Intelligent management method and test equipment for vehicle-mounted data based on large language model

Abstract

The invention provides intelligent management and test equipment for vehicle-mounted data based on a large language model, and relates to the technical field of vehicle-mounted data management. According to the method, in an operation monitoring stage, when a vehicle runs, an operation log of a nonvolatile memory of a vehicle open system architecture of the vehicle is obtained, and a plurality of key index features in the operation log are extracted; determining whether the running log is abnormal or not on the basis of a preset large language model and an abnormal recognition strategy corresponding to each key index feature; and if yes, determining the type of the exception and the corresponding solution thereof. As the operation log is abnormal by using the large language model based on the abnormal identification strategy corresponding to each key index feature, the accuracy is high, a large amount of time cost and labor cost are saved, and the efficiency is high.

Description

Technical Field

[0001] This application relates to the field of vehicle data management technology, and in particular to an intelligent management and testing device for vehicle data based on a large language model. Background Technology

[0002] AUTOSAR (Automotive Open System Architecture) is an international standard for developing automotive electronic systems. It achieves hardware and software decoupling through a layered design. Its core module, NvM (Non-Volatile Memory Manager), manages data read and write operations on non-volatile memory (such as EEPROM and Flash), ensuring that critical data is securely preserved even after power loss. In AUTOSAR, NvM is a core software module located in the service layer. It provides unified, reliable, and efficient non-volatile data management services to the application layer and achieves access isolation to the underlying complex storage hardware through hardware abstraction mechanisms.

[0003] With the increasing complexity of automotive electronic architecture, AUTOSAR's non-volatile memory, as the core of in-vehicle system data storage, faces a dramatic increase in the complexity of its configuration, verification, and management. Therefore, it is necessary to diagnose and identify anomalies in the management of in-vehicle data associated with NvM (Non-Volatile Memory) so that staff can promptly rectify any issues. Currently, test engineers spend a significant amount of time on average troubleshooting in-vehicle data management anomalies, and the accuracy and reliability of anomaly identification in this area are low. Summary of the Invention

[0004] This application provides an intelligent management and testing device for vehicle data based on a large language model, which solves the problems of the existing technology that takes a lot of time to manage vehicle data anomalies and has low accuracy and reliability in identifying anomalies in vehicle data management.

[0005] Firstly, this application provides an intelligent management method for vehicle-mounted data based on a large language model, applied to testing equipment. The method provided in this application includes: During the operation monitoring phase, while the vehicle is running, the operation logs of the vehicle's open system architecture non-volatile memory are acquired; Extract multiple key metrics features from the runtime logs; Based on a pre-defined large language model and an anomaly identification strategy corresponding to each key indicator feature, it is determined whether an anomaly has occurred in the operation log; if an anomaly exists, the type of the anomaly and its corresponding solution are determined. Output the types of exceptions and their corresponding solutions.

[0006] In some implementations, based on a pre-defined large language model and an anomaly detection strategy corresponding to each key indicator feature, it is determined whether an anomaly has occurred in the operation log; if an anomaly exists, the type of the anomaly and its corresponding solution are determined, including: Based on the preset large language model, when the error rate of the cyclic redundancy check of the running log reaches the set error rate, the type of the anomaly is determined to be that the error rate of the cyclic redundancy check is too high. The corresponding solution is to periodically read and write the running log in Flash once. Based on the preset large language model, when the average write time of each piece of vehicle data in the operation log is detected to be greater than the set time threshold, the anomaly is determined to be write latency, and the corresponding solution is to adjust the priority scheduling strategy of the task associated with non-volatile memory. Based on the preset large language model, if the number of erase / write operations of the Flash storage sector corresponding to the operation log exceeds the set upper limit, the anomaly type is determined to be storage sector aging. The corresponding solution is to migrate the operation log to the Flash backup sector when the vehicle is parked and the battery power is greater than the preset power threshold.

[0007] In some implementations, if an anomaly exists, the type of the anomaly and its corresponding solution are determined, including: If an anomaly is found, determine the type of anomaly that occurred; Based on the type of anomaly that occurred, determine whether at least one historical solution for the same type of anomaly that occurred in the past can be found; If at least one historical solution for the same type of anomaly can be found, then the historical solution with the highest score among the at least one historical solution is selected as the solution corresponding to the type of anomaly. If at least one historical solution for the same type of anomaly cannot be found, then a network retrieval tool is invoked to search for solutions corresponding to the type of anomaly in the target network.

[0008] In some embodiments, the method provided in this application further includes: During the stress testing phase, the system receives user input describing the test objectives associated with the system stress test based on a large language model, and generates executable test scripts from the descriptions of the test objectives. Execute the executable test script. When the test results indicate that there is a delay or failure in data writing, output the reasons for the delay or failure and the corresponding optimization suggestions.

[0009] In some embodiments, the method provided in this application further includes: During the system design phase, the data storage requirement description is received from the user based on the large language model. The data storage requirement description includes the name of the new data block, the size of the data block under the new data block name, and the vehicle functional domain associated with the new data block name. Check if there are any newly added data block names in the pre-configured storage specification. The storage specification includes multiple historical data block names and their corresponding storage sectors in Flash. If no new data block name exists, the storage area for the new data block name is determined based on the vehicle functional domain associated with the new data block name. Based on the data block size, determine the storage sector in the storage area for the name of the newly added data block; If the functional safety level of the newly added data block name is higher than the preset level threshold, and the storage sector stores data blocks with other historical data block names of the vehicle functional domain, and if the priority of the newly added data block name is higher than the priority of other historical data block names, then a suggestion will be output to migrate the data blocks with other historical data block names of the vehicle functional domain stored in the storage sector. After the user confirms the suggestion, a new storage specification is generated based on the storage sector in the storage area with the newly added data block name, as well as the updated storage sector associated with other historical data block names.

[0010] In some implementations, after determining the storage sector of the newly added data block name in the storage area based on the data block size, the method provided in this application further includes: If the functional safety level of the newly added data block name is lower than or equal to the preset level threshold and the storage sector is associated with other historical data block names of the vehicle functional domain, the original expected lifespan of the storage sector is determined based on the storage space size and remaining erasure / write cycles of the storage sector, as well as the data block size and read / write frequency of other data block names of the vehicle functional domain associated with the storage sector. The expected lifespan of a storage sector is determined based on the size of its storage space, the remaining number of erase / write cycles, the size of the data blocks under each data block name corresponding to the storage sector, and their read / write frequency. The data storage risk level of a storage sector is determined based on the size of its expected lifespan and the rate of change of its expected lifespan relative to the expected lifespan of the storage sector. If the data storage risk level of a storage sector is high, then the data blocks in the storage sector are stored redundantly. If the data storage risk level of a storage sector is medium risk, then the data blocks under the newly added data block name in the storage sector will be migrated to an empty storage sector.

[0011] In some implementations, after generating a new storage specification, the method provided in this application further includes: According to each data block name in the new storage specification, data is read / written to the storage sector corresponding to the data block name in Flash according to multiple preset read / write data methods. After the read / write data is completed, the actual feedback result is received from Flash. If the actual feedback result is inconsistent with the expected feedback result associated with the data reading / writing method, a test report will be output. The test report includes the test result that indicates the test failure and the corresponding reason for the test failure.

[0012] In some implementations, multiple preset methods for reading / writing data include: Read / write data blocks associated with data block names at normal read / write frequencies within a preset read / write frequency range; Read / write data blocks associated with data block names at the boundary read / write frequencies within a preset read / write frequency range; Read / write data blocks whose names are not associated with the data block name at normal read / write frequencies within the preset read / write frequency range.

[0013] In a second aspect, this application provides a test apparatus, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it causes the test apparatus to perform the method provided in the first aspect of this application.

[0014] Thirdly, this application also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the first aspect of this application.

[0015] This application provides an intelligent management and testing device for vehicle-mounted data based on a large language model. During the operation monitoring phase, while the vehicle is running, it acquires the operation logs from the non-volatile memory of the vehicle's open system architecture and extracts multiple key indicator features from the logs. Then, based on a preset large language model and an anomaly identification strategy corresponding to each key indicator feature, it determines whether an anomaly has occurred in the operation logs; if an anomaly exists, it determines the type of anomaly and its corresponding solution. Since the anomaly identification using the large language model based on the anomaly identification strategy corresponding to each key indicator feature is highly accurate, it saves significant time and manpower costs, resulting in high efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This application provides an embodiment of an intelligent management method for vehicle data based on a large language model during the operation monitoring phase. Figure 2 This application provides an intelligent management method for in-vehicle data based on a large language model during the stress testing phase. Figure 3 This application presents an intelligent management method for vehicle data based on a large language model during the system design phase. Detailed Implementation

[0018] Embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. Furthermore, descriptions of well-known structures and technologies are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0019] The accompanying drawings illustrate various structural schematics according to embodiments of the present disclosure. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0020] In the context of this disclosure, when a layer / element is referred to as being "above" another layer / element, the layer / element may be directly above the other layer / element, or there may be an intermediate layer / element between them. Additionally, if a layer / element is "above" another layer / element in one orientation, then when the orientation is reversed, the layer / element may be "below" the other layer / element.

[0021] The technical solutions of this application and how they solve the aforementioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0022] Please see Figure 1This application provides an intelligent management method for vehicle-mounted data based on a large language model, applied to testing equipment. The method provided in this application includes: S101: During the operation monitoring phase, while the vehicle is running, obtain the operation logs of the vehicle's open system architecture non-volatile memory.

[0023] S102: Extract multiple key indicator features from the operation log.

[0024] For example, multiple key metrics may include, but are not limited to, the error rate of cyclic redundancy check of the operation log, the average write time of each piece of vehicle data in the operation log, and the number of erase / write cycles of the flash storage sector corresponding to the operation log.

[0025] S103: Based on the preset large language model and the anomaly identification strategy corresponding to each key indicator feature, determine whether an anomaly has occurred in the running log; if so, execute S104.

[0026] Large Language Models (LLMs) are deep learning models trained on massive amounts of text data. These models can generate natural language text or understand the meaning of language text. By training on large datasets, they can provide in-depth knowledge and language production on various topics. The core idea is to learn patterns and structures of natural language through large-scale unsupervised training, mimicking human language cognition and generation processes to some extent. For example, a large language model can be trained by inputting multiple training samples into the large language network to be trained. Each training sample includes historical key indicator features, historical anomaly recognition strategies corresponding to the historical key indicator features, and historical actual operational anomalies from the corresponding historical logs.

[0027] S104: Determine the type of exception that occurred and its corresponding solution.

[0028] For example, as shown in Table 1 below, based on a preset large language model, if the error rate of cyclic redundancy check (CRUD) on the runtime log reaches a set error rate (such as 0.1% or 0.2%), the anomaly type can be determined as a high CRUD error rate. The corresponding solution is to periodically read and write the runtime log in Flash. It should be noted that if the runtime log missing rate exceeds 5% for a certain period, runtime log retransmission will be automatically triggered to avoid misjudgment of anomalies due to incomplete data caused by factors such as electromagnetic interference.

[0029] In addition, based on the preset large language model, if the average write time of each piece of vehicle data in the operation log is found to be greater than the set time threshold (such as 50ms or 75ms), the anomaly type can be determined to be write latency, and the corresponding solution is to adjust the priority scheduling strategy of the task associated with non-volatile memory.

[0030] Additionally, based on a pre-defined large language model, if the write / erase count of the Flash storage sector corresponding to the operation log exceeds a set upper limit (e.g., 90,000 or 85,000), the anomaly can be identified as storage sector aging. The corresponding solution is to migrate the operation log to the Flash backup sector when the vehicle is parked and the battery level is above a preset threshold (e.g., 20%). If the migration times out (e.g., exceeding 30 seconds), the operation log is rolled back to the original storage sector. When a decrease in the reliability of the operation log storage sector is detected, the operation log can be migrated to the Flash backup sector the next time the vehicle is parked and the battery level is above the preset threshold, thus improving the reliability of the storage sector.

[0031]

[0032] Table 1 Furthermore, step 104 above may also include: if an anomaly exists, determining the type of the anomaly; based on the type of the anomaly, determining whether at least one historical solution for the same type of anomaly can be found; if at least one historical solution for the same type of anomaly can be found, selecting the historical solution with the highest score among the at least one historical solution and determining it as the solution corresponding to the type of anomaly; if at least one historical solution for the same type of anomaly cannot be found, then invoking a network retrieval tool to search for the solution corresponding to the type of anomaly in the target network. In this way, the solution corresponding to the type of anomaly can be reliably and accurately determined.

[0033] S105: Output the type of exception and its corresponding solution.

[0034] In addition, in some implementations, such as Figure 2 As shown, the method provided in this application embodiment further includes: S201: During the stress testing phase, the system receives user input describing the test objectives associated with the system stress test based on a large language model, and generates an executable test script from the description of the test objectives.

[0035] For example, a test objective description associated with system stress testing could be, but is not limited to, "simulating continuous concurrent writes to all NvM data blocks over 60 seconds." The large language model can generate an executable test script corresponding to "simulating continuous concurrent writes to all NvM data blocks over 60 seconds."

[0036] S202: Execute the executable test script.

[0037] S203: When the test results indicate that there is a delay or failure in data writing, output the reasons for the delay or failure and the corresponding optimization suggestions.

[0038] For example, a large language model can analyze log features such as task scheduling status, CPU load, NvM queue depth, FEE operation time, and Flash status when executing executable test scripts, thereby determining whether the test results indicate that there is a delay or failure in data writing.

[0039] Based on S201-S203 above, during the stress testing phase, executable test scripts can be automatically generated, and the reasons for write delays or failures, along with corresponding optimization suggestions, can be automatically output. This saves significant manpower and time costs and ensures high reliability. It should be noted that the reasons for write delays or failures and the corresponding optimization suggestions are shown in Table 2 below.

[0040]

[0041] Table 2 In addition, such as Figure 3 As shown, the method provided in this application embodiment may further include: S301: During the system design phase, a description of the data storage requirements for receiving user input based on a large language model.

[0042] The data storage requirements description includes the name of the new data block, the size of the data block under the new data block name, and the vehicle functional domain associated with the new data block name.

[0043] For example, the data storage requirement description can be, but is not limited to, "Add a 4kb SOC data storage for the new power domain battery, requiring writing once every 100ms and no data loss upon power failure"; then the name of the new data block is SOC data, the vehicle functional domain associated with the new data block name is the power domain, and the size of the data block under the new data block name is 4kb.

[0044] S302: Check if there are any newly added data block names in the pre-configured storage specification. The storage specification includes multiple historical data block names and their corresponding storage sectors in Flash. If not, proceed to S303.

[0045] S303: Determine the storage area for the newly added data block name based on the vehicle functional domain associated with the newly added data block name.

[0046] Each vehicle functional domain is mapped to a storage area. For example, the vehicle body domain is mapped to storage area A, the vehicle power domain is mapped to storage area B, and so on.

[0047] S304: Determine the storage sector in the storage area for the name of the newly added data block based on the data block size.

[0048] For example, identify storage sectors with remaining storage space that meet the data block size and do not conflict with already stored data.

[0049] S305: If the functional safety level of the newly added data block name is higher than the preset level threshold, and the storage sector stores data blocks with other historical data block names of the vehicle functional domain, and if the priority of the newly added data block name is higher than the priority of other historical data block names, then a suggestion is output to migrate the data blocks with other historical data block names of the vehicle functional domain stored in the storage sector.

[0050] For example, if the priority of SOC data is ASILB level and ASILB level is higher than the preset level threshold, and motor temperature data is stored in the storage sector, and the priority of SOC data is higher than the priority of motor temperature data, then a suggestion to migrate the motor temperature data out of the storage sector will be output.

[0051] S306: After the user confirms the suggestion, a new storage specification is generated based on the storage sector in the storage area with the newly added data block name, as well as the updated storage sector associated with other historical data block names.

[0052] Based on S301-S306 above, during the system design phase, the stored data can be accurately and reliably diagnosed automatically, and suggestions for data adjustment can be generated, saving manpower and time costs.

[0053] The storage specifications can be shown in Table 3 below.

[0054]

[0055] Table 3 Furthermore, following S306 above, the method provided in this application embodiment may further include: sequentially reading / writing data to the storage sector corresponding to the data block name in Flash according to each data block name in the new storage specification, and following multiple preset read / write data methods; after the read / write data is completed, receiving the actual feedback result from Flash. If the actual feedback result is inconsistent with the expected feedback result associated with the read / write data method, a test report is output, which includes the test result indicating a test failure and the corresponding reason for the test failure.

[0056] In this way, the accuracy of the configuration in the new storage specification can be ensured. If the actual feedback results are inconsistent with the expected feedback results associated with the read / write data method, the testers can adjust the configuration in the new storage specification based on the test results that characterize the test failure and the corresponding reasons for the test failure.

[0057] For example, the multiple preset read / write data methods include, but are not limited to: reading / writing data blocks associated with data block names at normal read / write frequencies within a preset read / write frequency range; reading / writing data blocks associated with data block names at boundary read / write frequencies within a preset read / write frequency range; and reading / writing data blocks not associated with data block names at normal read / write frequencies within a preset read / write frequency range.

[0058] In addition, following S304 described above, the method provided in this application embodiment further includes: Step 1: If the functional safety level of the newly added data block name is lower than or equal to the preset level threshold, and the storage sector is associated with other historical data block names in the vehicle functional domain, determine the original expected lifespan of the storage sector based on the storage space size and remaining erasure / write cycles of the storage sector, as well as the data block size and read / write frequency under other data block names in the vehicle functional domain associated with the storage sector.

[0059] Step 2: Determine the expected lifespan of the storage sector based on the storage space size and remaining erase / write cycles of the storage sector, as well as the size and read / write frequency of the data blocks under each data block name corresponding to the storage sector.

[0060] Step 3: Determine the data storage risk level of the storage sector based on the expected lifespan of the update and the rate of change of the expected lifespan of the update relative to the expected lifespan of the update.

[0061] Step 4: If the data storage risk level of the storage sector is high risk, then redundant storage is performed on the data blocks in the storage sector.

[0062] Step 5: If the data storage risk level of the storage sector is medium risk, then migrate the data blocks under the newly added data block name in the storage sector to an empty storage sector.

[0063] Based on steps 1-5 above, when mapping new data block names to storage sectors, the lifespan of the storage sector can be predicted, and the storage strategy for new data block names can be adjusted according to the lifespan of the storage sector, thus ensuring the security and reliability of data storage.

[0064] For example, the risk levels and corresponding response strategies for different storage sectors can be shown in Table 4 below.

[0065]

[0066] Table 4 In summary, this application provides an intelligent management method for vehicle data based on a large language model. During the operation monitoring phase, while the vehicle is running, the operation logs of the vehicle's open system architecture (OSA) non-volatile memory are acquired, and multiple key indicator features are extracted from the logs. Then, based on a preset large language model and an anomaly identification strategy corresponding to each key indicator feature, it is determined whether an anomaly has occurred in the operation logs. If an anomaly exists, the type of anomaly and its corresponding solution are determined. Since the anomaly identification using the large language model based on the anomaly identification strategy corresponding to each key indicator feature is highly accurate and saves significant time and manpower costs, it is highly efficient. Furthermore, during the system design phase, it can accurately and reliably perform automated diagnosis of stored data and generate data adjustment suggestions, saving manpower and time costs. During the stress testing phase, it can automatically generate executable test scripts and automatically output the reasons for write delays or failures and corresponding optimization suggestions, saving significant manpower and time costs and ensuring high reliability. Thus, it achieves automated monitoring and testing of the entire AUTOSAR NvM lifecycle, greatly reducing costs and improving efficiency.

[0067] In addition, this application provides a test device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the test device performs the method provided in the above embodiments of this application.

[0068] In addition, this application embodiment also provides a storage medium storing a computer program, which, when executed by a processor, causes the computer to perform the method provided in the above embodiments of this application.

[0069] The above description does not provide detailed technical specifications regarding the structure of each layer. However, those skilled in the art should understand that layers and regions of desired shapes can be formed using various technical means. Furthermore, to form the same structure, those skilled in the art can also design methods that are not entirely identical to those described above. Additionally, although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be advantageously combined.

[0070] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0071] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An intelligent management method for vehicle-mounted data based on a large language model, characterized in that, Applied to a testing device, the method includes: During the operation monitoring phase, while the vehicle is running, the operation logs of the vehicle's open system architecture non-volatile memory are acquired. Extract multiple key indicator features from the operation log; Based on a pre-defined large language model and an anomaly identification strategy corresponding to each key indicator feature, determine whether the operation log has an anomaly; if an anomaly exists, determine the type of the anomaly and its corresponding solution. Output the type of the exception and its corresponding solution.

2. The method according to claim 1, characterized in that, The pre-set large language model determines whether the operation log has an anomaly based on the anomaly identification strategy corresponding to each of the key indicator features; If an anomaly exists, determine the type of the anomaly and its corresponding solution, including: Based on the preset large language model, if the error rate of the cyclic redundancy check of the running log reaches the set error rate, the type of the anomaly is determined to be that the error rate of the cyclic redundancy check is too large. The corresponding solution is to periodically read and write the running log in Flash once. Based on the preset large language model, if the average write time of each piece of vehicle data in the operation log is found to be greater than the set time threshold, the anomaly type is determined to be write latency, and the corresponding solution is to adjust the priority scheduling strategy of the task associated with the non-volatile memory. Based on a preset large language model, if the number of erase / write operations of the Flash storage sector corresponding to the operation log exceeds the set upper limit, the abnormality is determined to be storage sector aging. The corresponding solution is to migrate the operation log to the backup sector of the Flash when the vehicle is parked and the battery power is greater than a preset power threshold.

3. The method according to claim 1, characterized in that, If an anomaly exists, the method of determining the type of the anomaly and its corresponding solution includes: If an anomaly is found, determine the type of anomaly that occurred; Based on the type of anomaly that occurred, determine whether at least one historical solution for the same type of anomaly that occurred in the past can be found; If at least one historical solution for the same type of anomaly can be found, then the historical solution with the highest score among the at least one historical solutions is selected as the solution corresponding to the type of anomaly. If at least one historical solution for the same type of anomaly cannot be found, then a network retrieval tool is invoked to search for a solution corresponding to the type of anomaly in the target network.

4. The method according to claim 1, characterized in that, The method further includes: During the stress testing phase, the system receives user input describing the test objectives associated with the system stress test based on the large language model, and generates an executable test script from the description of the test objectives. Execute the executable test script. When the test results indicate that there is a delay or failure in data writing, output the reasons for the delay or failure and the corresponding optimization suggestions.

5. The method according to claim 1, characterized in that, The method further includes: During the system design phase, the system receives a data storage requirement description input by the user based on the large language model. The data storage requirement description includes the name of the newly added data block, the size of the data block under the name of the newly added data block, and the vehicle functional domain associated with the name of the newly added data block. Check whether the newly added data block name exists in the pre-configured storage specification, wherein the storage specification includes multiple historical data block names and their corresponding storage sectors in Flash; If the newly added data block name does not exist, the storage area of ​​the newly added data block name is determined according to the vehicle functional domain associated with the newly added data block name; Based on the data block size, determine the storage sector in the storage area where the newly added data block name is located; If the functional safety level of the newly added data block name is higher than the preset level threshold, and the storage sector stores data blocks with other historical data block names of the vehicle functional domain, and if the priority of the newly added data block name is higher than the priority of the other historical data block names, then a suggestion to migrate the data blocks with other historical data block names of the vehicle functional domain stored in the storage sector will be output. After the user confirms the suggestion, a new storage specification is generated based on the storage sector in the storage area with the newly added data block name, and the updated storage sector associated with the other historical data block names.

6. The method according to claim 5, characterized in that, After determining the storage sector in the storage area where the newly added data block name is located based on the data block size, the method further includes: If the functional safety level of the newly added data block name is lower than or equal to the preset level threshold and the storage sector is associated with other historical data block names of the vehicle functional domain, the original expected service life of the storage sector is determined based on the storage space size and remaining erasure / write cycles of the storage sector, as well as the data block size and read / write frequency of other data block names of the vehicle functional domain associated with the storage sector. The expected lifespan of the storage sector is determined based on the size of the storage space of the storage sector, the remaining number of erase and write cycles, and the size and read / write frequency of the data blocks under each data block name corresponding to the storage sector. The data storage risk level of the storage sector is determined based on the magnitude of the expected update lifespan and the rate of change of the expected update lifespan of the storage sector relative to the expected update lifespan. If the data storage risk level of the storage sector is high risk, then the data blocks in the storage sector are stored redundantly. If the data storage risk level of the storage sector is medium risk, then the data blocks under the newly added data block name in the storage sector will be migrated to an empty storage sector.

7. The method according to claim 5, characterized in that, After generating the new storage specification, the method further includes: According to each data block name in the new storage specification, data is read / written to the storage sector corresponding to the data block name in the Flash according to multiple preset read / write data methods. After the read / write data is completed, the actual feedback result is received from the Flash. If the actual feedback result is inconsistent with the expected feedback result associated with the read / write data method, a test report is output. The test report includes the test result indicating the test failure and the corresponding reason for the test failure.

8. The method according to claim 7, characterized in that, The multiple preset methods for reading / writing data include: Read / write the data block associated with the data block name at a normal read / write frequency within a preset read / write frequency range; Read / write data blocks associated with the data block name at the boundary read / write frequency of the preset read / write frequency range; At a normal read / write frequency within a preset read / write frequency range, read / write data blocks that are not associated with the data block name.

9. A testing device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it causes the test device to perform the method as described in any one of claims 1 to 8.

10. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the computer to perform the method as described in any one of claims 1 to 8.

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