Automobile diagnosis authenticity and abnormal data determination method, device, equipment and medium

Abstract

The present application relates to the technical field of information data processing, and particularly relates to a vehicle diagnosis authenticity and abnormality data determination method, device, equipment and medium. The vehicle diagnosis authenticity and abnormality data determination method provided by the present application collects information data generated by a vehicle diagnosis device and transmits the information data to a cloud platform, performs data cleaning on the information data, divides the diagnosis information data into valid information data and abnormal information data, classifies the valid information data, deletes or repairs the abnormal information data, and stores the valid information data according to categories. Compared with the prior art, the present application screens out abnormal data by collecting and cleaning massive data, and then classifies and stores the valid data.

Description

[0001] This application is a divisional application of the invention patent application filed on July 19, 2022, with application number 2022108484962 and titled "A Method, Apparatus, Device and Medium for Processing Automotive Diagnostic Data". Technical Field

[0002] This invention relates to the field of information data processing technology, and in particular to a method, apparatus, equipment and medium for determining the authenticity and abnormality of vehicle diagnostic data. Background Technology

[0003] Automotive diagnostic equipment consists of non-destructive testing and inspection machines that can quickly and accurately determine the technical condition of a vehicle, its assemblies, and mechanisms, and arrive at reliable conclusions. These mainly include wheel balancers, headlight testers, reaction brake test benches, chassis dynamometers, sideslip test benches, front wheel alignment machines, engine test benches, exhaust gas analyzers, exhaust smoke meters, and noise meters. During the process of users using diagnostic equipment to diagnose vehicles, massive amounts of diagnostic and user data are generated. Therefore, the effective and rational management of this vast amount of diagnostic data is becoming increasingly important.

[0004] While existing technologies, such as Chinese Patent Publication No. CN108228744A, disclose a method and apparatus for managing vehicle diagnostic data, specifically disclosing the process of receiving vehicle diagnostic data submitted by an on-board device; structuring and storing the diagnostic data according to preset rules; extracting feature values ​​from the vehicle diagnostic data and generating a data index based on the feature values; registering the data index in a blockchain; receiving user data usage requests; and sending corresponding diagnostic data to the user according to the user type, this invention ensures the reliability of vehicle diagnostic data by sending corresponding diagnostic data according to different users, thus enabling users to accurately obtain the vehicle diagnostic data they need. However, it does not provide a solution for managing massive amounts of vehicle diagnostic data and cleaning erroneous data. Furthermore, in the process of cleaning numerous erroneous data, how to distinguish, identify, and verify erroneous data is a major challenge. Continuously verifying erroneous data would severely consume equipment resources and affect the smooth operation of the equipment. Summary of the Invention

[0005] To address the shortcomings of the existing technology, this invention provides a method for determining the authenticity and abnormality of automotive diagnostic data, comprising the following steps:

[0006] S10: Collect information data generated by the vehicle diagnostic equipment and transmit the information data to the cloud platform;

[0007] S20: Perform data cleaning on the information data, and divide the information data into valid information data and abnormal information data;

[0008] S30: Classify the valid information data, delete or repair the abnormal information data; collect the abnormal information data, and divide the abnormal information data into true abnormal information data and false abnormal information data according to preset rules; the true abnormal information data is the abnormal information data that is actually diagnosed; the false abnormal information data is the abnormal information data generated due to transmission communication software and hardware failure.

[0009] The device source of the pseudo-abnormal information data issues a command to retransmit the information data in order to reacquire the original information data;

[0010] The original information data is compared and analyzed with the pseudo-abnormal information data. If they are consistent, it is judged as true abnormal information data; otherwise, it is judged as deterministic pseudo-abnormal information data.

[0011] The communication links that generate deterministic pseudo-anomaly information data are investigated and repaired for hardware and software faults, and alternative communication links are selected to retransmit the information data.

[0012] The number of times the authenticity of abnormal information data is judged is counted. If the newly acquired data is compared with the original false abnormal data and it is found that the data is true more than N times, then the judgment of authenticity of abnormal data is paused. The judgment is restarted after the number of abnormal data occurrences exceeds a certain number.

[0013] The vehicle diagnostic device is equipped with a cache, which is used to save the information data uploaded to the cloud platform within a preset time. After receiving the instruction to retransmit the information data, the vehicle diagnostic device re-uploads the information data in the cache to the cloud platform.

[0014] S40: Store the valid information data according to categories.

[0015] In one embodiment, the preset rules for judging the authenticity of abnormal information data are modified after the number of false judgments exceeds a certain limit.

[0016] In one embodiment, the cloud platform in step S10 connects to the automotive diagnostic device via the WebSock protocol, and the information data includes vehicle diagnostic data generated during the use of the automotive diagnostic device and behavioral data that the user is allowed to upload.

[0017] In one embodiment, step S30 includes the following steps:

[0018] S31: Detect the information data and screen out information data that does not conform to the rules as abnormal information data;

[0019] S32: Repair the abnormal information data and delete the abnormal information data that still does not conform to the rules after the repair process;

[0020] S33: Check whether the repaired abnormal information data conforms to the rules. If it conforms to the rules, restore it to valid information data.

[0021] In one embodiment, the preset rules include:

[0022] A threshold is set for the size of the space occupied by the abnormal information data. If the size of the abnormal information data exceeds the threshold, it is classified as pseudo-abnormal information data; or

[0023] Determine whether there are several abnormal information data that are continuously generated within a set time period. If so, classify the several abnormal information data into pseudo abnormal information data.

[0024] In one embodiment, step S30 involves classifying the valid information data, including classifying by source, classifying by time sequence, or classifying by time segment.

[0025] The present invention also provides an automotive diagnostic data processing device, comprising:

[0026] The acquisition unit is used to acquire information data generated by automotive diagnostic equipment and transmit the information data to the cloud platform.

[0027] The cleaning unit performs data cleaning on the information data, dividing the information data into valid information data and abnormal information data.

[0028] The classification unit classifies the valid information data and deletes or repairs the abnormal information data; it collects the abnormal information data and divides it into true abnormal information data and false abnormal information data according to preset rules; the true abnormal information data is the abnormal information data that has been actually diagnosed; the false abnormal information data is the abnormal information data caused by transmission communication software and hardware failures.

[0029] The device source of the pseudo-abnormal information data issues a command to retransmit the information data in order to reacquire the original information data;

[0030] The original information data is compared and analyzed with the pseudo-abnormal information data. If they are consistent, it is judged as true abnormal information data; otherwise, it is judged as deterministic pseudo-abnormal information data.

[0031] The communication links that generate deterministic pseudo-anomaly information data are investigated and repaired for hardware and software faults, and alternative communication links are selected to retransmit the information data.

[0032] The number of times the authenticity of abnormal information data is judged is counted. If the newly acquired data is compared with the original false abnormal data and it is found that the data is true more than N times, then the judgment of authenticity of abnormal data is paused. The judgment is restarted after the number of abnormal data occurrences exceeds a certain number.

[0033] The vehicle diagnostic device is equipped with a cache, which is used to save the information data uploaded to the cloud platform within a preset time. After receiving the instruction to retransmit the information data, the vehicle diagnostic device re-uploads the information data in the cache to the cloud platform.

[0034] The storage unit stores the valid information data according to categories.

[0035] The present invention also provides a computer device, including at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the processor to perform a method for determining the authenticity of abnormal vehicle diagnostic data as described in any of the preceding claims.

[0036] Based on the above, the present invention provides a method for determining the authenticity of abnormal data in automotive diagnostics. This method uses preset rules to judge the authenticity of abnormal data, comparing and analyzing the original data with pseudo-abnormal data to identify either genuine or definitively pseudo-abnormal data. Simultaneously, the number of times the abnormal data is judged is counted. If the judgment results are all genuine abnormal data after a set number of judgments, the judgment process is paused; the judgment is resumed only after a certain number of abnormal data occurrences. This effectively saves equipment resources and improves the smoothness of equipment operation.

[0037] Other features and beneficial effects of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other beneficial effects of the invention can be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments of the present invention 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Unless otherwise specified, the positional relationships shown in the drawings in the following description are based on the direction in which the components are drawn in the figure.

[0039] Figure 1This is a schematic diagram of the method for determining the authenticity and abnormality of vehicle diagnostic data provided by the present invention.

[0040] Figure 2 This is a schematic diagram of step S30 in an embodiment of the present invention;

[0041] Figure 3 This is a schematic diagram illustrating the further processing flow of abnormal information data in an embodiment of the present invention;

[0042] Figure 4 This is a schematic diagram of the vehicle diagnostic data authenticity and anomaly determination device according to an embodiment of the present invention;

[0043] Figure 5 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The technical features designed in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0045] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integrally formed connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0046] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments. Unless the context clearly indicates otherwise, the singular forms “a” and “an” as used herein are also intended to include the plural. It should also be understood that the terms “comprising” and / or “including” as used herein specify the presence of the stated features, integers, steps, operations, units, and / or components, without excluding the presence or addition of one or more other features, integers, steps, operations, units, components, and / or combinations thereof.

[0047] To achieve at least one or more advantages, such as Figure 1 As shown, the present invention provides a method for determining the authenticity and abnormality of automotive diagnostic data, comprising the following steps:

[0048] S10: Collect information data generated by the vehicle diagnostic equipment and transmit the information data to the cloud platform;

[0049] S20: Perform data cleaning on the information data, and divide the information data into valid information data and abnormal information data;

[0050] S30: Classify the valid information data, and delete or repair the abnormal information data;

[0051] S40: Store the valid information data according to categories.

[0052] A cloud platform, also known as a cloud computing platform, refers to a service that provides computing, networking, and storage capabilities based on hardware and software resources. Cloud computing platforms can be divided into three categories: storage-based cloud platforms, primarily focused on data storage; computing-based cloud platforms, primarily focused on data processing; and comprehensive cloud computing platforms that balance computing, data storage, and processing. In this embodiment, a self-developed IM data platform is used, but it is not limited to this; the cloud platform can also utilize other existing platforms such as Tencent Cloud and Alibaba Cloud.

[0053] Furthermore, to preserve the integrity of received data as much as possible and avoid wasting data resources, this invention further processes the data classified as abnormal information. Some of this abnormal information is caused by hardware or software issues in the transmission link, while some is simply useless garbled data. Directly deleting the former would actually waste data resources. Therefore... Figure 3 As shown, in some embodiments, step S30 of the present invention further includes:

[0054] The abnormal information data is collected and divided into true abnormal information data and false abnormal information data according to preset rules; the true abnormal information data is the abnormal information data that is actually diagnosed; the false abnormal information data is the abnormal information data generated due to transmission communication software and hardware failure.

[0055] The device source of the pseudo-abnormal information data issues a command to retransmit the information data in order to reacquire the original information data;

[0056] The original information data is compared and analyzed with the pseudo-abnormal information data. If they are consistent, it is judged as true abnormal information data; otherwise, it is judged as pseudo-abnormal information data.

[0057] The communication links that generate false abnormal information data are investigated and repaired for hardware and software faults, and alternative communication links are selected for retransmission of the information data.

[0058] By further classifying and judging the authenticity of abnormal information data, false abnormal data can be screened out and restored and verified by re-acquiring data. On the one hand, the integrity of the information data itself is preserved, and on the other hand, the transmission link can be repaired, realizing the self-inspection and self-repair of the transmission link.

[0059] Preferably, in some embodiments, the number of times the authenticity of abnormal information data is judged can be counted. If, after comparing the re-acquired data with the original false abnormal data, it is found that more than N times the data is genuinely abnormal, then the judgment of authenticity of abnormal data is paused; the judgment is restarted only after the number of abnormal data occurrences exceeds a certain limit. This avoids wasting resources due to a large number of false judgments. Furthermore, the preset rules for judging the authenticity of abnormal information data can be modified after the number of false judgments exceeds a certain limit.

[0060] In some embodiments, the vehicle diagnostic device is equipped with a cache, which is used to save the information data uploaded to the cloud platform for a preset time. After receiving the instruction to retransmit the information data, the vehicle diagnostic device re-uploads the information data in the cache to the cloud platform.

[0061] In some embodiments, the cloud platform in step S10 connects to the automotive diagnostic device via the WebSock protocol, and the information data includes vehicle diagnostic data generated during the use of the automotive diagnostic device and behavioral data that the user is allowed to upload.

[0062] Generally, vehicle diagnostic data includes diagnostic information obtained through testing equipment such as wheel balancers, headlight testers, reaction brake test benches, chassis dynamometers, sideslip test benches, front wheel alignment machines, engine test benches, exhaust gas analyzers, exhaust smoke meters, and noise meters.

[0063] User behavior data includes, for example, user attribute data, user behavior data, and user transaction data. The elements constituting user behavior data include, for example, the "5+1" elements: Who: In addition to basic user information, this includes information about the user's access terminal, such as mobile devices (phone number, device ID, openID, unionID), PCs (cookies, MAC address), cameras, and IoT devices; When: The time, duration, frequency, and recurrence of the behavior; Where: Offline (latitude and longitude, displacement, altitude), online (user source channel, user's page location); What: Content (images, videos, audio), products, functions (subdivided layer by layer according to sub-modules); How: Content interaction, product interaction (browsing, adding to cart, reservation, retention, placing orders, payment, etc.), and function interaction; How much: For example, transaction data such as order amount, order type, and product details. In short: Who, at what time, in what place, did what content, and what results were generated?

[0064] Focusing on user behavior has two key benefits. First, it helps understand user needs and guide business upgrades. This includes creating user profiles, inferring user preferences, and segmenting the user lifecycle. By combining user lifecycle stages with user profiles, we can determine the true needs of similar users for products. Furthermore, user journey maps can help identify business bottlenecks. For example, how can we design new user onboarding to quickly guide users to their "Aha" moment? How can we ensure that best-selling products appropriately capture users' attention? How can we ensure that existing orders are quickly viewed and conveniently encouraged to "order again"? This also helps us assess the closeness of the relationship between users and the business, and improve user journey maps, such as through refined operations. Second, it helps predict user behavior and guide business innovation. By analyzing existing user behavior, we can predict user choices or guide user choices through various activities. Ideally, this should be based on a data loop rather than relying on external data tags, aiming to achieve "precise" and "high-frequency" marketing.

[0065] During the data cleaning process, information data can be filtered using relatively broad preset rules. For example, preset rules can be set to classify data that does not conform to the rules, such as garbled text or data containing special characters, as abnormal information data.

[0066] Specifically, such as Figure 2 As shown, in some embodiments, step S30 includes the following steps:

[0067] S31: Detect the information data and screen out information data that does not conform to the rules as abnormal information data; the rules can be based on the judgment of a specific data format, such as classifying garbled text or data containing special characters as abnormal information data.

[0068] S32: Repair the abnormal information data, and delete any abnormal information data that still does not conform to the rules after the repair process; for example, perform conventional repair on garbled characters to make the encoding rules of the garbled characters compatible with the reading rules, and use common repair methods in existing technology to restore the garbled characters to readable and valid information data. This invention does not impose any special limitations on this. For abnormal information data that cannot be repaired, deletion or further processing can be selected.

[0069] S33: Check whether the repaired abnormal information data conforms to the rules. If it conforms to the rules, restore it to valid information data. After determining that the abnormal data conforms to the rules, change the abnormal information data from the abnormal information data category to the valid information category.

[0070] Preferably, the preset rules for judging the authenticity of abnormal data include: setting a threshold for the size of the space occupied by the abnormal information data; if the size of the abnormal information data exceeds the threshold, it is classified as the pseudo-abnormal information data; or judging whether there are several abnormal information data that are continuously generated within a set time period; if so, the several abnormal information data are classified as the pseudo-abnormal information data. However, this invention is not limited to these provisions and does not impose any special limitations. The preset rules can be set according to actual needs.

[0071] In some embodiments, step S30 classifies the valid information data, including classification by source, such as vehicle diagnostic data or user behavior data; classification by time sequence, which refers to data collected at different times, reflecting changes over time; or classification by time segment, which refers to data collected at the same or similar time points.

[0072] Furthermore, by visually representing the data stored according to categories, decision-makers can intuitively see various product and user data.

[0073] This invention also provides a device for determining the authenticity and abnormality of automotive diagnostic data, such as... Figure 4 As shown, it includes:

[0074] The acquisition unit is used to acquire information data generated by automotive diagnostic equipment and transmit the information data to the cloud platform.

[0075] The cleaning unit performs data cleaning on the information data, dividing the information data into valid information data and abnormal information data.

[0076] The classification unit classifies the valid information data and deletes or repairs the abnormal information data.

[0077] The storage unit stores the valid information data according to categories.

[0078] The present invention also provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement a method for determining the authenticity and abnormality of vehicle diagnostic data as described in any of the preceding claims.

[0079] In specific implementations, computer-readable storage media may include magnetic disks, optical disks, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drives (HDDs), or solid-state drives (SSDs); computer-readable storage media may also include combinations of the above types of memory.

[0080] The present invention also provides a computer device, such as Figure 5 As shown, it includes at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the processor to perform a method for determining the authenticity of abnormal vehicle diagnostic data as described in any of the preceding claims.

[0081] In practice, the number of processors can be one or more, and the processor can be a Central Processing Unit (CPU). The processor can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations of the above types of chips. A general-purpose processor can be a microprocessor or any conventional processor.

[0082] The memory and the processor can be connected to communicate via a bus or other means. The memory stores instructions that can be executed by at least one processor. The instructions are executed by at least one processor to cause the processor to perform any of the above methods for determining the authenticity of automotive diagnostic abnormal data.

[0083] Based on the above, the present invention provides a method for determining the authenticity of abnormal data in automotive diagnostics. This method uses preset rules to judge the authenticity of abnormal data, comparing and analyzing the original data with pseudo-abnormal data to identify either genuine or definitively pseudo-abnormal data. Simultaneously, the number of times the abnormal data is judged is counted. If the judgment results are all genuine abnormal data after a set number of judgments, the judgment process is paused; the judgment is resumed only after a certain number of abnormal data occurrences. This effectively saves equipment resources and improves the smoothness of equipment operation.

[0084] Furthermore, those skilled in the art should understand that although many problems exist in the prior art, each embodiment or technical solution of the present invention can be improved in only one or a few aspects, without necessarily solving all the technical problems listed in the prior art or the background art simultaneously. Those skilled in the art should understand that any content not mentioned in a claim should not be construed as a limitation on that claim.

[0085] Although this document frequently uses terms such as automotive diagnostic equipment, information data, cloud platform, data cleaning, valid information data, and abnormal information data, the possibility of using other terms is not excluded. These terms are used merely for the convenience of describing and explaining the essence of this invention; interpreting them as any additional limitation would contradict the spirit of this invention.

[0086] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for determining the authenticity and abnormality of automotive diagnostic data, characterized in that, Includes the following steps: S10: Collect information data generated by the vehicle diagnostic equipment and transmit the information data to the cloud platform; S20: Perform data cleaning on the information data, and divide the information data into valid information data and abnormal information data; S30: Classify the valid information data, and delete or repair the abnormal information data; Collect the abnormal information data and classify it into true abnormal information data and false abnormal information data according to preset rules; The true anomaly information data refers to the anomaly information data that has been actually diagnosed. The pseudo-abnormal information data is abnormal information data generated due to transmission communication software and hardware failures. The device source of the pseudo-abnormal information data issues a command to retransmit the information data in order to reacquire the original information data; The original information data is compared and analyzed with the pseudo-abnormal information data. If they are consistent, it is judged as true abnormal information data; otherwise, it is judged as deterministic pseudo-abnormal information data. The communication links that generate deterministic pseudo-anomaly information data are investigated and repaired for hardware and software faults, and alternative communication links are selected to retransmit the information data. The number of times the authenticity of abnormal information data is judged is counted. If the newly acquired data is compared with the original false abnormal data and it is found that more than N times the data is true abnormal data, then the judgment of authenticity of abnormal data is paused. The judgment will only be initiated after abnormal data is detected more than a certain number of times. The vehicle diagnostic device is equipped with a cache, which is used to save the information data uploaded to the cloud platform within a preset time. After receiving the instruction to retransmit the information data, the vehicle diagnostic device re-uploads the information data in the cache to the cloud platform. S40: Store the valid information data according to categories.

2. The method for determining the authenticity and abnormality of automotive diagnostic data according to claim 1, characterized in that: After the number of false positives exceeds a certain limit, the preset rules for judging the authenticity of abnormal information data are corrected.

3. The method for determining the authenticity and abnormality of automotive diagnostic data according to claim 1, characterized in that: In step S10, the cloud platform connects to the automotive diagnostic device via the WebSock protocol. The information data includes vehicle diagnostic data generated during the use of the automotive diagnostic device and behavioral data that the user is allowed to upload.

4. The method for determining the authenticity and abnormality of automotive diagnostic data according to claim 1, characterized in that: Step S30 includes the following steps: S31: Detect the information data and screen out information data that does not conform to the rules as abnormal information data; S32: Repair the abnormal information data and delete the abnormal information data that still does not conform to the rules after the repair process; S33: Check whether the repaired abnormal information data conforms to the rules. If it conforms to the rules, restore it to valid information data.

5. The method for determining the authenticity and abnormality of automotive diagnostic data according to claim 1, characterized in that: The preset rules include: A threshold is set for the size of the space occupied by the abnormal information data. If the size of the abnormal information data exceeds the threshold, it is classified as pseudo-abnormal information data; or Determine whether there are several abnormal information data that are continuously generated within a set time period. If so, classify the several abnormal information data into pseudo abnormal information data.

6. The method for determining the authenticity and abnormality of automotive diagnostic data according to claim 1, characterized in that: In step S30, the valid information data is classified, including by source, by time sequence, or by time segment.

7. A device for determining the authenticity and abnormality of automotive diagnostic data, characterized in that, include: The acquisition unit is used to acquire information data generated by automotive diagnostic equipment and transmit the information data to the cloud platform. The cleaning unit performs data cleaning on the information data, dividing the information data into valid information data and abnormal information data. The classification unit classifies the valid information data and deletes or repairs the abnormal information data. Collect the abnormal information data and classify it into true abnormal information data and false abnormal information data according to preset rules; The true anomaly information data refers to the anomaly information data that has been actually diagnosed. The pseudo-abnormal information data is abnormal information data generated due to transmission communication software and hardware failures. The device source of the pseudo-abnormal information data issues a command to retransmit the information data in order to reacquire the original information data; The original information data is compared and analyzed with the pseudo-abnormal information data. If they are consistent, it is judged as true abnormal information data; otherwise, it is judged as deterministic pseudo-abnormal information data. The communication links that generate deterministic pseudo-anomaly information data are investigated and repaired for hardware and software faults, and alternative communication links are selected to retransmit the information data. The number of times the authenticity of abnormal information data is judged is counted. If the newly acquired data is compared with the original false abnormal data and it is found that more than N times the data is true abnormal data, then the judgment of authenticity of abnormal data is paused. The judgment will only be initiated after abnormal data is detected more than a certain number of times. The vehicle diagnostic device is equipped with a cache, which is used to save the information data uploaded to the cloud platform within a preset time. After receiving the instruction to retransmit the information data, the vehicle diagnostic device re-uploads the information data in the cache to the cloud platform. The storage unit stores the valid information data according to categories.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, which, when executed by a processor, implement a method for determining the authenticity and abnormality of vehicle diagnostic data as described in any one of claims 1-5.

9. A computer device, characterized in that: The device includes at least one processor and a memory communicatively connected to the processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to cause the processor to perform a method for determining the authenticity of abnormal vehicle diagnostic data as described in any one of claims 1-5.

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