps:

S400: Obtain an externally input reference object ID, determine a reference movement track corresponding to the reference object ID, and calculate the similarity between the movement track corresponding to each object ID and the reference movement track;

S500: Select at least one movement trajectory with the greatest similarity to the reference movement trajectory from the movement trajectories corresponding to each object ID.

Steps S400 and S500 may be performed after step S300.

The reference object ID can be one of each object ID (that is, the object ID of the object whose movement track is determined), and at this time, the reference movement track corresponding to the reference object ID can be obtained from the movement track corresponding to each object ID; of course, The reference object ID may also be the object ID of other objects. In this case, the reference object ID may be obtained from a local preset movement track.

The similarity between the movement trajectory corresponding to each object ID and the reference movement trajectory can be calculated by a trajectory similarity algorithm, and the specific trajectory similarity algorithm is not limited, such as the LCSS algorithm.

After calculating the similarity between the movement trajectory corresponding to each object ID and the reference movement trajectory, at least one movement trajectory with the greatest similarity with the reference movement trajectory is selected from the movement trajectories corresponding to each object ID. The movement trajectories may be sorted in descending order of similarity, and the first M movement trajectories may be used as target movement trajectories, where M may be an integer greater than or equal to 1.

In order to regulate the entry and exit management of personnel in the park and prevent unsafe factors caused by criminals, track the trajectory of personnel in the park throughout the day, accurately grasp the movement trajectory of each employee in the park, and find out the trajectory of the target person according to the trajectory of the target person. Corresponding peers are of great significance to the safety of the park.

In this embodiment, the selected movement trajectory is regarded as the target movement trajectory. Since the similarity between the target movement trajectory and the reference movement trajectory is relatively high, it can be considered that the object corresponding to the target movement trajectory is a peer object of the object corresponding to the reference movement trajectory. This can further determine whether the peers are legal and ensure the safety of scenarios such as parks.

In step S400, the calculation of the similarity between the movement trajectory corresponding to each object ID and the reference movement trajectory includes:

For the movement track corresponding to each object ID, determine the data attribute pair and the data attribute sum N according to the movement track and the reference movement track, calculate the ratio of the number of data attribute pairs to the N, and determine the movement track and the N according to the ratio. similarity between the reference movement trajectories;

Wherein, the data source location information included in one data attribute in each pair of data attributes is in the movement track, the data source location information included in the other data attribute is in the reference movement track, and the time of each pair of data attributes is in the movement track. The difference value of the information is less than or equal to the specified time information, and the region identifiers corresponding to the data source location information in each pair of data attribute pairs are the same; the sum of the data attributes is the movement track and the data where the position information of each data source in the reference movement track is located. The sum of the number of attributes.

Determining the data attribute pair according to the movement trajectory and the reference movement trajectory is actually to determine the longest common subsequence of the movement trajectory and the reference movement trajectory.

Assume that the movement trajectory is X, and the reference movement trajectory is Y. The easiest algorithm to think of when solving the longest common subsequence problem is the exhaustive search method, that is, for each subsequence of X, check whether it is also a subsequence of Y, so as to determine whether it is a common subsequence of X and Y, And the longest common subsequence is selected during the inspection process. After all subsequences of X and Y have been checked, the longest common subsequence of X and Y can be found.

In order to reduce the amount of computation required, the LCSS algorithm can be used to find the longest common subsequence of X and Y. Assuming that the movement trajectory X=<x1,x2,...,xm> and Y=<y1,y2,...,yn> a longest common subsequence Z=<z1,z2,...,zk>, then the LCSS algorithm will There are the following recursive definitions:

If xm=yn, then zk=xm=yn and Zk-1 is the longest common subsequence of Xm-1 and Yn-1;

If xm≠yn and zk≠xm, then Z is the longest common subsequence of Xm-1 and Y;

If xm≠yn and zk≠yn, then Z is the longest common subsequence of X and Yn-1.

Among them, m is the length of X, n is the length of Y, and k is the length of Z.

xm=yn means that xm is similar to yn, xm≠yn means that xm and yn are not similar, and the same is true for other similar situations. In this embodiment, in the LCSS algorithm, under which conditions xm and yn are similar, and under which conditions xm and yn are not similar, are customized.

Taking the position information xm in X and the position information yn in Y as an example, if the area identifier corresponding to xm is the same as the area identifier corresponding to yn, and the time information corresponding to xm and the time information corresponding to yn differ by no more than specified time information, then xm is similar to yn, that is, xm=yn, otherwise xm≠yn. The specified time information is, for example, 3 minutes, 5 minutes, etc., of course, the specific value is not limited to this.

The area identifier can also be extracted from the data source and included in the data attribute. Each data source may be pre-configured with the area identifier of the area where it is located, and the area identifiers of the data sources in the same area are the same, for example, the area identifiers of the data sources in the same canteen in the park are the same. If the area identifiers corresponding to the two location information are the same, it means that the two objects have appeared in the same area, such as a cafeteria.

Based on the above definition, the problem can be regarded as a dynamic programming problem, a matrix C[m,n] is constructed, and the following recursive relationship is established to solve the matrix:

Among them, i is less than or equal to m, and j is less than or equal to n.

According to C[m,n] solved according to the above recursive relationship, the length of the longest common subsequence of X and Y is recorded in the element c[m,n], c[m,n] is the matrix C[m,n] in the length record element c[m,n] The element at row m and column n.

The obtained length of the longest common subsequence of X and Y is the number of data attribute pairs determined according to the movement track and the reference movement track. The longer the length, the higher the similarity between the movement trajectory and the reference movement trajectory.

In this embodiment, considering the time interval between the object information collected by the collection device and the spatial distance of each collection device, when the area identifiers corresponding to xm and yn are the same and the corresponding time information is not much different, xm and yn Only similar, improve the recall rate of similar trajectories.

Next, the ratio of the number of data attribute pairs to the N can be calculated, and the similarity between the movement trajectory and the reference movement trajectory can be determined according to the ratio. For example, the product of the ratio and the preset value can be used as similarity.

Taking the default value of 2 as an example, the calculation formula of similarity is:

S=2*(c[m,n])/(m+n)

m is the number of data attributes where each data source location information is located in the movement track, n is the number of data attributes where each data source location information is located in the reference movement track, and m+n is the N.

The following is a specific example to illustrate the calculation method of the similarity between the movement trajectory and the reference movement trajectory. Assuming that the specified time information is 5 minutes, in the movement trajectory X, the time information and position information of the first object appearing are:

2018-09-28 12:00:00 A,

2018-09-28 12:10:00 B,

2018-09-28 12:20:00 C;

2018-09-28 12:30:00 E;

In the reference movement track Y, the time information and position information of the reference object appearing are:

2018-09-28 12:00:00 A,

2018-09-28 12:16:00 B,

2018-09-28 12:20:00 D;

2018-09-28 12:30:00 E;

Among them, A, B, C, D, E are location information, similar location information is A and E, and the corresponding time information is the same, correspondingly, the similarity is 2*2/(3+3)=66 %.

The present invention also provides a trajectory determination apparatus, referring to FIG. 2 , in one embodiment, the trajectory determination apparatus 100 includes:

The data attribute extraction module 101 is used for extracting data attributes from different data sources, the data attributes at least include: the object identification ID of the object, the time information when the object is collected, and the location information of the data source;

a data attribute storage module 102, configured to store each extracted data attribute to a specified data storage system;

The movement trajectory generation module 103 is configured to receive externally input query conditions, query the target data attributes that satisfy the query conditions from the specified data storage system according to the query conditions, and query the target data attributes that satisfy the query conditions according to the query conditions. The time information sorts the data source location information in the target data attribute including the same object ID to obtain a movement track corresponding to the object ID.

In one embodiment, each data attribute stored in the specified data storage system has a corresponding index, and the index corresponding to the data attribute includes time information contained in the data attribute;

The query conditions include: target time period; the movement trajectory generation module includes:

a target index query unit, configured to query a target index in a specified data storage system according to the target time period, and the time information in the target index is within the target time period;

a reference data attribute determining unit, configured to determine the data attribute corresponding to the target index as a reference data attribute;

The target data attribute determining unit is used for determining the target data attribute from each reference data attribute.

In one embodiment, the target data attribute determination unit includes:

A selection subunit for selecting a target object ID from the object IDs included in each reference data attribute, wherein the number of reference data attributes that include the target object ID in all reference data attributes is greater than a preset number;

The pulling subunit is used for pulling the reference data attribute including the target object ID from the designated data storage system in the scroll mode as the target data attribute.

In one embodiment, the designated data storage system includes at least two index storage segments, each index storage segment has a corresponding time segment, and each index storage segment is used to store an index whose time information is within the corresponding time segment;

The target index query unit includes:

an index storage segment determination subunit, configured to determine a target index storage segment from the specified data storage system, and the time segment corresponding to the target index storage segment has an intersection with the target time segment;

A target index query subunit, configured to query the target index from the target index storage segment.

In one embodiment,

The device further includes:

A similarity calculation module, used to obtain the reference object ID input from the outside, determine the reference movement track corresponding to the reference object ID, and calculate the similarity between the movement track corresponding to each object ID and the reference movement track;

A movement trajectory selection module, configured to select at least one movement trajectory with the greatest similarity to the reference movement trajectory from the movement trajectories corresponding to each object ID.

In one embodiment, the similarity calculation module includes:

The similarity calculation unit is used to determine the data attribute pair and the data attribute sum N according to the movement trajectory and the reference movement trajectory for the movement trajectory corresponding to each object ID, calculate the ratio of the number of data attribute pairs and the N, according to the The ratio determines the similarity between the movement trajectory and the reference movement trajectory;

Wherein, the data source location information included in one data attribute in each pair of data attributes is in the movement track, the data source location information included in the other data attribute is in the reference movement track, and the time of each pair of data attributes is in the movement track. The difference value of the information is less than or equal to the specified time information, and the region identifiers corresponding to the data source location information in each pair of data attribute pairs are the same; the sum of the data attributes is the movement track and the data where the position information of each data source in the reference movement track is located. The sum of the number of attributes.

For details of the implementation process of the functions and functions of each unit in the above device, please refer to the implementation process of the corresponding steps in the above method, which will not be repeated here.

For the apparatus embodiments, since they basically correspond to the method embodiments, reference may be made to the partial descriptions of the method embodiments for related parts. The device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units.

The present invention also provides an electronic device, including a processor and a memory; the memory stores a program that can be called by the processor; wherein, when the processor executes the program, the trajectory described in the foregoing embodiments is implemented Determine the method.

The embodiments of the trajectory determination apparatus of the present invention can be applied to electronic equipment. Taking software implementation as an example, a device in a logical sense is formed by reading the corresponding computer program instructions in the non-volatile memory into the memory for operation by the processor of the electronic device where the device is located. From the perspective of hardware, as shown in FIG. 3 , FIG. 3 is a hardware structure diagram of the electronic device where the trajectory determination apparatus 100 is located according to an exemplary embodiment of the present invention, except for the processor 510 and the memory shown in FIG. 3 530 , the interface 520 , and the non-volatile memory 540 , the electronic device in which the apparatus 100 is located in the embodiment may also include other hardware generally according to the actual function of the electronic collection device, which will not be repeated here.

The present invention also provides a machine-readable storage medium on which a program is stored, and when the program is executed by a processor, implements the trajectory determination method described in any one of the foregoing embodiments.

The present invention may take the form of a computer program product embodied on one or more storage media having program code embodied therein, including but not limited to disk storage, CD-ROM, optical storage, and the like. Machine-readable storage media includes both persistent and non-permanent, removable and non-removable media, and storage of information can be implemented by any method or technology. Information may be computer readable instructions, data structures, modules of programs, or other data. Examples of machine-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage , magnetic tape cartridges, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the present invention. within the scope of protection.

Claims (14)

1. a trajectory determination method, is characterized in that, comprises: Extracting data attributes from different data sources, the data attributes at least include: the object identification ID of the object, the time information when the object was collected, and the location information of the data source; Store each extracted data attribute to the specified data storage system; Receive externally input query conditions, query target data attributes that satisfy the query conditions from the specified data storage system according to the query conditions, and search the target data attributes including the same object ID according to the time information in the target data attributes including the same object ID. The data source location information in the target data attribute of the ID is sorted to obtain the movement track corresponding to the object ID. 2. The trajectory determination method of claim 1, wherein each data attribute stored in the specified data storage system has a corresponding index, and the index corresponding to the data attribute includes time information contained in the data attribute; The query conditions include: a target time period; according to the query conditions, query the target data attributes that satisfy the query conditions from the specified data storage system, including: querying a target index in a specified data storage system according to the target time period, where the time information in the target index is within the target time period; Determining the data attribute corresponding to the target index as the reference data attribute; The target data attribute is determined from the reference data attributes. 3. The trajectory determination method according to claim 2, wherein determining the target data attribute from the reference data attribute, comprising: Selecting a target object ID from the object IDs included in each reference data attribute, wherein the number of reference data attributes including the target object ID in all reference data attributes is greater than a preset number; The reference data attribute including the target object ID is pulled from the designated data storage system in a scroll manner as the target data attribute. 4. The trajectory determination method according to claim 2, wherein the designated data storage system comprises at least two index storage segments, each index storage segment has a corresponding time segment, and each index storage segment is used for The index of the storage time information in the corresponding time period; Querying the target index in the specified data storage system according to the target time period, including: Determine a target index storage segment from the specified data storage system, and the time segment corresponding to the target index storage segment has an intersection with the target time segment; The target index is queried from the target index bucket. 5. The trajectory determination method of claim 1, wherein the method further comprises: Obtain the reference object ID input from the outside, determine the reference movement track corresponding to the reference object ID, and calculate the similarity between the movement track corresponding to each object ID and the reference movement track; At least one movement trajectory with the greatest similarity to the reference movement trajectory is selected from the movement trajectories corresponding to each object ID. 6. The trajectory determination method according to claim 5, wherein the calculating the similarity between the movement trajectory corresponding to each object ID and the reference movement trajectory comprises: For the movement track corresponding to each object ID, determine the data attribute pair and the data attribute sum N according to the movement track and the reference movement track, calculate the ratio of the number of data attribute pairs to the N, and determine the movement track and the N according to the ratio. similarity between the reference movement trajectories; Wherein, the data source location information included in one data attribute in each pair of data attributes is in the movement track, the data source location information included in the other data attribute is in the reference movement track, and the time of each pair of data attributes is in the movement track. The difference value of the information is less than or equal to the specified time information, and the region identifiers corresponding to the data source location information in each pair of data attribute pairs are the same; the sum of the data attributes is the movement track and the data where the position information of each data source in the reference movement track is located. The sum of the number of attributes. 7. A trajectory determination device, characterized in that, comprising: A data attribute extraction module, used for extracting data attributes from different data sources, the data attributes at least include: the object identification ID of the object, the time information of the collected object, and the location information of the data source; a data attribute storage module for storing each extracted data attribute to a specified data storage system; The movement trajectory generation module is used for receiving externally input query conditions, and according to the query conditions, from the designated data storage system to query the target data attributes that satisfy the query conditions, according to the time in the target data attributes including the same object ID The information sorts the data source location information in the target data attribute including the same object ID to obtain a movement track corresponding to the object ID. 8. The trajectory determination device of claim 7, wherein each data attribute stored in the designated data storage system has a corresponding index, and the index corresponding to the data attribute includes time information contained in the data attribute; The query conditions include: target time period; the movement trajectory generation module includes: a target index query unit, configured to query a target index in a specified data storage system according to the target time period, and the time information in the target index is within the target time period; a reference data attribute determining unit, configured to determine the data attribute corresponding to the target index as a reference data attribute; The target data attribute determining unit is used for determining the target data attribute from each reference data attribute. 9. The trajectory determination device according to claim 8, wherein the target data attribute determination unit comprises: A selection subunit for selecting a target object ID from the object IDs included in each reference data attribute, wherein the number of reference data attributes that include the target object ID in all reference data attributes is greater than a preset number; The pulling subunit is used for pulling the reference data attribute including the target object ID from the designated data storage system in the scroll mode as the target data attribute. 10 . The trajectory determination device according to claim 8 , wherein the designated data storage system includes at least two index storage segments, each index storage segment has a corresponding time segment, and each index storage segment is used for 10 . The index of the storage time information in the corresponding time period; The target index query unit includes: an index storage segment determination subunit, configured to determine a target index storage segment from the specified data storage system, and the time segment corresponding to the target index storage segment has an intersection with the target time segment; A target index query subunit, configured to query the target index from the target index storage segment. 11. The trajectory determination device according to claim 7, characterized in that, The device further includes: A similarity calculation module, used to obtain the reference object ID input from the outside, determine the reference movement track corresponding to the reference object ID, and calculate the similarity between the movement track corresponding to each object ID and the reference movement track; A movement trajectory selection module, configured to select at least one movement trajectory with the greatest similarity to the reference movement trajectory from the movement trajectories corresponding to each object ID. 12. The trajectory determination device according to claim 11, wherein the similarity calculation module comprises: The similarity calculation unit is used to determine the data attribute pair and the data attribute sum N according to the movement trajectory and the reference movement trajectory for the movement trajectory corresponding to each object ID, calculate the ratio of the number of data attribute pairs and the N, according to the The ratio determines the similarity between the movement trajectory and the reference movement trajectory; Wherein, the data source location information included in one data attribute in each pair of data attributes is in the movement track, the data source location information included in the other data attribute is in the reference movement track, and the time of each pair of data attributes is in the movement track. The difference value of the information is less than or equal to the specified time information, and the region identifiers corresponding to the data source location information in each pair of data attribute pairs are the same; the sum of the data attributes is the movement track and the data where the position information of each data source in the reference movement track is located. The sum of the number of attributes. 13. An electronic device, characterized in that it comprises a processor and a memory; the memory stores a program that can be called by the processor; wherein, when the processor executes the program, the implementation of claims 1-6 Any one of the trajectory determination methods described above. 14. A machine-readable storage medium, characterized in that a program is stored thereon, and when the program is executed by a processor, the method for determining a trajectory according to any one of claims 1-6 is implemented.

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