TWI897818B - A system of intelligent insurance recommendation matching and dynamic cost estimation and the method thereof - Google Patents

Abstract

The present invention provides an intelligent insurance recommendation matching and dynamic cost estimation system and method thereof, comprising a driving behavior database, a policy database, a mobile device, a fixed device, a calculation analysis module, a recommendation module, a terminal device and a display interface. The mobile device is mounted on the steering wheel of the vehicle via the fixing device, wherein the fixing device further comprises a sensing element. The driving behavior database transmits the behavior data to the calculation analysis module, which will process the behavior data and the policy plan content separately to generate corresponding behavior scoring results. The module will then analyze and compare the policy plan content based on the behavior scoring results to generate a comparison result. Finally, the corresponding policy amount will be calculated based on the scoring results and the comparison results, so as to achieve accurate matching recommendations and dynamic cost estimation.

Description

Intelligent car insurance precise recommendation matching and dynamic cost estimation system and method

The present invention relates to the field of vehicle insurance systems, particularly a system that utilizes a special structure in conjunction with a mobile device to collect driving behavior data and analyze and recommend appropriate insurance plans through an artificial intelligence (AI) module.

Existing vehicle insurance often relies on static risk assessment methods, such as a driver's age, gender, driving experience, and past accident history. However, these methods fail to fully reflect a driver's real-time driving behavior and risk profile. With technological advancements, an increasing number of insurance companies are exploring the use of in-vehicle devices and smartphones to collect driving behavior data, enabling more accurate risk assessments and insurance solutions.

Traditionally, vehicle insurance policies have been determined based on static factors such as a driver's age, gender, location, and vehicle type. While useful, these factors fail to account for the dynamic nature of individual driving behavior, which can significantly impact a driver's risk profile. In recent years, the insurance industry has shifted toward usage-based insurance (UBI) models, which aim to align premiums more closely with actual driving behavior. These models typically rely on remote information processing devices installed on the vehicle to collect data on parameters such as speed, acceleration, and braking patterns. However, the integration of such systems with existing vehicle components, such as steering wheels, and the use of smartphones or sensors for data collection remain limited areas of exploration.

While usage-based insurance models have made progress, existing technologies still face several drawbacks and challenges. A significant challenge is the lack of seamless integration between data collection devices and vehicle components, which can lead to inaccurate data collection and analysis. Furthermore, existing systems typically do not provide drivers with immediate feedback, limiting their ability to proactively improve their driving habits. Furthermore, the process of adjusting insurance policies based on driver behavior is often manual and time-consuming, requiring intervention from the insurance provider. Systems that can benchmark driver performance against historical data are also needed to provide more accurate recommendations.

While existing technologies are capable of collecting and analyzing driving behavior data, several shortcomings and challenges remain. First, existing systems are often unable to update and dynamically adjust insurance premiums in real time, limiting the flexibility and personalization of insurance plans. Second, many systems lack effective data analysis and learning capabilities, making them unable to fully utilize the collected data to provide accurate insurance plan recommendations.

The purpose of this invention is to provide an intelligent auto insurance precise recommendation, matching, and dynamic cost estimation system to address the aforementioned problems in existing technologies.

Therefore, to achieve the aforementioned objectives of the present invention, the present invention provides a system comprising: a mobile device mounted on the steering wheel of a vehicle via a fixed device, wherein a sensor element in the fixed device is electrically connected to the mobile device; a terminal device configured to allow an insurance provider to input policy details and upload the information to a policy database for storage; a driving behavior database configured to receive behavioral data collected by the sensor element; and a computational analysis module configured to receive and analyze the policy details from the policy database and the behavioral data in the driving behavior database. a recommendation module that receives the comparison result and the policy amount from the computational analysis module and integrates them to generate a new policy plan, which is presented through a display interface of the terminal device. The computational analysis module processes the behavioral data and the policy plan content separately, first analyzing the behavioral data and generating a corresponding behavioral score result. It then compares the policy plan content with the behavioral score result to generate the comparison result. Finally, it combines the behavioral score result with the comparison result to calculate the corresponding policy amount.

The sensor component includes an accelerometer, a gyroscope, a GPS, an image recognition module, and an OBD-II interface information collection module.

The GPS receives location information from the mobile device's positioning module and records the vehicle's trajectory and speed. The mobile device's positioning module receives satellite signals and performs positioning calculations. The GPS compares the location information with the positioning module to confirm the vehicle's location.

The image recognition module is connected to the mobile device's camera and triggers the camera to capture images, thereby collecting driver image information. The image recognition module connects to the mobile device's camera via a software interface, and the image information captured by the camera is transmitted to the image recognition module for processing. The image recognition module first performs facial detection to locate the face in the image, then extracts facial landmarks. Finally, based on changes in these landmarks, it identifies the driver's facial expressions.

Furthermore, to achieve the aforementioned objectives of the present invention, the present invention provides a method comprising: (S10) the insurance provider inputs and uploads the policy plan contents to a policy database via a terminal device, and a computational analysis module analyzes the policy plan contents in the policy database; (S20) the mobile device is connected to the fixed device, such that the mobile device is electrically connected to a sensor element for information transmission; (S30) the sensor element collects driving behavior data and transmits it to a driving behavior database for storage; (S40) the computational analysis module analyzes the behavior data, and the driving behavior database transmits the behavior data to the computational analysis module for analysis, generating a corresponding behavior score result, which is a score of the driving behavior. The result is a score value and an analysis report detailing the driving habits; (S50) the calculation analysis module compares the behavior score result with the content of the insurance plan, and calculates the insurance plan that meets the behavior score result based on the matching condition formula to form a comparison result; (S60) the calculation analysis module calculates a policy amount The policy amount is dynamically adjusted based on the driver's driving behavior score; and (S70) the calculation and analysis module transmits the comparison result and the policy amount to a recommendation module. The recommendation module generates a new policy plan based on the comparison result and the policy amount, and presents it to the insurance provider via a display interface of the terminal device.

The step (S40) further includes the following steps: (S41) data pre-processing and feature extraction, wherein the computational analysis module cleans, transforms, and synchronizes the collected behavioral data, extracting features from the processed behavioral data; and (S42) establishing a scoring model, wherein the computational analysis module assigns different weights to the extracted features based on their impact on driving behavior, designs a scoring formula, calculates the score for each feature, and performs a comprehensive scoring.

Among them, step (S50) further includes the following steps: (S51) Comparing each clause with the insurance plan, the computational analysis module converts the behavior scoring results into specific driving behavior characteristics. It compares and analyzes each clause in the insurance plan to see if it meets the driver's driving behavior characteristics and needs, and selects corresponding clauses from the insurance plan for combination.

The step (S60) further includes the following steps: (S61) The calculation analysis module establishes a base premium and an adjustment factor, and establishes an adjustment formula to dynamically adjust the policy amount based on the driving behavior score result. The adjustment factor establishes a relationship based on a score range, and the relationship is linear or nonlinear.

The following is a detailed description using specific embodiments and accompanying drawings.

100:Fixed device

110: Bracket housing

111: Front

112:Rear

113: Side

120: Sensing element

121: Accelerometer

122: Gyroscope

123:GPS

124: Image Recognition Module

125: Vehicle Diagnostic Interface Information Collection Module

131, 132: Grip

140: Bracket

141: Connection

150: Fan

170: Connecting components

200: Mobile device

300: Computational Analysis Module

400: Recommended module

500: Terminal device

600: Display interface

AR11: First air flow path

AR12: Second air flow path

AR13: Third air flow path

AR14: Fourth air flow path

D1: Driving behavior database

D2: Policy Database

H: Steering wheel

H1: First vent

H2: Second vent

IS: Installation space

LS: Diversion Space

S: Accommodation space

OH: Open hole

P: Protective protrusion

T1: First through hole

T2: Second through hole

S10~S70: Steps

Figure 1 is a schematic diagram of the system of the present invention; Figure 2 is a schematic diagram of the sensing element of the system of the present invention; Figures 3 to 5 are schematic diagrams of the structure of the fixing device of the present invention; Figure 6 is a schematic diagram of an application example of the system of the present invention; and Figure 7 is a flow chart of the method of the present invention.

The present invention will be described in detail below by illustrating various embodiments of the present invention with reference to the accompanying drawings. However, the concepts of the present invention may be embodied in many different forms and should not be construed as limited to the exemplary embodiments described herein. Furthermore, the same reference numerals may be used to represent similar elements throughout the drawings.

Please refer to Figures 1 and 2 for a schematic diagram of the system of the present invention, which includes a driving behavior database D1, an insurance policy database D2, a mobile device 200, a fixed device 100, a computing and analysis module 300, a recommendation module 400, a terminal device 500, and a display interface 600. The mobile device 200 is mounted on the vehicle's steering wheel H via the fixed device 100, which further includes a sensor 120.

The sensor 120 is electrically connected to the driving behavior database D1, the terminal device 500 is electrically connected to the insurance policy database D2, the computational analysis module 300 is electrically connected to the driving behavior database D1, the insurance policy database D2, and the recommendation module 400, respectively. The recommendation module 400 is electrically connected to the terminal device 500.

The driving behavior database D1 is primarily used to store behavioral data collected via sensors, while the insurance policy database D2 is used to store all insurance policy details uploaded by insurance providers via terminal device 500.

Specifically, the driving behavior database D1 transmits collected and stored behavior data to the computational analysis module 300. Similarly, the insurance policy database D2 transmits collected and stored insurance plan details to the computational analysis module 300. The computational analysis module 300 processes the behavior data and insurance plan details separately. First, it analyzes the behavior data and generates a corresponding behavior score. Next, it compares the behavior score with the insurance plan details to generate a comparison result. Finally, it combines the score and comparison results to calculate the corresponding insurance amount. The comparison result and insurance amount are then transmitted to the recommendation module 400.

Specifically, the recommendation module 400 presents the final recommended new insurance plan and amount via the display interface 600 of the terminal device 500.

Furthermore, one embodiment of the present invention is described as follows:

The insurance provider inputs the details of all its policy plans through the terminal device 500 and uploads them to the policy database D2 for storage. The computational analysis module 300 analyzes the policy plans in the policy database D2 to understand the differences between the plans and the meaning of each clause.

On the other hand, a fixed device 100 is installed on the steering wheel H of the vehicle, and the fixed device 100 includes a sensor 120. When the driver wants to drive the vehicle, he installs the mobile device 200 on the fixed device 100. At this time, the lens of the mobile device 200 is electrically connected to the sensor 120 to transmit information, and when the mobile device 200 is turned on, the sensor 120 is turned on. When device 200 performs navigation, its positioning module electrically connects to the sensor element 120 to transmit information. While the driver is driving the vehicle, the sensor element 120 continuously collects driving behavior data and transmits it to the driving behavior database D1 for storage. Driving behavior database D1 then transmits the behavior data to the calculation and analysis module 300.

Specifically, the computational analysis module 300 analyzes behavioral data to understand the driver's driving habits and generates a corresponding behavioral score. This score can be a numerical value that determines the quality of the driver's driving habits. The behavioral score can also include an analysis report detailing the driver's driving habits, including analyzing the road sections where the driver is prone to overreaction or the driver's driving habits in different weather conditions.

Specifically, the computational analysis module 300 compares the behavior score results with the policy options. Based on a matching condition formula, it calculates the policy option that best matches the behavior score results, forming a comparison result. The policy amount can be simply understood as follows: when a driver's behavior score is poor, it may indicate a higher probability of an accident with the vehicle they drive, resulting in a higher displayed policy amount. This achieves what is known as dynamic adjustment.

Specifically, the comparison results and policy amount generated by the calculation and analysis module 300 are transmitted to the recommendation module 400. Based on the comparison results and the policy amount, the recommendation module 400 compiles a new insurance plan and presents it to the insurance provider's user via the display interface 600 of the terminal device 500. Because the comparison results may match multiple clauses that are not originally included in the same policy due to the calculation of the driving behavior score, the recommendation module 400 first integrates the content and generates a complete and driver-friendly insurance plan before presentation, achieving so-called precise matching and intelligent recommendation.

Specifically, sensor element 120 includes an accelerometer 121, a gyroscope 122, a GPS 123, an image recognition module 124, and an OBD-II interface information collection module 125. The accelerometer 121 primarily measures changes in vehicle acceleration along the three axes, enabling detection of acceleration, deceleration, and braking. Frequent acceleration and deceleration can be identified by the frequency and amplitude of acceleration changes. The gyroscope 122 primarily measures the vehicle's angular velocity, enabling detection of vehicle turns and tilts. Sharp turns can be identified by changes in angular velocity. The GPS 123 is connected to the positioning module of the mobile device 200 and receives location information, records driving trajectory, speed, etc., and combines the data of the accelerometer 121 and the gyroscope 122 to more accurately judge driving behavior, such as driving habits on a specific road section; the image recognition module 124 is connected to the camera of the mobile device 200 and contacts The camera captures the driver's facial expressions and gaze direction, and receives image information to determine driving status, such as fatigue or distracted driving. The vehicle diagnostic interface information collection module 125 reads vehicle diagnostic data, such as engine speed, vehicle speed, and throttle position. This data can provide more comprehensive driving behavior information.

For example, accelerometer 121 measures changes in vehicle acceleration along three mutually perpendicular axes: the X-axis (vehicle's forward direction), the Y-axis (vehicle's lateral direction), and the Z-axis (vehicle's vertical direction). Acceleration is the rate of change of velocity, measured in m/ ^s² . When the vehicle accelerates, acceleration is positive; when it decelerates, acceleration is negative; and when the velocity remains constant, acceleration is zero. Accelerometer 121 contains a sensor element. When the vehicle accelerates or decelerates, the sensor element deforms. Accelerometer 121 converts this deformation into an electrical signal, thereby measuring the acceleration value.

The accelerometer 121 can not only sense the acceleration and deceleration of the vehicle, but also interpret more subtle driving behaviors. When the vehicle accelerates, the acceleration of the X-axis will be positive, and the larger the acceleration value, the faster the acceleration. Conversely, when the vehicle decelerates, the acceleration of the X-axis will be negative, and the larger the absolute value of the acceleration value, the faster the deceleration. Braking is a special case of deceleration, typically accompanied by large negative acceleration values. In addition to acceleration and deceleration, the accelerometer can also sense the vehicle's cornering behavior. When the vehicle turns, the Y-axis acceleration changes. This change can be used to determine the direction and magnitude of the turn. Sharp turns typically result in greater changes in Y-axis acceleration.

From this, it can be inferred that frequent acceleration and deceleration is a common bad driving habit that not only increases fuel consumption but also may increase the risk of accidents. The accelerometer 121 can effectively detect this driving behavior. When the vehicle accelerates and decelerates frequently, the acceleration value will quickly switch between positive and negative, which means that the frequency of acceleration change is high. At the same time, frequent acceleration and deceleration is usually accompanied by a large amplitude of acceleration value change, that is, the degree of acceleration and deceleration is relatively large. Furthermore, by analyzing the data collected by accelerometer 121, a sophisticated algorithm can be designed to calculate the frequency and amplitude of acceleration changes. By setting appropriate thresholds, when the frequency and amplitude of acceleration changes exceed these thresholds, it can be determined whether the driver is frequently accelerating and decelerating.

Gyroscope 122 measures the angular velocity of an object's rotation, that is, the change in the object's angle per unit time, expressed in degrees per second (deg/s) or radians per second (rad/s). Gyroscope 122 contains a high-speed rotor. As gyroscope 122 rotates, it generates a force perpendicular to the direction of rotation. Gyroscope 122 converts this force into an electrical signal, which is then used to measure the angular velocity. Gyroscope 122 typically measures angular velocity along three perpendicular axes: the X-axis (vehicle roll angle), the Y-axis (vehicle pitch angle), and the Z-axis (vehicle yaw angle).

The gyroscope 122 not only senses vehicle turns but also interprets more subtle driving behaviors. When a vehicle turns, the angular velocity of the Z axis changes significantly. This change allows us to determine the direction and magnitude of the turn. Sharp turns typically result in greater changes in the angular velocity of the Z axis. This is similar to how we perceive the lateral force felt when driving a car: the greater the turn, the more pronounced the lateral force.

GPS 123 receives location information from the positioning module of mobile device 200 and records the vehicle's trajectory, speed, and other information. The positioning module of mobile device 200 is actually a GPS, receiving satellite signals and performing positioning calculations. GPS 123 first compares the location information with the positioning module to confirm the vehicle's position and improve accuracy. GPS 123 periodically receives satellite signals to obtain the mobile device's 200 real-time location information (latitude and longitude coordinates). By linking the continuously acquired location information, the vehicle's trajectory is formed. Based on changes in the location information and the time interval, the vehicle's speed can be calculated.

The driving trajectory recorded by location information can be used to determine whether a vehicle is traveling on a specific road section, such as a highway, urban road, or mountain road. Combined with accelerometer and gyroscope data, the driver's driving behavior on that road section can be analyzed, such as speeding, frequent lane changes, and sharp turns. Based on this driving behavior on that road section, the driver's driving habits can be assessed. Subsequently, the differences in the driver's driving behavior on different roads can be analyzed to understand their driving habits.

The image recognition module 124 is connected to the camera of the mobile device 200 and triggers the camera to capture images to collect driver image information. The image recognition module 124 typically connects to the mobile device's camera through a software interface, and the image information captured by the camera is transmitted to the image recognition module 124 for processing. The image recognition module 124 first performs facial detection to locate the face in the image. Next, it extracts facial features, such as the location and shape of the eyes, nose, and mouth. Based on changes in these facial features, it identifies the driver's facial expressions, such as smiling, frowning, and yawning. This allows the module to analyze the driver's gaze and determine whether the driver is focused on the road ahead.

Based on information such as facial expressions (e.g., frequent blinking, yawning) and gaze direction (e.g., closed eyes, blurred vision, eyes away from the road ahead), the image recognition module 124 can determine whether the driver is tired or distracted, which helps facilitate subsequent analysis of driving behavior habits.

OBD-II (On-Board Diagnostics II) is a standardized vehicle diagnostic interface, equipped on nearly all modern vehicles. The vehicle diagnostic interface information collection module 125 uses the OBD-II interface to access various vehicle diagnostic data, such as engine speed, vehicle speed, throttle position, water temperature, battery voltage, and fault codes. The fixed device 100 can be externally connected to the vehicle's OBD-II diagnostic connector, allowing the vehicle diagnostic interface information collection module 125 to access vehicle diagnostic data via the OBD-II interface. For example: engine speed (RPM), vehicle speed, throttle position, coolant temperature, battery voltage, etc. Combining data such as engine speed, vehicle speed, and throttle position allows for a more comprehensive analysis of the driver's driving behavior, such as whether the driver drives smoothly, fuel-efficiently, frequently accelerates, or frequently exceeds the speed limit.

Furthermore, the aforementioned driving behavior data is transmitted to the driving behavior database D1 for storage and then transmitted to the calculation and analysis module 300 for analysis.

First, data preprocessing is performed. Data cleaning: The collected raw data needs to be cleaned to filter out abnormal or erroneous data, such as sensor failures and signal loss. Data conversion: Data in different formats or units is converted into a unified format and units to facilitate subsequent analysis and calculations. Data synchronization: Because data from different sensors may differ in time, data synchronization is required to ensure that all data points are aligned in time.

Next, feature extraction is performed. Acceleration/deceleration frequency and amplitude reflect the frequency and intensity of the driver's acceleration and deceleration; turning frequency and angle reflect the frequency and amplitude of the driver's turns; speed distribution reflects the proportion of time the driver spends driving at different speeds; speeding time proportion reflects the extent of the driver's speeding; fatigue driving indicators include blinking frequency and yawning frequency; and distracted driving indicators include the proportion of time the driver's eyes are away from the road ahead.

Build a scoring model and assign weights: Assign different weights to each feature based on its impact on driving behavior. For example, speeding might be given a higher weight. Develop a scoring formula based on the values and weights of each feature to calculate the score for each feature. Develop an overall score: Take the weighted average of all feature scores to arrive at the final driving behavior score.

Through the aforementioned model building and training, combined with natural language processing and large-scale language models, the computational analysis module 300 is able to accurately analyze and generate corresponding behavioral scoring results. The behavioral scoring results also include text reports analyzing driving habits.

The computational analysis module 300 compares the behavioral scoring results with the policy details in the policy database D2. The computational analysis module 300 first understands all the information (terms and conditions) about the policy details in the policy database D2 before performing the comparison.

Clause-by-Clause Comparison: The comparison process is no longer limited to the plan level, but goes deeper into the clause level, assessing each clause to see if it meets the driver's driving behavior characteristics and needs. Driving Behavior Characteristic Mapping: The driving behavior scoring results are converted into specific driving behavior characteristics, such as high-risk driving: frequent rapid acceleration, sudden braking, and speeding; low-risk driving: smooth driving and compliance with traffic regulations. Clause Screening: Based on driving behavior characteristics and clause preferences, the most suitable clauses are selected from various plans. Plan Combination: The selected clauses are combined to generate a new insurance plan.

In addition, to dynamically adjust the policy amount, the calculation and analysis module 300 can establish a delivery formula logic or mechanism, as illustrated below.

Establish a base premium. Each policy option has a base premium, which is the original premium calculated based on the risk assessment of the insured. Set an adjustment factor. The driving behavior score results are converted into an adjustment factor, which can be a percentage or a numeric value. Establish an adjustment formula. The policy amount can be adjusted using the following formula: Adjusted Premium = Base Premium * (1 + Adjustment Factor). If the adjustment factor is positive, the premium increases; if it is negative, the premium decreases.

The calculation of the adjustment factor can be broken down into score intervals. The driving behavior score is divided into different intervals, with each interval corresponding to an adjustment factor. A linear or nonlinear relationship is established between the behavior score and the adjustment factor. In a linear relationship, the adjustment factor changes linearly with each increase or decrease in the score. In a nonlinear relationship, changes in the score within different intervals have varying degrees of impact on the adjustment factor.

Through the aforementioned computational logic design, the computational analysis module 300 can dynamically adjust the policy amount while accurately matching and recommending insurance contracts.

Specifically, the fixing device 100 of the present invention has the following special structure, which can simultaneously stabilize the mobile device 200 and continuously and stably collect various driving behavior data.

Please refer to Figures 3 to 6. Figure 3 is an exploded perspective view of the fixing device 100.

As shown in FIG3 , the fixing device 100 in this embodiment includes a bracket housing 110 , a sensor element 120 , a pair of handles 131 and 132 , a bracket 140 , and a fan 150 .

The bracket housing 110 has a storage space S inside for accommodating the sensor element 120. The bracket housing 110 can be divided into a front portion 111, a rear portion 112, and a side portion 113 to form the internal storage space S.

The front portion 111 supports the back of the mobile device 200. A first ventilation hole H1 is formed below the front portion 111. Furthermore, a protective protrusion P is formed to prevent the back of the mobile device 200 from coming into contact with any surface.

In addition to the protective protrusion P, the protective device can also adopt various structures.

The rear portion 112 is separated from the front portion 111, with a storage space S therebetween. A second ventilation hole H2 is formed above the rear portion 112.

The side portion 113 is used to form a receiving space S between the front portion 111 and the rear portion 112. It can be integrally injection molded with the front portion 111 or the rear portion 112.

The sensor element 120 is located in the receiving space S within the bracket housing 110 and is used to electrically connect to the mobile device 200 mounted on the front portion 111.

A pair of grips 131 and 132 are horizontally movably connected to the support housing 110 to hold or release the mobile device 200. Of course, the pair of grips 131 and 132 can also be moved automatically or manually.

The bracket 140 supports the bottom of the mobile device 200. The two ends of the mobile device 200 are gripped by grips 131 and 132 at the bottom of the bracket housing 110. An opening OH is formed on the back of the bracket 140. The bracket 140 has a mounting space IS for the fan 150 and a connection portion 141 for connecting to the bracket housing 110.

A guide space LS is formed in the connection portion 141 to guide the air forced to flow by the fan 150.

Furthermore, a first through hole T1 corresponding to the first ventilation hole H1 is formed on the front surface of the connecting portion 141, and a second through hole T2 corresponding to the second ventilation hole H2 is formed on the upper surface. Therefore, the second through hole T2 is formed at a higher position than the first through hole T1.

The fan 150 forces the cooling air to flow through the first ventilation hole H1 and the second ventilation hole H2 and is placed in the installation space IS of the bracket 140.

The connecting member 170 is used to electrically connect the fixing device 100 to the vehicle and is also a mounting component mounted on the steering wheel H.

In one embodiment, the fan 150 operates to draw air from the receiving space S and discharge it through the open hole (OH).

As shown in Figure 4, a first air flow path AR11 and a second air flow path AR12 are formed.

The first air flow path AR11 is the path through which external air passes through the back of the mobile device 200 to cool it, then passes through the first ventilation hole H1, the first through hole T1, the guide space LS, and the installation space IS, and is discharged through the open hole OH.

The second air flow path AR12 is a path where air is drawn into the accommodating space S through the second ventilation hole H2, passes through the sensing element 120 to cool the sensing element 120, and then passes through the second through-hole T2, the guide space LS, and the mounting space IS before being discharged through the open hole OH.

That is, air traveling along the first air flow path AR11 and air traveling along the second air flow path AR12 mix within the air guide space LS before being discharged through the opening OH. Therefore, according to this embodiment, air used to cool the mobile device 200 does not pass through the sensing element 120, and air used to cool the sensing element 120 does not pass through the mobile device 200.

In another embodiment, the fan 150 operates to discharge the air drawn in through the opening OH from the receiving space S.

As shown in Figure 5, a third air flow path AR13 and a fourth air flow path AR14 are formed.

The third air flow path AR13 is the path through which the outside air drawn in through the opening OH passes through the mounting space IS, the first through hole T1, the first ventilation hole H1, and finally passes through the back of the mobile device 200.

The fourth air flow path AR14 is the path through which external air drawn in through the opening OH passes through the area between the second through hole T2 and the second ventilation hole H2, through the sensing element 120, and is discharged through the mounting space IS, the second through hole T2, and the second ventilation hole H2.

In this embodiment, the air moving along the third air flow path AR13 and the air moving along the fourth air flow path AR14 are separated from each other by the first through hole T1 and the second through hole T2 from the flow guiding space LS, thereby preventing the air used to cool the mobile device 200 from flowing through the sensing element 120.

Please refer to Figure 6, which is a schematic diagram of a practical application of the present invention. A fixed device 100 is mounted on a steering wheel H via a connecting member 170, while a mobile device 200 is placed on the fixed device 100. During driving, the sensor 120 collects behavioral data, and the fan 150 dissipates heat, allowing the sensor 120 and mobile device 200 to operate continuously without overheating.

The above-mentioned device and system of the present invention can be implemented through the following method flow.

Refer to Figure 7, step S10, where the insurance provider uploads the policy plan details. The insurance provider inputs the policy plan details via terminal device 500 and uploads them to the policy database D2. The computational analysis module 300 analyzes the policy plan details in policy database D2 to understand the differences between the various plans and the meaning of the terms.

In step S20, the mobile device 200 is connected to the fixed device 100. When the driver is driving the vehicle, the mobile device 200 is mounted on the fixed device 100, electrically connecting the camera of the mobile device 200 to the sensor 120 to transmit information. When the mobile device 200 is performing navigation, its positioning module is electrically connected to the sensor 120 to transmit information.

In step S30, the sensor 120 collects driving behavior data. The sensor 120 continuously collects driving behavior data and transmits it to the driving behavior database D1 for storage. The sensor 120 includes an accelerometer 121, a gyroscope 122, a GPS 123, an image recognition module 124, and a vehicle diagnostic interface information collection module 125.

In step S40, the computational analysis module 300 analyzes the behavioral data. The driving behavior database D1 transmits the behavioral data to the computational analysis module 300. The computational analysis module 300 analyzes the behavioral data, understands the driver's driving habits, and generates a corresponding behavioral score. The behavioral score can be a numerical value or include an analysis report detailing the driver's driving habits.

Step S50: Compare the behavior score results with the insurance plan. The calculation and analysis module 300 compares the behavior score results with the insurance plan content and calculates the insurance plan that best matches the behavior score results based on the matching condition formula to form a comparison result.

Step S60: Calculate the policy amount. The calculation and analysis module 300 calculates the policy amount, which will be dynamically adjusted based on the driving behavior scoring results.

In step S70, the recommendation module 400 presents the insurance plan and the policy amount. The calculation and analysis module 300 transmits the comparison results and the policy amount to the recommendation module 400. Based on the comparison results and the policy amount, the recommendation module 400 compiles the new insurance plan content and presents it to the insurance provider's user through the display interface 600 of the terminal device 500. Before presentation, the recommendation module 400 integrates the content to generate a complete and suitable insurance plan for the driver.

Specifically, step S40 further includes step S41, data preprocessing and feature extraction. The computational analysis module 300 cleans, transforms, and synchronizes the collected raw behavioral data. Features are extracted from the processed data, such as acceleration and deceleration frequency and amplitude, and turning frequency and angle.

Step S42: Establish a scoring model. Based on the impact of each characteristic on driving behavior, set different weights, design a scoring formula, calculate the score for each characteristic, and perform a comprehensive scoring.

Step S50 further includes step S51, where the clauses are compared and combined with the insurance plan. The computational analysis module 300 converts the driving behavior score results into specific driving behavior characteristics. It then compares and analyzes each clause in the insurance plan to see if it meets the driver's driving behavior characteristics and requirements. It then selects corresponding clauses from the various plans and combines them. Finally, the recommendation module 400 generates a new insurance plan.

Step S60 further includes step S61, dynamically adjusting the policy amount. A base premium is established, an adjustment factor is set, and an adjustment formula is established to dynamically adjust the policy amount based on the driving behavior score. The adjustment factor can be established based on the score range, and can be a linear or nonlinear relationship.

Finally, the technical features of the present invention and the technical effects it can achieve are summarized as follows:

First, the intelligent auto insurance precise recommendation and matching and dynamic cost estimation system and method of the present invention accurately collects and analyzes driving behavior data and uses a computational analysis module to recommend and match suitable auto insurance policies.

Second, through the training and design of computational analysis modules, the results of behavioral data analysis can be combined to dynamically adjust premium amounts.

Third, the structure of the fixing device of the present invention enables the sensing element to continuously and stably collect behavioral data.

It must be emphasized that the above detailed description is a specific description of feasible embodiments of the present invention. However, such embodiments are not intended to limit the patent scope of the present invention. Any equivalent implementation or modification that does not deviate from the technical spirit of the present invention should be included in the patent scope of this case.

100:Fixed device

120: Sensing element

200: Mobile device

300: Computational Analysis Module

400: Recommended module

500: Terminal device

600: Display interface

D1: Driving behavior database

D2: Policy Database

H: Steering wheel

Claims (8)

An intelligent car insurance precise recommendation matching and dynamic cost estimation system includes: a mobile device, which is installed on the steering wheel of a vehicle via a fixed device, and a sensor element in the fixed device is electrically connected to the mobile device; a terminal device, which is used to provide an insurance provider with the policy plan content input and upload it to a policy database for storage; a driving behavior database, which receives the behavior data collected from the sensor element; an operation analysis module, which receives the policy plan content from the policy database and the behavior data from the driving behavior database, analyzes and compares them, and generates a comparison result and a policy amount; and a recommendation module, which receives the comparison result from the operation analysis module. The result and the policy amount are integrated to generate a new policy plan, which is presented through a display interface of the terminal device; wherein the calculation analysis module processes the behavior data and the content of the policy plan respectively, first analyzes the behavior data and generates a corresponding behavior scoring result, then analyzes and compares the content of the policy plan based on the behavior scoring result to generate the comparison result, and finally combines the behavior scoring result with the comparison result to calculate the corresponding policy amount, wherein the fixing device further includes a bracket housing, the sensing element, a handle, a bracket and a fan, and further the bracket housing is divided into a front part, a rear part and a side part to form a storage space, and the storage space is used The sensor element is placed on the front portion, and a first ventilation hole and a protective protrusion are provided at the bottom of the front portion, and a second ventilation hole is provided at the top of the rear portion. The lens of the mobile device is electrically connected to the sensor element to transmit information, and when the mobile device performs a navigation function, a positioning module thereof is also electrically connected to the sensor element to transmit information. When a driver is driving a vehicle, the sensor element continuously collects driving behavior data and transmits it to a driving behavior database for storage. The driving behavior database then transmits the behavior data to the calculation analysis module. The sensor element includes a GPS and an image recognition module. The GPS receives the positioning module from the mobile device. The mobile device's positioning module receives satellite signals and performs positioning calculations. The GPS compares the positioning information with the positioning module to confirm the vehicle's position. The image recognition module is connected to the mobile device's lens and triggers the lens to capture images to collect the driver's image information. The image recognition module is connected to the mobile device's lens through a software interface. The image information captured by the lens is transmitted to the image recognition module for processing. The image recognition module first performs facial detection to locate the location of the human face in the image, then extracts facial features. Finally, based on changes in the facial features, it identifies the driver's facial expression. In the intelligent auto insurance precise recommendation matching and dynamic cost estimation system described in claim 1, the behavior scoring result may be a numerical value, and the computational analysis module determines the quality of a driver's driving habits based on the numerical value. The behavior scoring result may also include an analysis report, the computational analysis module generating the content of which using a large language model, detailing the driver's driving habits. The intelligent auto insurance precise recommendation matching and dynamic cost estimation system as described in claim 1, wherein the sensing element includes an accelerometer, a gyroscope, and a vehicle diagnostic interface (OBD-II Interface) information collection module. As described in claim 1, the intelligent car insurance precise recommendation matching and dynamic cost estimation system, wherein the bracket supports the bottom of the mobile device, the two ends of the mobile device are gripped by the handle of the bracket housing, and the back of the bracket has an open hole, the bracket also has an installation space for arranging the fan, and a connecting portion connected to the bracket housing, the connecting portion having a diversion space. As described in claim 4, the intelligent car insurance precise recommendation matching and dynamic cost estimation system, wherein a first through hole corresponding to the first vent is formed on the front surface of the connecting portion, and a second through hole corresponding to the second vent is formed on the upper surface of the connecting portion, and the second through hole is located at a higher position than the first through hole. A method for precise auto insurance recommendation matching and dynamic cost estimation includes the following steps: (S10) an insurance provider inputs and uploads policy plan details to a policy database via a terminal device, and a computational analysis module analyzes the policy plan details in the policy database; (S20) a mobile device is connected to a fixed device, the mobile device is electrically connected to a sensor element for information transmission, a lens of the mobile device is electrically connected to the sensor element for information transmission, and when the mobile device performs a navigation function, a positioning module is electrically connected to the sensor element for information transmission; (S30) The sensor collects driving behavior data and transmits it to a driving behavior database for storage. A GPS of the sensor receives location information from the positioning module of the mobile device and records the driving track and driving speed. The positioning module of the mobile device receives satellite signals and performs positioning calculations. The GPS compares the location information with the positioning module. The vehicle position is confirmed by a signal, an image recognition module of the sensor element is connected to the lens of the mobile device and triggers the lens to take a picture, thereby collecting the image information of the driver. The image recognition module is connected to the lens of the mobile device through a software interface, and the image information captured by the lens is transmitted to the image recognition module for processing; (S40) The computational analysis module analyzes the behavior data, and the driving behavior database transmits the behavior data to the computational analysis module for analysis, generating a corresponding behavior score result, which is a numerical value, and an analysis report detailing the driving habits; (S50) the computational analysis module compares the behavior score result with the content of the insurance plan, and calculates the insurance plan that meets the behavior score result based on the matching condition formula to form a comparison result; (S60) the computational analysis module calculates an insurance policy amount, which is dynamically adjusted based on the driving behavior score result; and (S70) The calculation and analysis module transmits the comparison result and the policy amount to a recommendation module, which generates a new policy plan content based on the comparison result and the policy amount, and presents it to the insurance provider through a display interface of the terminal device; wherein, the step (S50) further includes the following steps: (S51) comparing the terms one by one with the plan combination, the calculation and analysis module converts the behavior scoring result into specific driving behavior characteristics, and compares and analyzes each term of the policy plan content to determine whether it meets the driving behavior characteristics and requirements of the driver, and selects the corresponding terms from the policy plan content for combination. The method for precise recommendation, matching, and dynamic cost estimation of intelligent auto insurance as described in claim 6, wherein step (S40) further includes the following steps: (S41) data preprocessing and feature extraction, wherein the computational analysis module cleans, converts, and synchronizes the collected behavioral data and extracts features from the processed behavioral data; and (S42) establishing a scoring model, wherein the computational analysis module sets different weights based on the degree of influence of the extracted features on driving behavior, designs a scoring formula, calculates a score for each feature, and performs a comprehensive scoring. As described in claim 6, the intelligent auto insurance precise recommendation matching and dynamic cost estimation method, wherein the step (S60) further includes the following steps: (S61) the calculation analysis module establishes a base premium and an adjustment factor, establishes an adjustment formula, and dynamically adjusts the policy amount according to the driving behavior score result, and the adjustment factor establishes a relationship according to a score interval, and the relationship is linear or nonlinear.