A system of intelligent insurance recommendation matching and dynamic cost estimation and the method thereof

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 Auto Insurance Precision Recommendation and Matching and Dynamic Cost Estimation System and Method This invention relates to the field of vehicle insurance systems, and more particularly to a system that uses a special structure combined with a mobile device to collect driving behavior data and analyzes and recommends suitable insurance schemes through an artificial intelligence (AI) module. In current technology, vehicle insurance typically relies on static risk assessment methods, such as the driver's age, gender, driving experience, and past accident records. However, these methods cannot fully reflect the driver's real-time driving behavior and risk level. With technological advancements, more and more insurance companies are exploring the use of in-vehicle devices and smartphones to collect driving behavior data in order to provide more accurate risk assessments and insurance solutions. Traditionally, vehicle insurance policies are determined based on static factors such as the driver's age, gender, location, and vehicle type. While these factors are useful, they do not take into account the dynamic nature of individual driving behavior, which can significantly impact a driver's risk profile. In recent years, the insurance industry has shifted towards usage-based insurance (UBI) models, aiming to make insurance premiums more closely reflect actual driving behavior. These models typically rely on remote information processing devices installed in 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 the steering wheel, and the use of smartphones or sensors for data collection, remains an area with limited exploration. While usage-based insurance models have made progress, existing technologies still present several shortcomings 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. Additionally, the process of adjusting insurance policies based on driving behavior is often manual and time-consuming, requiring intervention from the insurance provider. Systems capable of benchmarking driver performance based on historical data are also needed to provide more accurate recommendations. While existing technologies are capable of collecting and analyzing driving behavior data, several limitations and challenges remain. First, current systems typically cannot update and dynamically adjust insurance amounts in real time, limiting the flexibility and personalization of insurance plans. Second, many systems lack effective data analysis and learning capabilities, failing to fully utilize the collected data to provide accurate insurance recommendations. The purpose of this invention is to propose an intelligent auto insurance accurate recommendation and matching and dynamic cost estimation system, which solves the problems existing in the prior art. Therefore, in order to achieve the above-mentioned objective of the present invention, the present invention provides a system comprising: a mobile device mounted on the steering wheel of a vehicle by a fixing device, wherein a sensing element in the fixing device is electrically connected to the mobile device; A terminal device is used to allow insurance providers to input policy details and upload them to a policy database for storage; a driving behavior database receives behavioral data collected from the sensing element; a computing and analysis module receives the policy details from the policy database and the behavioral data from the driving behavior database, analyzes and compares them, and generates a comparison result and a policy amount; and a recommendation module receives the comparison result and the policy amount from the computing and analysis module, integrates them to generate a new policy, and presents it through a display interface of the terminal device; wherein, the computing and analysis module processes the behavioral data and the policy details respectively, first analyzing the behavioral data and generating a corresponding behavioral score result, then analyzing and comparing the policy details based on the behavioral score result to generate the comparison result, and finally combining the behavioral score result and the comparison result to calculate the corresponding policy amount. The sensing element includes an accelerometer, a gyroscope, a GPS, an image recognition module, and a vehicle diagnostic interface (OBD-II Interface) information collection module. The GPS receives location information from the positioning module of the mobile device and records the driving trajectory and speed. The positioning module of the mobile device receives satellite signals and performs positioning calculations. The GPS compares the location information with that of the positioning module to confirm the vehicle's location. The image recognition module is connected to the camera of the mobile device and triggers the camera to take pictures, thereby collecting video information about the driver. The image recognition module is connected to the camera of the mobile device through a software interface, and the video information captured by the camera is transmitted to the image recognition module for processing. The image recognition module first performs face detection to find the location of the face in the image, then extracts facial feature points, and finally recognizes the driver's facial expressions based on changes in facial feature points. Furthermore, to achieve the aforementioned objective of this invention, the present invention provides a method comprising: (S10) an insurance provider inputting and uploading policy details to a policy database via a terminal device, and a computational analysis module analyzing the policy details in the policy database; (S20) a mobile device connecting to a fixed device, thereby electrically connecting the mobile device to a sensing element for information transmission; (S30) the sensing element collecting driving behavior data and transmitting it to a driving behavior database for storage; (S40) the computational analysis module analyzing the behavior data, and the driving behavior database transmitting the behavior data to the computational analysis module for analysis, generating a corresponding behavior score result, which is a numerical score, and an analysis report detailing the driver's driving habits; (S50) the computational analysis module processing the behavior score... The system compares the score results with the policy plan content and calculates the policy plan that matches the behavior score results according to the matching condition formula, forming a comparison result; (S60) the calculation and analysis module calculates a policy amount, which will be dynamically adjusted according to the driving behavior score results; and (S70) the calculation and analysis module transmits the comparison result and the policy amount to a recommendation module, which combines the comparison result and the policy amount to generate a new policy plan content and presents it to the insurance provider through a display interface of the terminal device. The step (S40) further includes the following steps: (S41) data preprocessing and feature extraction, in which the computational analysis module cleans, transforms and synchronizes the collected behavioral data, and extracts features from the processed behavioral data; and (S42) establishing a scoring model, in which 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 the score of each feature, and performs a comprehensive score. In this step (S50), the following steps are further included: (S51) the clauses are compared and combined with the scheme one by one. The calculation and analysis module converts the behavior score result into specific driving behavior characteristics. For each clause of the policy scheme, the module compares and analyzes whether it meets the driving behavior characteristics and needs, and selects the corresponding clauses from the policy scheme for combination. In step (S60), the following steps are further included: (S61) The calculation and analysis module sets a benchmark premium and an adjustment factor, establishes an adjustment formula, and dynamically adjusts the policy amount according to the driving behavior score result. The adjustment factor establishes a relationship based on a score interval, and the relationship is linear or non-linear. The following is a detailed description using specific embodiments and accompanying drawings. 100: Fixture 110: Support 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: Connecting part 150: Fan 170: Connecting component 200: Mobile Device 300: Computational Analysis Module 400: Recommended Module 500: Terminal device 600: Display Interface AR11: First airflow path AR12: Second airflow path AR13: Third Airflow Path AR14: Fourth Airflow Path D1: Driving Behavior Database D2: Policy Database H: Steering wheel H1: First ventilation hole H2: Second ventilation hole IS: Installation space LS: Guide Space S: Accommodation space OH: Open pore 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 flowchart of the method of the present invention. The invention will be described in detail below by way of the drawings illustrating various embodiments thereof. However, the inventive concept may be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Furthermore, the same reference numerals in the drawings may be used to denote similar elements. Please refer to Figures 1 and 2 simultaneously. The system schematic diagram of the present invention 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 steering wheel H of the vehicle via the fixed device 100, wherein the fixed device 100 further includes a sensing element 120. The sensing element 120 is electrically connected to the driving behavior database D1, the terminal device 500 is electrically connected to the policy database D2, the calculation and analysis module 300 is electrically connected to the driving behavior database D1, the policy database D2, and the recommendation module 400, and the recommendation module 400 is electrically connected to the terminal device 500. The driving behavior database D1 is mainly used to store behavioral data collected by sensing elements, while the policy database D2 is used to store all policy information uploaded by insurance providers through terminal device 500. Specifically, the driving behavior database D1 transmits the collected and stored behavioral data to the calculation and analysis module 300. Similarly, the policy database D2 also transmits the collected and stored policy plan content to the calculation and analysis module 300. The calculation and analysis module 300 will process the behavioral data and policy plan content separately. First, it analyzes the behavioral data and generates corresponding behavioral scores. Then, it analyzes and compares the policy plan content based on the behavioral scores to generate comparison results. Finally, it combines the scores and comparison results to calculate the corresponding policy amount and then transmits the comparison results and policy amount to the recommendation module 400. Specifically, the recommendation module 400 presents the final recommended new insurance policy plan and amount through the display interface 600 of the terminal device 500. Furthermore, one embodiment of the present invention is described below: The insurance provider inputs all the policy details it owns into the policy database D2 via the terminal device 500. The calculation and analysis module 300 analyzes the policy details in the policy database D2 to understand the differences between the plans and the meaning of each clause. On the other hand, a fixing device 100 is installed on the steering wheel H in the vehicle, and the fixing device 100 includes a sensing element 120. When the driver wants to drive the vehicle, the mobile device 200 is installed on the fixing device 100. At this time, the lens of the mobile device 200 is electrically connected to the sensing element 120 to transmit information. When the mobile device 200 performs navigation, its positioning module is electrically connected to the sensing element 120 to transmit information. During the driving process, the sensing element 120 continuously collects driving behavior data and transmits it to the driving behavior database D1 for storage. The driving behavior database D1 then transmits the behavior data to the computing and analysis module 300. Specifically, the computing and analysis module 300 analyzes behavioral data to understand the driver's driving habits and provides corresponding behavioral scores. These scores can be numerical values, and the quality of the driver's driving habits can be judged by the numerical values. The behavioral scores can also include an analysis report, which details the driver's driving habits and can even analyze which road sections the driver is prone to overreacting or what different driving habits the driver has in different weather conditions. Specifically, the calculation and analysis module 300 compares the behavior score results with the policy plan content, calculates the policy plan that best matches the behavior score result based on the matching condition formula, and calculates the policy amount. The policy amount can be simply understood as follows: when the driving behavior score result is poor, it may mean that the vehicle being driven has a higher probability of being involved in an accident, and the displayed policy amount is therefore higher, thus achieving the so-called dynamic adjustment. Specifically, the comparison results and policy amount generated by the calculation and analysis module 300 are transmitted to the recommendation module 400. The recommendation module 400 integrates the comparison results and policy amount into a new policy plan and presents it to the insurance provider's personnel through the display interface 600 of the terminal device 500. Since the comparison results may match multiple clauses that are not originally in the same policy plan based on the driver's behavior score, the recommendation module 400 integrates the content and generates a complete and suitable policy plan before presentation, achieving so-called accurate matching and intelligent recommendation. Furthermore, the sensing element 120 includes an accelerometer 121, a gyroscope 122, a GPS 123, an image recognition module 124, and a vehicle diagnostic interface (OBD-II Interface) information collection module 125. Accelerometer 121 is mainly used to measure the acceleration changes of the vehicle in three axes, and can detect acceleration, deceleration, braking and other actions. Frequent acceleration and deceleration behavior can be judged by the frequency and amplitude of acceleration changes. Gyroscope 122 is mainly used to measure the angular velocity of the vehicle, and can detect the turning, tilting and other actions of the vehicle. Sharp turning behavior can be judged by the change in angular velocity. GPS 123 is interconnected with the positioning module of mobile device 200 and receives location information, records driving trajectory, speed, etc. Combining the data of accelerometer 121 and gyroscope 122, driving behavior can be judged more accurately, such as driving habits on specific road sections. Image recognition module 124 is interconnected with the lens of mobile device 200 and triggers the lens to take pictures, capture the driver's facial expression information and gaze direction and receive image information to judge the driving status, such as whether the driver is fatigued or distracted. Vehicle diagnostic interface information collection module 125 reads vehicle diagnostic data, such as engine speed, vehicle speed, throttle opening, etc., which can provide more comprehensive driving behavior information. For example, accelerometer 121 measures the change of acceleration of a vehicle 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. 2 When the vehicle accelerates, the acceleration is positive; when it decelerates, the acceleration is negative; and when the speed remains constant, the acceleration is zero. The accelerometer 121 contains a sensitive element that deforms when the vehicle accelerates or decelerates. The 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 a vehicle, but also interpret more subtle driving behaviors. When the vehicle accelerates, the acceleration on the X-axis will be positive, and the larger the acceleration value, the faster the acceleration. Conversely, when the vehicle decelerates, the acceleration on 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, which is usually accompanied by a large negative acceleration value. In addition to acceleration and deceleration, the accelerometer can also sense the vehicle's turning behavior. When the vehicle turns, the acceleration on the Y-axis will change. Through this change, the direction and magnitude of the turn can be determined. If it is a sharp turn, the change in acceleration on the Y-axis will usually be greater. This suggests that frequent acceleration and deceleration is a common bad driving habit. It not only increases fuel consumption but may also increase the risk of accidents. Accelerometer 121 can effectively detect this driving behavior. When a vehicle frequently accelerates and decelerates, the acceleration value switches rapidly between positive and negative values, indicating a high frequency of acceleration changes. Furthermore, frequent acceleration and deceleration are usually accompanied by large amplitude changes in acceleration values, meaning both acceleration and deceleration are relatively large. Further, by analyzing the data collected by accelerometer 121, a sophisticated algorithm can be designed to calculate the frequency and amplitude of acceleration changes. After setting appropriate thresholds, when the frequency and amplitude of acceleration changes exceed these thresholds, it can be determined whether the driver is engaging in frequent acceleration and deceleration behavior. The gyroscope 122 measures the angular velocity of an object's rotation, which is the change in angle of rotation of the object per unit time, measured in degrees per second (deg / s) or radians per second (rad / s). Inside the gyroscope 122 is a high-speed rotating rotor. When the gyroscope 122 itself rotates, the rotor generates a force perpendicular to the direction of rotation. The gyroscope 122 converts this force into an electrical signal, thereby measuring the angular velocity value. The gyroscope 122 typically measures the angular velocity along three mutually perpendicular axes: the X-axis (vehicle roll angle), the Y-axis (vehicle pitch angle), and the Z-axis (vehicle yaw angle). The gyroscope 122 can not only sense the vehicle's turning, but also interpret more subtle driving behaviors. When the vehicle turns, the angular velocity of the Z-axis will change significantly. Through this change, we can judge the direction and magnitude of the turn. If it is a sharp turn, the change in the angular velocity of the Z-axis will usually be greater. Just like when we drive and turn in our daily lives, the larger the turning angle, the more obvious the lateral force felt by the body. GPS 123 receives location information from the positioning module of mobile device 200 and records driving trajectory, speed, etc. The positioning module of mobile device 200 is also 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 real-time location information (latitude and longitude coordinates) of mobile device 200. By connecting the continuously obtained location information, the vehicle's driving trajectory can be formed. Based on the changes in location information and the time interval, the vehicle's speed can be calculated. By recording driving trajectories through location information, it is possible to determine whether a vehicle is traveling on a specific road segment, such as highways, urban roads, or mountain roads. By combining data from accelerometers and gyroscopes, the driving behavior of the driver on that specific road segment can be analyzed, such as whether the driver is speeding, frequently changing lanes, or making sharp turns. Based on the driving behavior on that specific road segment, the quality of the driver's driving habits can be assessed. Subsequently, the differences in the driver's driving behavior on different road segments can be analyzed to understand the characteristics of their driving habits. The image recognition module 124 is interconnected with the camera of the mobile device 200 and triggers the camera to take pictures to collect driving image information. The image recognition module 124 is usually connected to the camera of the mobile device through a software interface. 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 find the position of the face in the image. Then, it extracts facial feature points, such as the position and shape of the eyes, nose, and mouth. Then, based on the changes in facial feature points, it recognizes the driver's facial expressions, such as smiling, frowning, and yawning. It can also analyze the driver's gaze direction to 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, gaze away from the road ahead), the image recognition module 124 can determine whether the driver is fatigued or distracted, which helps in subsequent analysis of driving behavior habits. OBD-II (On-Board Diagnostics II) is a standardized vehicle diagnostic interface, equipped in almost all modern automobiles. The vehicle diagnostic interface information collection module 125 can read various diagnostic data of the vehicle through the OBD-II interface, such as engine speed, vehicle speed, throttle position, coolant temperature, battery voltage, and fault codes. The mounting device 100 can be externally connected to the vehicle's OBD-II diagnostic connector, allowing the vehicle diagnostic interface information collection module 125 to read vehicle diagnostic data through the OBD-II interface. Examples include engine speed (RPM), vehicle speed, throttle position, coolant temperature, and battery voltage. 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 driving is smooth, fuel-efficient, prone to rapid acceleration, or frequently speeding. Furthermore, all of the aforementioned driving behavior data are transmitted to the driving behavior database D1 for storage, and then transmitted to the computing and analysis module 300 for analysis. First, data preprocessing is performed, including data cleaning: the collected raw data needs to be cleaned to filter out abnormal or erroneous data, such as sensor malfunctions or signal loss; data conversion: data of different formats or units is converted into a unified format and unit to facilitate subsequent analysis and calculation; data synchronization: since the 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, including acceleration / deceleration frequency and magnitude: reflecting the frequency and intensity of the driver's acceleration and deceleration; turning frequency and angle: reflecting the frequency and magnitude of the driver's turns; vehicle speed distribution: reflecting the proportion of time the driver spends at different speeds; speeding time proportion: reflecting the degree of speeding by the driver; fatigue driving indicators: such as blinking frequency, yawning frequency, etc.; and distracted driving indicators: such as the proportion of time the driver's eyes are off the road. Establish a scoring model and set weights: Assign different weights to each feature based on its influence on driving behavior. For example, speeding might be given a higher weight. Scoring formula: Design a scoring formula based on the numerical value and weight of each feature to calculate the score for each feature. Overall score: Calculate the final driving behavior score by weighted averaging of the scores for each feature. Through the establishment and training of the above models, and by incorporating natural language processing and large-scale language models, the computational analysis module 300 can accurately analyze and generate corresponding behavioral scoring results, which also include text reports analyzing driving habits. The computational analysis module 300 compares and analyzes the behavioral scoring results with the policy plan content in the policy database D2. The computational analysis module 300 first understands all the information (term details) of the policy plan content in the policy database D2, and then performs the comparison. Clause-by-clause comparison: The comparison process is no longer limited to the scheme level, but delves into the clause level, evaluating whether each clause matches the characteristics and needs of driving behavior; Driving behavior characteristic matching: The driving behavior score results are converted into specific driving behavior characteristics, such as: high-risk driving: frequent rapid acceleration, sudden braking, speeding, low-risk driving: smooth driving, compliance with traffic rules, etc.; Clause selection: Based on driving behavior characteristics and clause preferences, the most suitable clauses are selected from various schemes; Scheme combination: The selected clauses are combined to generate a brand new insurance scheme. In addition, the dynamic adjustment of the policy amount can be achieved by setting up a delivery formula logic or mechanism in the calculation and analysis module 300, as illustrated below. A base premium is established for each policy plan. This base premium is the original premium calculated based on the risk assessment of the insured object. An adjustment factor is set; the driving behavior score is converted into an adjustment factor, which can be a percentage or a numerical value. An adjustment formula is established; 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 further divided into scoring intervals. Driving behavior scores are divided into different intervals, each corresponding to an adjustment factor, and a relationship is established—a linear or non-linear relationship. The relationship between the behavior score result and the adjustment factor can be linear or non-linear. In a linear relationship, the adjustment factor changes linearly with each increase or decrease in the score result by a certain value. In a non-linear relationship, the impact of changes in the score result on the adjustment factor varies across different intervals. Through the above-mentioned calculation logic design, the calculation and analysis module 300 can dynamically adjust the policy amount and accurately match and recommend policy 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 Figure 3, the fixing device 100 in this embodiment includes a bracket housing 110, a sensing element 120, a pair of grips 131 and 132, a bracket 140, and a fan 150. The bracket housing 110 has an internal receiving space S for accommodating the sensing element 120. The bracket housing 110 can be divided into a front part 111, a rear part 112, and a side part 113 to form the internal receiving space S. The front part 111 supports the back of the mobile device 200, and a first ventilation hole H1 is formed below the front part 111. In addition, a protective protrusion P is formed as a protective means to prevent the back of the mobile device 200 from contacting the surface. Besides the form of a protective protrusion P, protective devices can also adopt various structures. The rear part 112 is separated from the front part 111, and there is a receiving space S between them. A second ventilation hole H2 is formed above the rear part 112. The side portion 113 is used to form a receiving space S between the front portion 111 and the rear portion 112, and can be integrally injection molded with the front portion 111 or integrally injection molded with the rear portion 112. The sensing 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 111. A pair of grips 131, 132 are horizontally movably connected to the support housing 110 to grip or release the motion device 200. Of course, the pair of grips 131, 132 can also be moved automatically or manually. The bracket 140 supports the bottom of the mobile device 200. Both ends of the mobile device 200 are held by the grips 131 and 132 at the bottom of the bracket housing 110. An open hole OH is formed on the back of the bracket 140. The bracket 140 has a mounting space IS for mounting the fan 150 and a connecting part 141 connected to the bracket housing 110. A flow guide space LS is formed in the connection part 141 to guide the air forced to flow by the fan 150 to move therein. Furthermore, a first through hole T1 corresponding to the first vent H1 is formed on the front surface of the connecting portion 141, and a second through hole T2 corresponding to the second vent 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 airflow to make cooling air flow through the first vent H1 and the second vent H2, and place it in the mounting space IS of the bracket 140. The connecting member 170 is a component for electrically connecting the fixing device 100 to the vehicle, and is also a mounting component installed on the steering wheel H. In one embodiment, the fan 150 operates to draw in air from the housing space S and discharge it through the opening (OH). As shown in Figure 4, a first airflow path AR11 and a second airflow path AR12 are formed. The first airflow path AR11 is a path through which external air passes over the back of the mobile device 200 to cool the mobile device 200, then passes through the first ventilation hole H1, the first through hole T1, the guide space LS and the mounting space IS and is discharged through the open hole OH. The second airflow path AR12 is a path that draws in air from the receiving space S through the second ventilation hole H2, passes it through the sensing element 120 to cool the sensing element 120, and then exits through the second through hole T2, the guide space LS and the mounting space IS and through the open hole OH. That is, the air moving along the first airflow path AR11 and the air moving along the second airflow path AR12 are mixed in the guide space LS and then discharged through the opening OH. Therefore, according to this embodiment, the air used to cool the mobile device 200 will not pass through the sensing element 120, and the air used to cool the sensing element 120 will not pass through the mobile device 200. In another embodiment, fan 150 operates to exhaust air drawn in through opening OH from containment space S. As shown in Figure 5, a third airflow path AR13 and a fourth airflow path AR14 are formed. The third airflow path AR13 is the path through which external air drawn in via the open hole OH passes through the installation space IS, the first through hole T1, the first ventilation hole H1, and then through the back of the mobile device 200. The fourth airflow path AR14 is the path through which external air drawn in through the open hole OH passes through the area between the second through hole T2 and the second ventilation hole H2, and is simultaneously discharged through the sensing element 120 via the mounting space IS, the second through hole T2, and the second ventilation hole H2. In this embodiment, since 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 from the guide space LS through the first through hole T1 and the second through hole T2, the air used to cool the mobile device 200 is prevented from flowing through the sensing element 120. Please refer to Figure 6, which is a schematic diagram of the actual application of the present invention. The fixed device 100 is mounted on the steering wheel H by the connecting member 170, and the mobile device 200 is placed on the fixed device 100. During driving, behavioral data is collected through the sensing element 120, and heat is dissipated through the fan 150, so that the sensing element 120 and the mobile device 200 can continue to operate without overheating. The apparatus and system of the present invention described above can be implemented through the following method process. Please refer to Figure 7, step S10, where the insurance provider uploads the policy plan details. The insurance provider inputs and uploads the policy plan details to the policy database D2 via terminal device 500. The calculation and analysis module 300 analyzes the policy plan details in the policy database D2 to understand the differences between the plans and the meaning of the terms. In step S20, the mobile device 200 is connected to the fixed device 100. When driving a vehicle, the mobile device 200 is mounted on the fixed device 100, and the lens of the mobile device 200 is electrically connected to the sensing element 120 to transmit information. When the mobile device 200 performs navigation functions, its positioning module is electrically connected to the sensing element 120 to transmit information. In step S30, the sensing element 120 collects driving behavior data. The sensing element 120 continuously collects driving behavior data and transmits it to the driving behavior database D1 for storage. The sensing element 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. 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 provides corresponding behavioral scores. The behavioral score can be a numerical score or an analysis report detailing the driver's driving habits. Step S50: Compare the behavioral scoring results with the policy plan. The calculation and analysis module 300 compares the behavioral scoring results with the policy plan content, calculates the policy plan that best matches the behavioral scoring results based on the matching condition formula, and forms the 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 score results. In step S70, the recommendation module 400 presents the policy plan and amount. The calculation and analysis module 300 transmits the comparison results and policy amount to the recommendation module 400. The recommendation module 400 compiles the comparison results and policy amount into a new policy plan and presents it to the insurance provider's personnel 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 policy plan for driving. 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 / deceleration frequency and amplitude, turning frequency and angle, etc. Step S42: Establish a scoring model. Based on the degree of influence of each feature on driving behavior, set different weights, design a scoring formula, calculate the score of each feature, and perform a comprehensive score. Step S50 further includes step S51, where each clause is compared and combined with the solution. The calculation and analysis module 300 transforms the driving behavior score into specific driving behavior characteristics. For each clause in the policy solution, it compares and analyzes whether it conforms to the driving behavior characteristics and needs, and selects the corresponding clauses from each solution for combination. Finally, the recommendation module 400 generates a brand new insurance solution. Step S60 further includes step S61, which dynamically adjusts the policy amount. A base premium is established, adjustment factors are set, an adjustment formula is created, and the policy amount is dynamically adjusted based on the driving behavior score results. The adjustment factors can be related to the score range, and this relationship can be linear or non-linear. Finally, the technical features of this invention and the technical effects it can achieve are summarized as follows: Firstly, the intelligent car insurance accurate recommendation and matching and dynamic cost estimation system and method of the present invention accurately collects and analyzes driving behavior data, and recommends and matches suitable car insurance policies through the calculation and analysis module. Secondly, by training and designing the computational analysis module, the premium amount is dynamically adjusted in conjunction with the results of behavioral data analysis. Thirdly, 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, the embodiments are not intended to limit the patent scope of the present invention. All equivalent implementations or modifications that do not depart from the spirit of the present invention should be included in the patent scope of this case. 100: Fixture 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

1. A smart auto insurance accurate recommendation and matching and dynamic cost estimation system includes: A mobile device is mounted on the steering wheel of a vehicle via a fixing device, wherein a sensing element in the fixing device is electrically connected to the mobile device; a terminal device is used to provide an insurance provider with input of policy details and upload them to a policy database for storage; a driving behavior database receives behavioral data collected from the sensing element; and a computing and analysis module receives the policy details from the policy database and the behavioral data from the driving behavior database, analyzes and compares them, and generates a comparison result and a policy amount. The system also includes a recommendation module that receives the comparison result and the policy amount from the computing and analysis module, integrates them to generate a new policy plan, and presents it through a display interface of the terminal device. The computing and analysis module processes the behavioral data and the policy plan content. First, it analyzes the behavioral data and generates a corresponding behavioral score. Then, it analyzes and compares the policy plan content based on the behavioral score to generate a comparison result. Finally, it combines the behavioral score and the comparison result to calculate the corresponding policy amount. The mounting device further includes a bracket housing, the sensing element, a grip, a bracket, and a fan. The bracket housing is further divided into a front, a rear, and a side portion, forming a receiving space for housing the sensing element. A first ventilation hole and a protective protrusion are provided at the lower part of the front portion, and a second ventilation hole is provided at the upper part of the rear portion. The camera of the mobile device is electrically connected to the sensing element to transmit information. When the mobile device performs navigation, its positioning module also connects to the sensing element. The sensing element is electrically connected to transmit information. When a driver is driving a vehicle, the sensing 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 computing and analysis module. The sensing element includes a GPS and an image recognition module. The GPS receives location information from the positioning module of the mobile device and records the driving trajectory and 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 to confirm the vehicle's position. The image recognition module is interconnected with the camera of the mobile device and triggers the camera to take pictures to collect the driver's image information. The image recognition module is connected to the camera of the mobile device through a software interface. The image information captured by the camera is transmitted to the image recognition module for processing. The image recognition module first performs face detection to find the position of the face in the image, then extracts facial feature points, and finally recognizes the driver's facial expression based on the changes in facial feature points. The intelligent auto insurance accurate recommendation and matching and dynamic cost estimation system described in request item 1, wherein, The behavior score can be a numerical value. The analysis module uses the numerical value to determine the quality of a driver's driving habits. The behavior score can also include an analysis report, which is generated by the analysis module using a large language model to describe the driver's driving habits in detail. The intelligent auto insurance accurate recommendation and matching and dynamic cost estimation system described in request item 1, wherein, The sensing element includes an accelerometer, a gyroscope, and a vehicle diagnostic interface (OBD-II Interface) information collection module. The intelligent auto insurance accurate recommendation and matching and dynamic cost estimation system described in request item 1, wherein, The bracket supports the bottom of the mobile device, and the two ends of the mobile device are held by the handle of the bracket housing. The back of the bracket has an open hole, and the bracket also has a mounting space for the fan and a connecting part connected to the bracket housing, which has a flow guiding space. The intelligent auto insurance accurate recommendation and matching and dynamic cost estimation system described in request item 4, wherein... A first through hole corresponding to the first ventilation hole is formed on the front surface of the connector, and a second through hole corresponding to the second ventilation hole is formed on the upper surface of the connector. The second through hole is located at a higher position than the first through hole. A method for intelligent auto insurance accurate recommendation matching and dynamic cost estimation includes the following steps: (S10) An insurance provider inputs and uploads policy details to a policy database via a terminal device, and a computing and analysis module analyzes the policy details in the policy database; (S20) A mobile device is connected to a fixed device, and the mobile device is electrically connected to a sensing element to transmit information, and a camera of the mobile device is electrically connected to the sensing element to transmit information. Furthermore, when the mobile device is performing navigation, a positioning module is electrically connected to the sensing element to transmit information; (S30) The sensing element collects driving behavior data and transmits it to a driving behavior database for storage. A GPS unit of the sensing element receives location information from the positioning module of the mobile device and records the driving trajectory and speed. The positioning module of the mobile device receives satellite signals and performs positioning calculations. The GPS unit compares the location information with the positioning module to confirm the vehicle's position. An image recognition module of the sensing element is connected to the camera of the mobile device and triggers the camera to take pictures, thereby collecting image information of the driver. The image recognition module is connected to the camera of the mobile device through a software interface, and the image information captured by the camera is transmitted to the image recognition module for processing; (S40) The calculation and analysis module analyzes the behavior data. The driving behavior database transmits the behavior data to the calculation and analysis module for analysis and generates a corresponding behavior score result, which is a numerical score and an analysis report detailing the driver's driving habits; (S50) The calculation and analysis module compares the behavior score result with the policy plan content, and calculates the policy plan that matches the behavior score result according to the matching condition formula, forming a comparison result; (S60) The calculation and analysis module calculates a policy amount, which is dynamically adjusted according to 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 aggregates the comparison result and the policy amount to generate a new policy plan content, and presents it to the insurance provider through a display interface of the terminal device; wherein, This step (S50) further includes the following steps: (S51) The clauses are compared and combined with the scheme one by one. The calculation and analysis module converts the behavior score result into specific driving behavior characteristics. For each clause of the policy scheme, it compares and analyzes whether it meets the driving behavior characteristics and needs, and selects the corresponding clauses from the policy scheme for combination. The intelligent auto insurance precise recommendation and matching and dynamic cost estimation method described in request item 6, wherein... This step (S40) further includes the following steps: (S41) Data preprocessing and feature extraction: The computational analysis module cleans, transforms and synchronizes the collected behavioral data, and extracts features from the processed behavioral data. And (S42) Establish a scoring model. The calculation and analysis module sets different weights based on the degree of influence of the extracted feature on driving behavior, designs a scoring formula, calculates the score of each feature, and performs a comprehensive score. The intelligent auto insurance precise recommendation and matching and dynamic cost estimation method described in request item 6, wherein... This step (S60) further includes the following steps: (S61) The calculation and analysis module establishes a benchmark premium and an adjustment factor, establishes an adjustment formula, and dynamically adjusts the policy amount based on the driving behavior score result. The adjustment factor establishes a relationship based on a score interval, and the relationship is linear or non-linear.

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