Insurance intelligent risk control system

Abstract

The invention discloses an insurance intelligent risk control system, and relates to the technical field of risk control, and the system comprises a data collection module which is used for collecting corresponding vehicle operation data and external environment data in the driving process of a target vehicle; the data analysis module is used for analyzing the vehicle operation data corresponding to the target vehicle in the driving process, generating a driving behavior evaluation coefficient corresponding to the target vehicle based on an analysis result, and processing the external environment data corresponding to the target vehicle to obtain a driving behavior evaluation result; determining an environment risk assessment coefficient corresponding to the target vehicle according to the processing result; the insurance adjustment module is used for generating a comprehensive risk assessment coefficient according to the driving behavior assessment coefficient and the environmental risk assessment coefficient, and determining a rate adjustment strategy in combination with a pricing model preset by an insurance company; and the strategy execution module is used for responding to the rate adjustment strategy and dynamically adjusting the rate corresponding to the insurance of the target vehicle. The method and the device have the effect of improving the risk assessment accuracy.

Description

Technical Field

[0001] The present application relates to the field of risk control technology, and in particular to an insurance intelligent risk control system. Background Art

[0002] With the rapid growth of car ownership, the vehicle insurance market has also developed rapidly. Traditional vehicle insurance mainly relies on static risk assessment models, which are usually based on the owner's historical claims record, basic vehicle information (such as model, age, etc.), and the driver's age, gender and other factors. Insurance companies use these static variables to determine insurance premiums and make claims after an accident. However, with the advancement of technology and the improvement of data analysis capabilities, the traditional static pricing and risk control model has gradually shown its limitations and is difficult to adapt to the complex and changing traffic and driving environment in modern society.

[0003] In recent years, with the rapid development of technologies such as the Internet of Things (IoT), big data, and artificial intelligence (AI), the vehicle insurance industry has begun to explore new intelligent risk control systems. By installing smart devices (such as on-board GPS, sensors, cameras, etc.) on vehicles, insurance companies can obtain real-time vehicle operation data, driver behavior data, and external environment data. These data can provide insurance companies with more comprehensive and dynamic risk assessment capabilities, helping companies to more accurately predict the probability of accidents and adjust premiums based on real-time risks. At the same time, insurance companies can also analyze drivers' driving habits and provide them with personalized risk warnings and suggestions, thereby reducing the probability of accidents.

[0004] In related technologies, existing insurance intelligent risk control systems find it difficult to achieve efficient integration of multi-source data, which affects the accuracy of risk assessment and there is room for improvement. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present application provides an insurance intelligent risk control system.

[0006] In the first aspect, the present application provides an insurance intelligent risk control system, which adopts the following technical solutions: An insurance intelligent risk control system, comprising: A data acquisition module is used to collect vehicle operation data and external environment data corresponding to the target vehicle during driving; A data analysis module is used to analyze the vehicle operation data corresponding to the target vehicle during driving, and generate a driving behavior assessment coefficient corresponding to the target vehicle based on the analysis results, and process the external environment data corresponding to the target vehicle, and confirm the environmental risk assessment coefficient corresponding to the target vehicle according to the processing results; The insurance adjustment module is used to generate a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and to confirm the rate adjustment strategy in combination with the pricing model preset by the insurance company; The policy execution module is used to respond to the rate adjustment policy and dynamically adjust the rate corresponding to the target vehicle insurance.

[0007] Preferably, the vehicle operation data corresponding to the target vehicle during driving includes sudden acceleration data, sudden braking data, speeding data, sharp turn data, fatigue driving data and distracted driving data; The external environment data corresponding to the target vehicle during driving includes weather data, road condition data, traffic flow data and time data.

[0008] Preferably, the vehicle operation data corresponding to the target vehicle during driving is analyzed, and a driving behavior evaluation coefficient corresponding to the target vehicle is generated based on the analysis result, specifically including: Obtain the vehicle operation data corresponding to the target vehicle during driving, and extract the sudden acceleration data, sudden braking data, speeding data, sharp turn data, fatigue driving data and distracted driving data from the vehicle operation data, and number the vehicle operation data to obtain the actual value corresponding to the vehicle operation data ,in, Indicates the number corresponding to each vehicle operation data. ; By formula , filter out the evaluation score corresponding to each vehicle operation data ,in, Indicated as number The standard threshold corresponding to the vehicle operation data, Indicated as number The weight coefficient corresponding to the vehicle operation data; Then through the formula , confirm the driving behavior evaluation coefficient corresponding to the target vehicle .

[0009] Preferably, the external environment data corresponding to the target vehicle is processed, and the environmental risk assessment coefficient corresponding to the target vehicle is confirmed according to the processing result, specifically including: Acquire external environment data corresponding to the target vehicle during driving, and extract weather data, road condition data, traffic flow data and time data from the external environment data; Quantifying the external environment data according to a preset quantification method, and then determining the risk score corresponding to the external environment data; By formula , confirm the environmental risk assessment coefficient corresponding to the target vehicle ,in, They are respectively represented by the risk scores corresponding to weather data, road condition data, traffic flow data, and time data. Expressed as a preset weight coefficient.

[0010] Preferably, a comprehensive risk assessment coefficient is generated according to the driving behavior assessment coefficient and the environmental risk assessment coefficient, specifically including: Substitute the driving behavior assessment coefficient and the environmental risk assessment coefficient into the formula Then, the comprehensive risk assessment coefficient corresponding to the target vehicle is determined. ,in, They are respectively expressed as weight coefficients corresponding to the driving behavior assessment coefficient and the environmental risk assessment coefficient; The comprehensive risk assessment coefficient corresponding to the target vehicle The preset comprehensive risk assessment threshold range For comparison, They are respectively represented as a preset first comprehensive risk assessment threshold and a second comprehensive risk assessment threshold; If the comprehensive risk assessment coefficient corresponding to the target vehicle , the target vehicle driver is judged to be a high-risk driver; If the comprehensive risk assessment coefficient corresponding to the target vehicle , the target vehicle driver is judged to be a low-risk driver.

[0011] Preferably, the rate adjustment strategy is determined in combination with the pricing model preset by the insurance company, including: The insurance company's default pricing model is set up as: , confirm the insurance premium adjustment index through the insurance company's preset pricing model ,in, Expressed as the basic insurance premium rate, Represented as the preset first adjustment coefficient, Represented as a preset second adjustment coefficient.

[0012] Preferably, dynamically adjusting the insurance premium corresponding to the target vehicle further includes: Select a time window, collect the driving behavior evaluation coefficient corresponding to the target vehicle in real time, and establish a real-time change curve of the driving behavior evaluation coefficient of the target vehicle ; By formula , confirm the change coefficient of the target vehicle driving behavior evaluation coefficient ,in, Expressed as a preset standard driving behavior evaluation coefficient, They are respectively represented as the maximum and minimum values ​​corresponding to the target vehicle driving behavior evaluation coefficient, is represented as a preset adjustment function, Represented as a preset time period; The change coefficient of the target vehicle driving behavior evaluation coefficient The preset change threshold Make a comparison; If the change coefficient of the target vehicle driving behavior evaluation coefficient , then the insurance rate corresponding to the target vehicle needs to be adjusted dynamically.

[0013] In the second aspect, the present application provides an insurance intelligent risk control method, which adopts the following technical solution: An insurance intelligent risk control method comprises the following steps: Collect vehicle operation data and external environment data corresponding to the target vehicle during driving; Analyze the vehicle operation data corresponding to the target vehicle during driving, and generate a driving behavior assessment coefficient corresponding to the target vehicle based on the analysis results, and process the external environment data corresponding to the target vehicle, and confirm the environmental risk assessment coefficient corresponding to the target vehicle according to the processing results; Generate a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and confirm the rate adjustment strategy in combination with the insurance company's preset pricing model; Respond to the rate adjustment strategy and dynamically adjust the rate corresponding to the target vehicle insurance.

[0014] In a third aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute any one of the above-described intelligent insurance risk control systems.

[0015] In summary, the present application includes at least one of the following beneficial technical effects: 1. The present invention provides an insurance intelligent risk control system, which analyzes the vehicle operation data and external environment data corresponding to the target vehicle during driving, and respectively determines the driving behavior assessment coefficient and the environmental risk assessment coefficient, and then generates a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and determines the rate adjustment strategy in combination with the pricing model preset by the insurance company, thereby effectively realizing the efficient fusion of multi-source data, thereby effectively improving the accuracy of risk assessment; 2. By collecting the driving behavior assessment coefficient corresponding to the target vehicle in real time and establishing a real-time change curve of the driving behavior assessment coefficient of the target vehicle, the change coefficient of the driving behavior assessment coefficient of the target vehicle is confirmed, and the insurance rate corresponding to the target vehicle is dynamically adjusted based on the change coefficient of the driving behavior assessment coefficient of the target vehicle, thereby effectively improving the accuracy of risk assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0017] Figure 1 It is a system diagram of the intelligent insurance risk control according to the embodiment of the present application.

[0018] Figure 2 It is a flow chart of the method of intelligent insurance risk control in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following is combined with Figure 1-2 This application is described in further detail.

[0020] Example 1 The embodiment of the present application discloses an insurance intelligent risk control system.

[0021] Reference Figure 1 , an insurance intelligent risk control system, comprising: A data acquisition module is used to collect vehicle operation data and external environment data corresponding to the target vehicle during driving; A data analysis module is used to analyze the vehicle operation data corresponding to the target vehicle during driving, and generate a driving behavior assessment coefficient corresponding to the target vehicle based on the analysis results, and process the external environment data corresponding to the target vehicle, and confirm the environmental risk assessment coefficient corresponding to the target vehicle according to the processing results; The insurance adjustment module is used to generate a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and to confirm the rate adjustment strategy in combination with the pricing model preset by the insurance company; The policy execution module is used to respond to the rate adjustment policy and dynamically adjust the rate corresponding to the target vehicle insurance.

[0022] By adopting the above technical scheme, the vehicle operation data and external environment data corresponding to the target vehicle during driving are analyzed, and the driving behavior assessment coefficient and the environmental risk assessment coefficient are respectively confirmed. Then, a comprehensive risk assessment coefficient is generated based on the driving behavior assessment coefficient and the environmental risk assessment coefficient. In combination with the pricing model preset by the insurance company, the rate adjustment strategy is confirmed, thereby effectively realizing the efficient integration of multi-source data, thereby effectively improving the accuracy of risk assessment.

[0023] Furthermore, the vehicle operation data corresponding to the target vehicle during driving includes sudden acceleration data, sudden braking data, speeding data, sharp turn data, fatigue driving data and distracted driving data; The external environment data corresponding to the target vehicle during driving includes weather data, road condition data, traffic flow data and time data.

[0024] It should be noted that the vehicle operation data corresponding to the target vehicle during driving is analyzed, and the driving behavior evaluation coefficient corresponding to the target vehicle is generated based on the analysis results, specifically including: Obtain the vehicle operation data corresponding to the target vehicle during driving, and extract the sudden acceleration data, sudden braking data, speeding data, sharp turn data, fatigue driving data and distracted driving data from the vehicle operation data, and number the vehicle operation data to obtain the actual value corresponding to the vehicle operation data ,in, Indicates the number corresponding to each vehicle operation data. ; Specifically, in the embodiment of the present application, the sudden acceleration data is set to the corresponding number of sudden accelerations within the preset time, the sudden braking data is set to the corresponding number of sudden brakings within the preset time, the speeding data is set to the corresponding number of speedings within the preset time, the sharp turn data is set to the corresponding number of sharp turns within the preset time, the fatigue driving data is set to the corresponding number of fatigue driving within the preset time, the distracted driving data is set to the corresponding number of distracted driving within the preset time, and the number corresponding to the sudden acceleration data is 1, the number corresponding to the sudden braking data is 2, the number corresponding to the speeding data is 3, the number corresponding to the sharp turn data is 4, the number corresponding to the fatigue driving data is 5, and the number corresponding to the distracted driving data is 6; By formula , filter out the evaluation score corresponding to each vehicle operation data ,in, Indicated as number The standard threshold corresponding to the vehicle operation data, Indicated as number The weight coefficient corresponding to the vehicle operation data; For example, if the target vehicle driver has 10 acceleration data during driving, and the preset acceleration standard threshold and weight coefficient are 5 times and 0.2, then the evaluation score corresponding to the acceleration data is ; Then through the formula , confirm the driving behavior evaluation coefficient corresponding to the target vehicle .

[0025] Specifically, through the data of sudden acceleration, sudden braking, speeding, sharp turns, fatigue driving and distracted driving corresponding to the target vehicle during driving, the driver's driving behavior can be comprehensively and real-time evaluated, and then the driving behavior evaluation coefficient of the target vehicle can be confirmed. The benefits in many aspects, first of all, the driving behavior evaluation coefficient can effectively reflect the driver's driving habits and behavior patterns, help insurance companies more accurately assess driving risks, thereby providing personalized insurance pricing strategies, avoiding "one-size-fits-all" pricing methods, and improving the fairness and accuracy of insurance products; secondly, through the analysis of the above data, the driver can promptly understand whether there are safety hazards in his or her driving behavior, and make corresponding adjustments and improvements, thereby reducing the probability of traffic accidents and improving driving safety; in short, the driving behavior evaluation coefficient generated based on the above data can not only optimize insurance pricing, but also improve driving safety and management efficiency.

[0026] It should be noted that the external environment data corresponding to the target vehicle is processed, and the environmental risk assessment coefficient corresponding to the target vehicle is confirmed based on the processing result, specifically including: Acquire external environment data corresponding to the target vehicle during driving, and extract weather data, road condition data, traffic flow data and time data from the external environment data; Quantifying the external environment data according to a preset quantification method, and then determining the risk score corresponding to the external environment data; By formula , confirm the environmental risk assessment coefficient corresponding to the target vehicle ,in, They are respectively represented by the risk scores corresponding to weather data, road condition data, traffic flow data, and time data. Expressed as a preset weight coefficient.

[0027] Specifically, the risk score corresponding to the weather data is quantified according to the severity of the weather conditions. For example, in the embodiment of the present application, if it is sunny, the risk score corresponding to the weather data is 100, if it is light rain, the risk score corresponding to the weather data is 80, if it is heavy rain, the risk score corresponding to the weather data is 60, if it is snowing, the risk score corresponding to the weather data is 50, and if it is foggy, the risk score corresponding to the weather data is 40; similarly, the risk score corresponding to the road condition data is quantified according to the complexity of the road condition, and the complexity of the road condition includes but is not limited to smooth roads, slight congestion, severe congestion, road construction and slippery roads; the risk score corresponding to the traffic flow data is quantified according to the traffic density and the areas prone to traffic accidents, and the traffic density and the areas prone to traffic accidents are not limited to low traffic flow, medium traffic flow, high traffic flow and accident-prone areas; the risk score corresponding to the time data is quantified according to the degree of danger of driving time, and the degree of danger of driving time includes but is not limited to daytime, nighttime, rush hour and holidays.

[0028] Specifically, confirming the environmental risk assessment coefficient corresponding to the target vehicle based on weather data, road condition data, traffic flow data and time data has multiple benefits. First, the environmental risk assessment coefficient can accurately assess the risk level of the vehicle's environment in real time, provide insurance companies with a more accurate basis for risk pricing, and avoid claims uncertainty caused by environmental factors. Secondly, drivers can use this coefficient to understand the risks of the current driving environment, so as to adopt more cautious driving behavior and reduce the probability of accidents. In addition, the environmental risk assessment coefficient can also help insurance companies optimize risk control strategies, such as issuing timely warnings to drivers in high-risk environments or suggesting to avoid specific sections of the road, further reducing the possibility of accidents. The above-mentioned evaluation method based on real-time environmental data has improved the insurance industry's refined management level and customer satisfaction.

[0029] Furthermore, a comprehensive risk assessment coefficient is generated based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, specifically including: Substitute the driving behavior assessment coefficient and the environmental risk assessment coefficient into the formula Then, the comprehensive risk assessment coefficient corresponding to the target vehicle is determined. ,in, They are respectively expressed as weight coefficients corresponding to the driving behavior assessment coefficient and the environmental risk assessment coefficient; The comprehensive risk assessment coefficient corresponding to the target vehicle The preset comprehensive risk assessment threshold range For comparison, They are respectively represented as a preset first comprehensive risk assessment threshold and a second comprehensive risk assessment threshold; If the comprehensive risk assessment coefficient corresponding to the target vehicle , the target vehicle driver is judged to be a high-risk driver; If the comprehensive risk assessment coefficient corresponding to the target vehicle , the target vehicle driver is judged to be a low-risk driver.

[0030] It should be noted that the rate adjustment strategy is determined in combination with the insurance company's preset pricing model, including: The insurance company's default pricing model is set up as: , confirm the insurance premium adjustment index through the insurance company's preset pricing model ,in, Expressed as the basic insurance premium rate, Represented as the preset first adjustment coefficient, Represented as a preset second adjustment coefficient.

[0031] Specifically, the base rate is the initial insurance premium calculated by the insurance company based on the basic information of the vehicle and the owner's historical data, and is usually related to the following static factors: Vehicle type: The maintenance costs and accident rates of different vehicle models vary greatly; Vehicle age: Older vehicles may cost more to repair and may also have higher insurance rates; Age of the vehicle owner: Young or elderly drivers may have a higher accident rate; Historical claims record: Car owners with multiple claims may be considered high risk; Usage scenario: frequency of use and purpose of the vehicle (e.g. personal vehicle, commercial vehicle), etc. The basic rate is calculated as follows: R0=f(vehicle type, vehicle age, owner age, historical claims record, usage scenario) Among them, f is the basic rate model preset by the insurance company.

[0032] Specifically, in the embodiments of the present application, It is represented by the preset first adjustment coefficient, which is the low-risk driving discount coefficient. It is expressed as the preset second adjustment coefficient, which is the high-risk driving surcharge coefficient.

[0033] Furthermore, the insurance premium of the target vehicle is dynamically adjusted, including: Select a time window, collect the driving behavior evaluation coefficient corresponding to the target vehicle in real time, and establish a real-time change curve of the driving behavior evaluation coefficient of the target vehicle ; By formula , confirm the change coefficient of the target vehicle driving behavior evaluation coefficient ,in, Expressed as a preset standard driving behavior evaluation coefficient, They are respectively represented as the maximum and minimum values ​​corresponding to the target vehicle driving behavior evaluation coefficient, is represented as a preset adjustment function, Represented as a preset time period; The change coefficient of the target vehicle driving behavior evaluation coefficient The preset change threshold Make a comparison; If the change coefficient of the target vehicle driving behavior evaluation coefficient , then the insurance rate corresponding to the target vehicle needs to be adjusted dynamically.

[0034] Specifically, the process of dynamically adjusting the insurance premium corresponding to the target vehicle is set to the change coefficient of the target vehicle driving behavior evaluation coefficient , then the insurance rate of the target vehicle is increased. If the change coefficient of the target vehicle driving behavior evaluation coefficient is , then reduce the corresponding insurance rate of the target vehicle.

[0035] Example 2 The embodiment of the present application also discloses an insurance intelligent risk control method.

[0036] Reference Figure 2 , an insurance intelligent risk control method, comprising the following steps: Collect vehicle operation data and external environment data corresponding to the target vehicle during driving; Analyze the vehicle operation data corresponding to the target vehicle during driving, and generate a driving behavior assessment coefficient corresponding to the target vehicle based on the analysis results, and process the external environment data corresponding to the target vehicle, and confirm the environmental risk assessment coefficient corresponding to the target vehicle according to the processing results; Generate a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and confirm the rate adjustment strategy in combination with the insurance company's preset pricing model; Respond to the rate adjustment strategy and dynamically adjust the rate corresponding to the target vehicle insurance.

[0037] The above contents are merely examples and explanations of the concept of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0038] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0039] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An insurance intelligent risk control system, characterized in that: include: A data acquisition module is used to collect vehicle operation data and external environment data corresponding to the target vehicle during driving; A data analysis module is used to analyze the vehicle operation data corresponding to the target vehicle during driving, and generate a driving behavior assessment coefficient corresponding to the target vehicle based on the analysis results, and process the external environment data corresponding to the target vehicle, and confirm the environmental risk assessment coefficient corresponding to the target vehicle according to the processing results; The insurance adjustment module is used to generate a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and to confirm the rate adjustment strategy in combination with the pricing model preset by the insurance company; The policy execution module is used to respond to the rate adjustment policy and dynamically adjust the rate corresponding to the target vehicle insurance.

2. An insurance intelligent risk control system according to claim 1, characterized in that: The vehicle operation data corresponding to the target vehicle during driving includes sudden acceleration data, sudden braking data, speeding data, sharp turn data, fatigue driving data and distracted driving data; The external environment data corresponding to the target vehicle during driving includes weather data, road condition data, traffic flow data and time data.

3. An insurance intelligent risk control system according to claim 2, characterized in that: The vehicle operation data corresponding to the target vehicle during driving is analyzed, and the driving behavior evaluation coefficient corresponding to the target vehicle is generated based on the analysis results, including: Obtain the vehicle operation data corresponding to the target vehicle during driving, and extract the sudden acceleration data, sudden braking data, speeding data, sharp turn data, fatigue driving data and distracted driving data from the vehicle operation data, and number the vehicle operation data to obtain the actual value corresponding to the vehicle operation data ,in, Indicates the number corresponding to each vehicle operation data. ; By formula , filter out the evaluation score corresponding to each vehicle operation data ,in, Indicated as number The standard threshold corresponding to the vehicle operation data, Indicated as number The weight coefficient corresponding to the vehicle operation data; Then through the formula , confirm the driving behavior evaluation coefficient corresponding to the target vehicle .

4. The insurance intelligent risk control system according to claim 2, characterized in that: The external environment data corresponding to the target vehicle is processed, and the environmental risk assessment coefficient corresponding to the target vehicle is confirmed according to the processing results, including: Acquire external environment data corresponding to the target vehicle during driving, and extract weather data, road condition data, traffic flow data and time data from the external environment data; Quantifying the external environment data according to a preset quantification method, and then determining the risk score corresponding to the external environment data; By formula , confirm the environmental risk assessment coefficient corresponding to the target vehicle ,in, They are respectively represented by the risk scores corresponding to weather data, road condition data, traffic flow data, and time data. Represented as a preset weight coefficient.

5. An insurance intelligent risk control system according to claim 4, characterized in that: The comprehensive risk assessment coefficient is generated based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, including: Substitute the driving behavior assessment coefficient and the environmental risk assessment coefficient into the formula Then, the comprehensive risk assessment coefficient corresponding to the target vehicle is determined. ,in, They are respectively expressed as weight coefficients corresponding to the driving behavior assessment coefficient and the environmental risk assessment coefficient; The comprehensive risk assessment coefficient corresponding to the target vehicle The preset comprehensive risk assessment threshold range For comparison, They are respectively represented as a preset first comprehensive risk assessment threshold and a second comprehensive risk assessment threshold; If the comprehensive risk assessment coefficient corresponding to the target vehicle , the target vehicle driver is judged to be a high-risk driver; If the comprehensive risk assessment coefficient corresponding to the target vehicle , the target vehicle driver is judged to be a low-risk driver.

6. An insurance intelligent risk control system according to claim 5, characterized in that: Combined with the insurance company's preset pricing model, confirm the rate adjustment strategy, including: The insurance company's default pricing model is set up as: , confirm the insurance premium adjustment index through the insurance company's preset pricing model ,in, Expressed as the basic insurance premium rate, Represented as the preset first adjustment coefficient, Represented as a preset second adjustment coefficient.

7. An insurance intelligent risk control system according to claim 3, characterized in that: Dynamically adjust the insurance premiums for the target vehicle, including: Select a time window, collect the driving behavior evaluation coefficient corresponding to the target vehicle in real time, and establish a real-time change curve of the driving behavior evaluation coefficient of the target vehicle ; By formula , confirm the change coefficient of the target vehicle driving behavior evaluation coefficient ,in, Expressed as a preset standard driving behavior evaluation coefficient, They are respectively represented as the maximum and minimum values ​​corresponding to the target vehicle driving behavior evaluation coefficient, is represented as a preset adjustment function, Represented as a preset time period; The change coefficient of the target vehicle driving behavior evaluation coefficient The preset change threshold Make a comparison; If the change coefficient of the target vehicle driving behavior evaluation coefficient , then the insurance rate corresponding to the target vehicle needs to be adjusted dynamically.

8. An insurance intelligent risk control method, applied to an insurance intelligent risk control system as described in any one of claims 1 to 7, characterized in that: The following steps are involved: Collect vehicle operation data and external environment data corresponding to the target vehicle during driving; Analyze the vehicle operation data corresponding to the target vehicle during driving, and generate a driving behavior assessment coefficient corresponding to the target vehicle based on the analysis results, and process the external environment data corresponding to the target vehicle, and confirm the environmental risk assessment coefficient corresponding to the target vehicle according to the processing results; Generate a comprehensive risk assessment coefficient based on the driving behavior assessment coefficient and the environmental risk assessment coefficient, and confirm the rate adjustment strategy in combination with the insurance company's preset pricing model; Respond to the rate adjustment strategy and dynamically adjust the rate corresponding to the target vehicle insurance.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute an insurance intelligent risk control system as described in any one of claims 1 to 7.

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