Vehicle anti-theft test data processing method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle anti-theft test data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: classifying NVSA anti-theft test cases based on the structure information of a vehicle, and obtaining the classification information corresponding to different modules of each target system of the vehicle; the method comprises the following steps of: classifying the vehicle, calculating the score of each target system on the basis of classification information and preset score calculation functions of different modules of each target system, performing insurance rating on the basis of the score and an RCAR test result of the vehicle, and finally performing NVSA rating on the basis of the score, an insurance rating result and a preset rating standard to obtain the NVSA grade of the vehicle. By adopting the method, the analysis efficiency of the NVSA anti-theft test data can be improved, and meanwhile, the value of the NVSA anti-theft test data is deeply mined.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method, apparatus, electronic device, and storage medium for processing vehicle anti-theft test data. Background Technology

[0002] Currently, major OEMs have established relatively mature testing processes and methodologies in the field of vehicle testing, using diverse test cases to comprehensively verify vehicle performance, functionality, and reliability. In the data processing, statistics, and analysis phase, major OEMs generally employ data statistics techniques to statistically analyze test cases and drive product technology iteration. Among these, NVSA (New Vehicle Security Assessment) anti-theft test cases are crucial test data for evaluating vehicle anti-theft performance. However, with the increasing complexity of intelligent connected vehicles, the number of NVSA anti-theft test cases is growing exponentially. Traditional data statistics techniques are often limited to static description and one-way processing. Scattered NVSA anti-theft test data is difficult to integrate uniformly, the single statistical dimension leads to biased analysis results, and excessive manual intervention reduces efficiency. Furthermore, there is a lack of dynamic correlation mining and intelligent analysis capabilities for massive amounts of NVSA anti-theft test data. Limitations in real-time performance, multi-dimensional correlation analysis, and predictive capabilities are becoming increasingly apparent, resulting in the underutilization of the value of NVSA anti-theft test data. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a method, apparatus, electronic device and storage medium for processing vehicle anti-theft test data, so as to alleviate the above-mentioned problems existing in the related art.

[0004] In a first aspect, embodiments of the present invention provide a method for processing vehicle anti-theft test data, comprising: classifying NVSA anti-theft test cases based on the vehicle's structural information to obtain classification information corresponding to different modules of each target system of the vehicle; calculating the score of each target system based on the classification information and a preset score calculation function for each module of each target system; performing an insurance rating based on the score and the vehicle's RCAR test results; and performing an NVSA rating based on the score, the insurance rating results, and a preset rating standard to obtain the vehicle's NVSA level.

[0005] Secondly, embodiments of the present invention also provide a vehicle anti-theft test data processing device, comprising: a classification module, used to classify NVSA anti-theft test cases based on the vehicle's structural information to obtain classification information corresponding to different modules of each target system of the vehicle; a score calculation module, used to calculate the score of each target system based on the classification information and a preset score calculation function for each target system's different modules; a first rating module, used to perform an insurance rating based on the score and the vehicle's RCAR test results; and a second rating module, used to perform an NVSA rating based on the score, the insurance rating result, and a preset rating standard to obtain the vehicle's NVSA level.

[0006] Thirdly, embodiments of the present invention also provide an electronic device, including a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement the vehicle anti-theft test data processing method described in the first aspect above.

[0007] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions. When the computer-executable instructions are invoked and executed by a processor, the computer-executable instructions cause the processor to implement the vehicle anti-theft test data processing method described in the first aspect.

[0008] This invention provides a vehicle anti-theft test data processing method, apparatus, electronic device, and storage medium. First, NVSA anti-theft test cases are classified based on the vehicle's structural information to obtain classification information for different modules of each target system. Then, based on the classification information and preset score calculation functions for each module of each target system, a score is calculated for each target system. Next, an insurance rating is performed based on the score and the vehicle's RCAR test results. Finally, an NVSA rating is performed based on the score, insurance rating results, and preset rating standards to obtain the vehicle's NVSA level. Using this technology, massive amounts of NVSA anti-theft test data can be uniformly integrated into a single vehicle system score by incorporating the vehicle's structural information and preset score calculation functions for different modules. Combined with the vehicle's RCAR test results and preset rating standards, the vehicle's NVSA rating is obtained. This not only improves the efficiency of NVSA anti-theft test data analysis but also enables the automatic aggregation of massive amounts of NVSA anti-theft test data for subsequent visualization output, thereby deeply mining the value of NVSA anti-theft test data, providing strong data support for decision-making, and driving product technology iteration.

[0009] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0010] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0011] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating a vehicle anti-theft test data processing method according to an embodiment of the present invention; Figure 2 This is an example diagram showing the classification information corresponding to some modules of the mechanical safety system in an embodiment of the present invention; Figure 3 This is an example diagram of the fraction calculation function in an embodiment of the present invention; Figure 4 This is an example diagram of the rating criteria in an embodiment of the present invention; Figure 5 This is an example diagram showing the insurance group division, target system score, and main target system rating results in an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of a vehicle anti-theft test data processing device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] To facilitate understanding of this embodiment, a detailed description of a vehicle anti-theft test data processing method disclosed in this embodiment of the invention will be provided first. (See [link to relevant documentation]). Figure 1As shown, the method may include the following steps: Step S102: Classify NVSA anti-theft test cases based on vehicle structural information to obtain classification information corresponding to different modules of each target system of the vehicle.

[0015] Step S104: Calculate the score of each target system based on the classification information and the preset score calculation function of each target system's different modules.

[0016] Step S106: Conduct insurance rating based on the score and the vehicle's RCAR test results.

[0017] Step S108: Based on the score, insurance rating results and preset rating standards, perform NVSA rating to obtain the vehicle's NVSA level.

[0018] This invention provides a vehicle anti-theft test data processing method. First, NVSA anti-theft test cases are classified based on the vehicle's structural information to obtain classification information for different modules of each target system. Then, based on the classification information and preset score calculation functions for each module of each target system, a score is calculated for each target system. Next, an insurance rating is performed based on the score and the vehicle's RCAR test results. Finally, an NVSA rating is performed based on the score, insurance rating results, and preset rating standards to obtain the vehicle's NVSA level. This method utilizes the vehicle's structural information to introduce preset score calculation functions for different modules, unifying massive amounts of NVSA anti-theft test data into a single system score for the vehicle. Combined with the vehicle's RCAR test results and preset rating standards, the NVSA rating is performed. This not only improves the efficiency of NVSA anti-theft test data analysis but also enables the automatic aggregation of massive amounts of NVSA anti-theft test data for subsequent visualization output, thereby deeply mining the value of NVSA anti-theft test data, providing strong data support for decision-making, and driving product technology iteration.

[0019] As one possible implementation, the above-mentioned structural information may include the vehicle's system architecture information and module information; based on this, the above-mentioned step S102 (i.e., classifying NVSA anti-theft test cases based on the vehicle's structural information to obtain classification information corresponding to different modules of each target system of the vehicle) may include: performing a first classification on NVSA anti-theft test cases based on system architecture information to obtain the first classification information corresponding to each target system; and performing a second classification on the first classification information based on module information to obtain classification information.

[0020] For example, after obtaining several NVSA anti-theft test cases for a vehicle, it is necessary to comprehensively organize and classify the NVSA anti-theft test cases. Specifically, these NVSA anti-theft test cases can be imported into an Excel spreadsheet. Based on the vehicle's system architecture and the division of functional modules, these NVSA anti-theft test cases are initially classified into four main systems (including mechanical security system, VIN code system, electronic security system, and penalty point system), obtaining the test data corresponding to each main system. Then, the identification information (such as name, number, etc.) of each main system is used as a field to generate a data statistics table to classify and statistically analyze the test data corresponding to different main systems. Subsequently, according to the multiple functional modules of each main system (such as the "peripheral lock test" module, "hood release attack test" module, "in-vehicle entertainment system" module, etc. of the mechanical security system), the test cases corresponding to each main system are further classified in detail to obtain the test data corresponding to each module under each main system. Then, in the data statistics table, the identification information (such as name, number, etc.) of each module under each main system is used as a subfield to further classify and statistically analyze the test data corresponding to different modules under each main system.

[0021] As one possible implementation, the classification information may include test result information for different test items of the corresponding module; based on this, step S104 (i.e., calculating the score of each target system based on the classification information and the preset score calculation function for each different module of each target system) may include: Step A1: Based on the test result information of the same module and the preset score calculation function, calculate the initial score of the same module to obtain the initial score of each module.

[0022] For example, the test result information may include the preset score and test status identifier of the corresponding test item; the preset score and test status identifier of the same module can be calculated by using the preset score calculation function of the same module to obtain the initial score of the same module. The initial score of each module can be obtained by using the corresponding preset score calculation function to calculate the corresponding preset score and the corresponding test status identifier for each module.

[0023] Continuing from the previous example, let's take a mechanical safety system as an example, see... Figure 2 As shown, the data statistics table displays two modules under the mechanical safety system (i.e. Figure 2 The test items for the "Additional Safety Configuration" module and the "In-Vehicle Entertainment System" module, as well as the test status indicators for each test item (i.e., ... Figure 2 The "TRUE" and "FALSE" in the score and the set score (i.e. Figure 2The score in the cell to the right of the test status indicator can be used to calculate the total score for each module by summing the set scores of all test items with the test status indicator "TRUE" under that module using the module's score calculation function. This total score is then displayed in the designated area of ​​that module. Figure 2 The overall score of the module is displayed in the corresponding cell using a gray background and white text.

[0024] In practical applications, test items can be divided into anti-attack test items and non-anti-attack test items. The test status identifier for each anti-attack test item can be the corresponding test time, and the test status identifier for each non-anti-attack test item can indicate whether the corresponding test item has passed the test. For each module, the following steps A11 to A12 can be performed using the module's preset score calculation function: Step A11: If the module includes both non-attack resistance test items and attack resistance test items, the preset scores of all non-attack resistance test items of the module are summed to obtain the non-attack resistance test score of the module. The test time of all attack resistance test items of the module is judged according to the preset judgment rules. Then, based on the judgment result and the preset scores of all attack resistance test items of the module, the attack resistance test score of the module is calculated. The non-attack resistance test score and the attack resistance test score of the module are added together. Then, the minimum value between the sum and the preset initial score threshold of the module is used as the initial score of the module.

[0025] Step A12: If the module only includes non-anti-attack test items and does not include anti-attack test items, then the preset scores of all non-anti-attack test items of the module are summed to obtain the non-anti-attack test score of the module, and the minimum value between the non-anti-attack test score of the module and the preset initial score threshold is used as the initial score of the module.

[0026] Following the previous example, during vehicle testing, experiments can be conducted based on test cases. After the test results are entered into an Excel spreadsheet, taking a mechanical safety system as an example, see [link to Excel]. Figure 3 As shown, the representation can be introduced as follows: The SUMIF function is used to connect the data in the score calculation table. In this SUMIF function, column D is the category name in the score calculation table, cell A4 contains "Mechanical Safety", and column G contains the scores for each part. The SUMIF function will summarize the scores of each module in the Mechanical Safety system, intuitively reflecting the new car safety assessment performance of the tested vehicle in this part. The total score of the Mechanical Safety system in the score calculation table is summarized in an Excel spreadsheet, and so on to obtain the scores of each system. The introduced SUMIF function will automatically calculate the score of each module using the data in the score calculation table. Then, the score calculation function in the statistics interface is used to summarize the scores of each module in the Mechanical Safety system dimension using the following representation:

[0027] The meaning of this score calculation function is as follows: It selects the test items marked "TRUE" (i.e., passed tests) within the range "F11:F16" of the "Peripheral Lock Test" module in the mechanical safety system, and sums these scores to obtain the non-attack resistance test score for this module. It also selects the attack resistance time (specifically, the actual attack resistance time recorded after an attack on the mechanical lock on the driver's side door while the vehicle is armed) entered in cell F10 of the "Peripheral Lock Test" module in the mechanical safety system, and sums the scores corresponding to the scores set in cell G10. If the attack resistance time entered in cell F10 is greater than... If the door does not open within 120 seconds (indicating that the door has not been opened within 120 seconds), the score set in cell G10 (i.e., 20 points) is obtained as the anti-attack test score. If the anti-attack time entered in cell F10 is greater than 60 seconds but less than or equal to 120 seconds (indicating that the mechanical lock resists the attack for more than 60 seconds but less than 120 seconds), half of the score set in cell G10 (i.e., 10 points) is obtained as the anti-attack test score for this module. The non-anti-attack test score and the anti-attack test score of this module are added together, and the sum is compared with the maximum score of this module (i.e., 52 points). The minimum value between the sum and the maximum score is taken as the final score of the tested vehicle in this module. Only for the mechanical safety system dimension's anti-attack test items (such as anti-attack tests on door locks, hood, EPB locks, etc.), the anti-attack time needs to be filled in. For each module of other system dimensions, a corresponding scoring function is used to accumulate the set scores of the test items that have passed within the selected range and compare them with the maximum score of that module. The minimum value between the sum and the maximum score is then used as the final score of the tested vehicle for that module. For example, the scoring function for each module of other system dimensions can be expressed as follows:

[0028] The function means: select the test items that are "TRUE" in the "F45:F51" range of a certain module and the corresponding scores set in the "G45:G51" range, and accumulate these scores to obtain the non-attack test score of the module; compare the non-attack test score of the module with the maximum score of the module (i.e., 20 points), and then take the minimum value between the non-attack test score and the maximum score as the final score of the tested vehicle in the module.

[0029] Step A2: Based on the initial scores of different modules of the same target system, calculate the score of the same target system to obtain the score of each target system.

[0030] For example, the initial scores of all modules in the same target system can be summed to obtain the score of the same target system, and the initial scores of all modules in each target system can be summed to obtain the score of each target system.

[0031] Continuing from the previous example, for each of the three dimensions—mechanical safety system, VIN code system, and electronic safety system—the final scores of all modules under that dimension can be summed to obtain the system score for that dimension, thereby obtaining the system scores of the tested vehicle in the mechanical safety system, VIN code system, and electronic safety system respectively.

[0032] As one possible implementation, the target system may include multiple main target systems and penalty point systems, and the insurance rating result may include an insurance level. Based on this, step S106 (i.e., insurance rating based on scores and vehicle RCAR test results) may include: performing a preliminary insurance rating based on the RCAR test results to obtain an initial insurance level, and correcting the initial insurance level based on the scores of the penalty point system to obtain the insurance level.

[0033] Following the previous example, the SOP (Start of Production) status of the tested vehicle can be rated according to the Thatcham insurance rating assessment rules. The initial insurance rating group (i.e., insurance level) of the tested vehicle can be obtained through the test results of RCAR (Research Council for Automobile Repairs) (RCAR mainly considers low-speed structural crash tests and bumper crash tests). Then, the initial insurance rating group of the tested vehicle needs to be adjusted in combination with the impact of the penalty system. The specific adjustment method is as follows: for each test item in the penalty system that fails, the insurance rating group is directly upgraded by one insurance rating group. For example, if three test items in the penalty system fail, the insurance rating group will be upgraded from the initial insurance rating group Group 5 to the new insurance rating group Group 8 as the final insurance rating group of the tested vehicle.

[0034] As one possible implementation, the aforementioned preset rating criteria may include a first mapping relationship between multiple insurance level ranges and multiple score ranges for each primary target system, and a second mapping relationship between multiple score ranges for each primary target system and the system level. Based on this, the aforementioned step S108 (i.e., performing NVSA rating based on scores, insurance rating results, and preset rating criteria to obtain the vehicle's NVSA level) may include: determining the target insurance level range in which the insurance level falls, and determining the target score range for each primary target system based on the target insurance level range, the first mapping relationship, and the score of each primary target system; determining the first system level for each primary target system based on the target score range and the second mapping relationship; converting the first system level of each primary target system into a corresponding level value, and selecting a second system level as the NVSA level from the first system levels of all primary target systems based on the level value.

[0035] Continuing from the previous example, taking the mechanical safety system as an example, after obtaining the system score of the tested vehicle in the mechanical safety system, it can be combined with the final insurance rating group of the tested vehicle and... Figure 4 The rating criteria shown introduce the IF function to perform the following rating calculations: (1) If the final insurance rating group of the tested vehicle is Group 1-17, the system level of the mechanical safety system is calculated using the following IF function: IF($B$4>170,"E",IF($B$4>=125,"A",IF($B$4>=50,"D","U"))) The meaning of this IF function is as follows: if the score in cell B4 (i.e., the system score of the tested vehicle in the mechanical safety system) is greater than 170, the system grade is "E"; if the score in cell B4 is less than or equal to 170 but greater than or equal to 125, the system grade is "A"; if the score in cell B4 is less than 125 but greater than or equal to 50, the system grade is "D"; if the score in cell B4 is less than 50, the system grade is "U". (2) If the final insurance rating group of the tested vehicle is Group 18-28, the system level of the mechanical safety system is calculated using the following IF function: IF($B$4>210,"E",IF($B$4>=180,"A",IF($B$4>=75,"D","U"))) The meaning of this IF function is as follows: if the score in cell B4 is greater than 210, the system grade is "E"; if the score in cell B4 is less than or equal to 210 but greater than or equal to 180, the system grade is "A"; if the score in cell B4 is less than 180 but greater than or equal to 75, the system grade is "D"; if the score in cell B4 is less than 75, the system grade is "U". (3) If the final insurance rating group of the tested vehicle is Group 29-50, the system level of the mechanical safety system is calculated using the following IF function: IF($B$4>245,"E",IF($B$4>=210,"A",IF($B$4>=125,"D","U"))) The meaning of this IF function is as follows: if the score in cell B4 is greater than 245, the system grade is "E"; if the score in cell B4 is less than or equal to 245 but greater than or equal to 210, the system grade is "A"; if the score in cell B4 is less than 210 but greater than or equal to 125, the system grade is "D"; if the score in cell B4 is less than 125, the system grade is "U".

[0036] For example, if the tested vehicle scores 200 points in the mechanical safety system, and the final insurance rating group of the tested vehicle is Group 1-17, then the NVSA mechanical safety rating result (i.e., the system level of the mechanical safety system) is "E". If the insurance rating group is Group 18-28, then the NVSA mechanical safety rating result is "A". If the insurance rating group is Group 29-50, then the NVSA mechanical safety rating result is "U".

[0037] The same operational method can be used to combine the system scores of the tested vehicle in the VIN code system and electronic safety system with the vehicle's final insurance rating group. Figure 4 The rating criteria shown calculate the system ratings for the VIN code system and the electronic security system, respectively.

[0038] The VIN code system dimension is divided into only three system levels (i.e., "E", "A", "D"), while the mechanical safety system dimension and the electronic safety system dimension are divided into four system levels (i.e., "E", "A", "D", "U").

[0039] After obtaining the system levels for the mechanical safety system, VIN code system, and electronic safety system, since the system levels "E", "A", "D", and "U" are not easily compared directly, the function IF(C4="E",4,IF(C4="A",3,IF(C4="D",2,1))) can be used to convert the obtained system levels (recorded in cell C4) into numerical values. Specifically, if the system level is "E", the output value is 4; if the system level is "A", the output value is 3; if the system level is "D", the output value is 2; and so on. If the system rating is "U", the output value is 1. Then, the function IF(MIN(J4:J6)=1,"U",IF(MIN(J4:J6)=2,"D",IF(MIN(J4:J6)=3,"A","E"))) compares the values ​​converted from the system ratings of the mechanical safety system, VIN code system and electronic safety system under the same insurance rating group. The minimum value among these three values ​​is taken, and the system rating corresponding to the minimum value (i.e., the lowest of the three system ratings) is taken as the NVSA rating result of the tested vehicle.

[0040] The system scores for the four main systems (i.e., the mechanical safety system, the VIN code system, the electronic safety system, and the penalty point system) and the rating results (i.e., system grades) for the three main systems (i.e., the mechanical safety system, the VIN code system, and the electronic safety system) are as follows: Figure 5 As shown, Figure 5 It also shows the correlation between the insurance group (i.e., insurance rating group) of the tested vehicle and the rating results of the three main systems. (Plottable) Figure 5 The table shown visually displays the rating results of the three main systems, allowing relevant personnel to directly view the rating results of the three main systems of the tested vehicle and determine the lowest rating result among the three main systems as the final NVSA rating result (i.e., NVSA level) of the tested vehicle.

[0041] For ease of understanding, the implementation process of the above vehicle anti-theft test data processing method is described below using a specific application as an example.

[0042] First, the NVSA anti-theft test cases need to be comprehensively organized and categorized. Specifically, based on the system architecture and functional module division of the vehicle under test, the NVSA anti-theft test cases are categorized into 4 main systems (including mechanical security systems, VIN code systems, electronic security systems, and penalty point systems) and 20 sub-modules (such as peripheral lock tests, hood release attack tests, and in-vehicle entertainment systems). The detailed categorization results of the NVSA anti-theft test cases are as follows: Figure 2 As shown.

[0043] The scores for the three main systems—mechanical safety system, VIN code system, and electronic safety system—can be calculated separately and referenced to the insurance rating group for system rating (i.e., determining the system level of these three main systems); a separate summary table named "Data Statistics Results" should be created in an Excel spreadsheet (e.g., ...). Figure 5 As shown), it is used to centrally display the statistical results of various test data (including the correspondence between insurance group, system score, and system level).

[0044] In practical applications, the process of importing test cases can be quite cumbersome, as it's crucial to ensure that each test case is accurately categorized into its corresponding main system and module. While time-consuming, this step is critical for achieving scientific data statistics. Only by ensuring the accuracy and completeness of the basic data can subsequent data analysis and result presentation be more precise and reliable. During the test case import process, special attention must be paid to the uniqueness of the test case number, the completeness of the test steps, and the clarity of the expected results. Furthermore, during real-vehicle testing, the testing procedures (test tools, test methods, judgment criteria, etc.) must be strictly followed. These are all important factors in ensuring data quality.

[0045] by Figure 5 For example, the identification information of each of the four main systems (i.e., "Mechanical Safety", "VIN Code", "Electronic Safety", and "Penalty Item") can be written to the far left of the statistical results table (i.e., the summary table). Then, the SUMIF function mentioned above needs to be introduced for each main subsystem to connect the data in the score calculation table. For example, the Mechanical Safety system needs to introduce the function in the form of... The SUMIF function is used to connect the data in the score calculation table so that the scores of each module in the mechanical safety system can be calculated through this function. Then, the total score of the mechanical safety system in the score calculation table is calculated and displayed in an Excel spreadsheet. This process is repeated to obtain the scores of each system.

[0046] During testing, experiments are conducted based on test cases. After the test results are entered into an Excel spreadsheet, the SUMIF function automatically calculates the scores for each major system and summarizes them in the statistics interface (for example, calculating the final score for each module using the corresponding score calculation function). The expression form of the score calculation function is as follows: Figure 3 As shown.

[0047] Based on the summarized results, the SOP status of the tested vehicles is rated according to the Thatcham insurance rating rules. An initial insurance rating group is obtained from the RCAR test results. The final insurance rating group is obtained by adding the penalty point system's impact. Then, the scores of the three main systems (mechanical safety system, VIN system, and electronic safety system) are combined with the rating criteria (e.g., ...). Figure 4 As shown) and the final insurance rating group for the tested vehicle obtained the rating results from the three major systems (such as Figure 5 (As shown), the rating results of the three major systems are then converted into numerical values ​​and compared. The rating result of the major system with the smallest value is then used as the NVSA rating result of the tested vehicle.

[0048] The aforementioned vehicle anti-theft test data processing method utilizes statistical functions such as SUMIF in an Excel spreadsheet to intelligently analyze and process NVSA anti-theft test data. This allows for intuitive visualization of NVSA anti-theft test results through charts and other visual means. Compared to traditional manual data collection methods, this intelligent solution based on function relationships significantly improves the accuracy and efficiency of data analysis. By constructing a systematic statistical model, massive amounts of NVSA anti-theft test data are transformed into interrelated functional relationships, enabling automatic data aggregation for visualization output. This allows for in-depth mining of the value of NVSA anti-theft test data, providing strong data support for decision-making. Based on the Excel platform, this vehicle anti-theft test data processing method constructs a three-dimensional data network through function logic, integrating scattered NVSA anti-theft test data into a complete product quality profile. This facilitates the continuous improvement of product maturity through subsequent technological means. The aforementioned vehicle anti-theft test data processing method not only improves the analysis efficiency of vehicle body domain control testing, but also facilitates the discovery of potential problems through data-driven approaches, accelerating product iteration cycles. This data-centric technology upgrade will significantly enhance OEMs' ability to accurately control product quality, while increasing market acceptance of products, ultimately driving automotive R&D towards digitalization and intelligence.

[0049] The above-mentioned vehicle anti-theft test data processing method can present the NVSA anti-theft test results as the total score of each major system, so that there is a complete NVSA anti-theft test data summary method before the product is produced, which makes the product production faster, the problems in the product production process more controllable, and the product quality better.

[0050] Based on the above-described vehicle anti-theft test data processing method, this invention also provides a vehicle anti-theft test data processing device, see [link to device]. Figure 6 As shown, the device may include the following modules: The classification module 602 is used to classify NVSA anti-theft test cases based on the vehicle's structural information, and obtain the classification information corresponding to different modules of each target system of the vehicle.

[0051] The score calculation module 604 is used to calculate the score of each target system based on the classification information and the preset score calculation functions of different modules of each target system.

[0052] The first rating module 606 is used to perform an insurance rating based on the score and the vehicle's RCAR test results.

[0053] The second rating module 608 is used to perform NVSA rating based on the score, insurance rating result and preset rating standard to obtain the NVSA level of the vehicle.

[0054] By using the aforementioned vehicle anti-theft test data processing device, the vehicle's structural information can be used to introduce preset score calculation functions for different modules to integrate massive amounts of NVSA anti-theft test data into a unified score for a single vehicle system. Combined with the vehicle's RCAR test results, preset rating standards are introduced to perform NVSA rating on the vehicle. This not only improves the efficiency of NVSA anti-theft test data analysis but also enables the automatic aggregation of massive amounts of NVSA anti-theft test data for subsequent visualization output. This allows for in-depth mining of the value of NVSA anti-theft test data, providing strong data support for decision-making and driving product technology iteration.

[0055] The vehicle anti-theft test data processing device provided in this embodiment of the invention has the same implementation principle and technical effect as the aforementioned vehicle anti-theft test data processing method embodiment. For the sake of brevity, any parts not mentioned in the vehicle anti-theft test data processing device embodiment can be referred to the corresponding content in the aforementioned vehicle anti-theft test data processing method embodiment.

[0056] This invention also provides an electronic device, such as... Figure 7 The diagram shows the structure of the electronic device, which includes a processor 71 and a memory 70. The memory 70 stores computer-executable instructions that can be executed by the processor 71. The processor 71 executes the computer-executable instructions to implement the above-mentioned vehicle anti-theft test data processing method.

[0057] exist Figure 7 In the illustrated embodiment, the electronic device further includes a bus 72 and a communication interface 73, wherein the processor 71, the communication interface 73, and the memory 70 are connected via the bus 72.

[0058] The memory 70 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 73 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc. The bus 72 may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 72 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0059] The processor 71 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above-described vehicle anti-theft test data processing method can be completed by the integrated logic circuits in the processor 71 or by software instructions. The processor 71 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the vehicle anti-theft test data processing method disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in the memory. The processor 71 reads the information in the memory and, in conjunction with its hardware, completes the steps of the vehicle anti-theft test data processing method of the aforementioned embodiment.

[0060] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the above-described vehicle anti-theft test data processing method. For specific implementation details, please refer to the foregoing method embodiments, which will not be repeated here.

[0061] The computer program products of the vehicle anti-theft test data processing method, apparatus and electronic device provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the vehicle anti-theft test data processing method described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0062] Unless otherwise specifically stated, the relative steps, numerical expressions, and values ​​of the components and steps described in these embodiments do not limit the scope of the invention.

[0063] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0064] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0065] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A vehicle anti-theft test data processing method, characterized by, The method comprises the following steps: classifying the NVSA anti-theft test cases based on the vehicle structure information, to obtain classification information corresponding to each different module of each target system of the vehicle; calculating the score of each target system based on the classification information and a preset score calculation function of each different module of each target system; performing insurance rating based on the score and RCAR test result of the vehicle; performing NVSA rating based on the score, insurance rating result and preset rating standard, to obtain the NVSA level of the vehicle.

2. The vehicle anti-theft test data processing method according to claim 1, characterized in that, The structure information comprises system architecture information and module information of the vehicle; the classification of the NVSA anti-theft test cases based on the vehicle structure information, to obtain classification information corresponding to each different module of each target system of the vehicle, comprises the following steps: performing first classification of the NVSA anti-theft test cases based on the system architecture information, to obtain first classification information corresponding to each target system; performing second classification of the first classification information based on the module information, to obtain the classification information.

3. The vehicle anti-theft test data processing method according to claim 1, characterized in that, The classification information comprises test result information of different test items of the corresponding module; The calculation of the score of each target system based on the classification information and a preset score calculation function of each different module of each target system, comprises the following steps: calculating the initial score of the same module based on the test result information of the same module and the preset score calculation function, to obtain the initial score of each module; calculating the score of the same target system based on the initial scores of different modules of the same target system, to obtain the score of each target system.

4. The vehicle anti-theft test data processing method according to claim 3, characterized in that, The test result information comprises a preset score and a test state identifier of the corresponding test item; The calculation of the initial score of the same module based on the test result information of the same module and the preset score calculation function, comprises the following steps: calculating the initial score of the same module by using the preset score calculation function of the same module on the preset score and the test state identifier of the same module; The calculation of the score of the same target system based on the initial scores of different modules of the same target system, comprises the following steps: summing the initial scores of all modules of the same target system to obtain the score of the same target system.

5. The vehicle anti-theft test data processing method according to claim 4, characterized in that, The test items are divided into attack-resistant test items and non-attack-resistant test items; the test state identifier of each attack-resistant test item is the corresponding test time; the test state identifier of each non-attack-resistant test item represents whether the corresponding test item passes the test; the calculation of the initial score of the same module by using the preset score calculation function of the same module on the preset score and the test state identifier of the same module, comprises the following steps: for each module, the preset score calculation function of the module is used to perform the following operations: If the module includes both non-attack-resistant test items and attack-resistant test items, the preset scores of all non-attack-resistant test items of the module are summed to obtain a non-attack-resistant test score of the module, and the test times of all attack-resistant test items of the module are determined according to a preset determination rule, then the attack-resistant test score of the module is calculated based on the determination result and the preset scores of all attack-resistant test items of the module, and the non-attack-resistant test score and the attack-resistant test score of the module are added, and then the minimum value of the added result and the preset initial score threshold of the module is taken as the initial score of the module; If the module includes only non-attack-resistant test items and does not include attack-resistant test items, the preset scores of all non-attack-resistant test items of the module are summed to obtain a non-attack-resistant test score of the module, and the minimum value of the non-attack-resistant test score and the preset initial score threshold of the module is taken as the initial score of the module.

6. The vehicle anti-theft test data processing method according to claim 1, wherein The target system includes a plurality of main target systems and a penalty item system, and the insurance rating result includes an insurance level; the insurance rating based on the score and the RCAR test result of the vehicle includes: performing a preliminary insurance rating based on the RCAR test result to obtain an initial insurance level, and correcting the initial insurance level based on the score of the penalty item system to obtain the insurance level.

7. The vehicle anti-theft test data processing method according to claim 6, characterized in that, The preset rating standard includes a first mapping relationship between a plurality of insurance level ranges and a plurality of score ranges of each main target system, and a second mapping relationship between the plurality of score ranges of each main target system and a system level; The NVSA rating based on the score, the insurance rating result and the preset rating standard obtains the NVSA level of the vehicle, including: Determining a target insurance level range in which the insurance level is located, and determining a target score range of each main target system based on the target insurance level range, the first mapping relationship and the score of each main target system; Determining a first system level of each main target system based on the target score range of each main target system and the second mapping relationship; Converting the first system level of each main target system into a corresponding level value, and selecting a second system level from the first system levels of all main target systems as the NVSA level based on the level value.

8. A vehicle anti-theft test data processing device characterized by comprising: Including: The classification module is configured to classify the NVSA anti-theft test cases based on the structure information of the vehicle to obtain classification information corresponding to each different module of each target system of the vehicle; The score calculation module is configured to calculate the score of each target system based on the classification information and a preset score calculation function of each different module of each target system; The first rating module is configured to perform insurance rating based on the score and the RCAR test result of the vehicle; The second rating module is configured to perform NVSA rating based on the score, the insurance rating result and the preset rating standard to obtain the NVSA level of the vehicle.

9. An electronic device, comprising: The vehicle anti-theft test data processing method of any one of claims 1 to 7 is implemented by a processor executing computer executable instructions stored in a memory.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer executable instructions, which, when invoked and executed by a processor, cause the processor to implement the vehicle anti-theft test data processing method of any one of claims 1 to 7.

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