Vehicle group fault positioning method and device based on Internet of Vehicles

Abstract

The invention provides a vehicle group fault positioning method and device based on the Internet of Vehicles. The method comprises the following steps: acquiring and analyzing operation data and environment data of a target vehicle in real time; if the target vehicle has the suspected fault, generating a suspected fault feature vector of the target vehicle; obtaining suspected fault feature vectors shared by vehicles of the same type in the same time period within a preset range of the position of the target vehicle based on the Internet of Vehicles; calculating the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the suspected fault feature vector set; and if the number of similarities greater than or equal to the similarity threshold is greater than a preset number, determining that the target vehicle is suspected to have a group fault. Information interaction with surrounding vehicles is carried out based on the Internet of Vehicles, analysis is carried out from multiple dimensions of operation data and environmental data, the relevance between the vehicles and the influence of environmental factors are fully considered, and rapid positioning of vehicle group faults is facilitated.

Description

Technical Field

[0001] This invention relates to the field of vehicle fault diagnosis technology, and in particular to a method and device for locating group vehicle faults based on the Internet of Vehicles. Background Technology

[0002] The Internet of Vehicles (IoV) uses automobiles as mobile terminals and leverages advanced technologies such as wireless communication, satellite positioning, sensors, cloud computing, big data, and artificial intelligence to achieve comprehensive connectivity and data interaction between vehicles, people, roads, and cloud platforms, thus building an efficient, safe, intelligent, and environmentally friendly intelligent transportation ecosystem. Thanks to the powerful data transmission and processing capabilities of the IoV, vehicle fault diagnosis technology based on the IoV has become possible.

[0003] Current vehicle fault diagnosis methods based on the Internet of Vehicles (IoV) primarily focus on individual vehicles, neglecting the interrelationships between vehicles and the influence of environmental factors. They are heavily reliant on basic data; incomplete or inaccurate data, or insufficient representativeness of the data samples, can affect the accuracy of the classification model, leading to misjudgments or omissions of potential fault risks. This is particularly problematic when multiple similar or related vehicles exhibiting cluster faults with identical or similar characteristics within a certain timeframe struggle to be quickly identified, failing to rapidly uncover the correlation between faults across multiple vehicles. Therefore, a rapid localization method for cluster vehicle faults is urgently needed. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes a method and device for locating group vehicle faults based on the Internet of Vehicles.

[0005] In a first aspect, embodiments of the present invention provide a method and apparatus for locating group vehicle faults based on the Internet of Vehicles (IoV), comprising:

[0006] Real-time acquisition of target vehicle's operational and environmental data;

[0007] The system analyzes the target vehicle's operating data and environmental data based on a pre-defined fault feature database to determine whether the target vehicle has any suspected faults.

[0008] If the target vehicle has a suspected fault, a suspected fault feature vector of the target vehicle is generated based on the target vehicle's operating data and environmental data.

[0009] Based on the Internet of Vehicles, suspected fault feature vectors shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period are obtained, forming a set of suspected fault feature vectors.

[0010] Calculate the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the set of suspected fault feature vectors;

[0011] If the number of similarities greater than or equal to the similarity threshold is greater than the preset number, then the target vehicle is suspected of having a group malfunction.

[0012] Optionally, the method further includes:

[0013] Send the suspected fault feature vector of the target vehicle to the cloud platform;

[0014] The cloud platform uses clustering algorithms to perform clustering analysis on the received suspected fault feature vectors;

[0015] If the number of suspected fault feature vectors in the same cluster is greater than or equal to the threshold for determining a group fault, and the time and location of each suspected fault feature vector in the cluster are correlated, then a group fault is confirmed to exist.

[0016] Optionally, the method further includes:

[0017] Determine the correlation between mass failures and vehicle parameters, environmental factors, and driving habits;

[0018] Fault tree analysis and Bayesian networks are used to locate the causes of mass failures;

[0019] A location report for the cluster failure is generated based on the correlation and cause, and the location report is sent to the target vehicle.

[0020] Optionally, the method further includes:

[0021] Update the fault feature database based on the location report.

[0022] Optionally, before analyzing the target vehicle's operating data and environmental data based on a preset fault feature database, the method further includes:

[0023] The target vehicle's operational and environmental data are cleaned, converted in format, and compressed.

[0024] Optionally, before the cloud platform performs clustering analysis on the received suspected fault feature vectors using a clustering algorithm, the method further includes:

[0025] The cloud platform combines its stored historical fault data, vehicle information, and environmental data to perform multi-dimensional data fusion on the received suspected fault feature vectors.

[0026] Optionally, the suspected fault feature vector of the target vehicle is sent to the cloud platform, including:

[0027] In response to a user's command to report a suspected fault, the suspected fault feature vector of the target vehicle is sent to the cloud platform.

[0028] Secondly, embodiments of the present invention provide a vehicle group fault location device based on the Internet of Vehicles, comprising:

[0029] The first acquisition module is used to acquire the target vehicle's operating data and environmental data in real time;

[0030] The analysis module is used to analyze the operating data and environmental data of the target vehicle based on a preset fault feature library to determine whether the target vehicle has any suspected faults.

[0031] The generation module is used to generate a suspected fault feature vector of the target vehicle based on the target vehicle's operating data and environmental data if the target vehicle has a suspected fault.

[0032] The second acquisition module is used to acquire suspected fault feature vectors shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period based on the vehicle network, and form a set of suspected fault feature vectors.

[0033] The calculation module is used to calculate the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the set of suspected fault feature vectors.

[0034] The processing module is used to determine that the target vehicle is suspected of having a group malfunction if the number of similarities greater than or equal to the similarity threshold is greater than a preset number.

[0035] Thirdly, the present invention provides an electronic device including a processor and a memory, the memory storing computer-readable instructions that, when executed by the processor, perform the steps of the method provided in the first aspect above.

[0036] Fourthly, the present invention provides a storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0037] Fifthly, the present invention provides a computer program product comprising a computer program that, when executed by a processor, performs the steps of the method provided in the first aspect above.

[0038] As can be seen from the above technical solutions, the present invention has the following advantages:

[0039] This invention provides a method and apparatus for locating group vehicle faults based on the Internet of Vehicles (IoV). It acquires real-time operational and environmental data of the target vehicle; analyzes the operational and environmental data according to a pre-defined fault feature library to determine if the target vehicle has a suspected fault; if a suspected fault exists, a suspected fault feature vector is generated based on the target vehicle's operational and environmental data; based on the IoV, it acquires suspected fault feature vectors shared by similar vehicles within a pre-defined range of the target vehicle's location during the same time period, forming a suspected fault feature vector set; it calculates the similarity between the target vehicle's suspected fault feature vector and each suspected fault feature vector in the suspected fault feature vector set; if the number of similarities greater than or equal to a similarity threshold exceeds a pre-defined number, it is determined that the target vehicle is suspected of having a group fault. By interacting with surrounding vehicles through the IoV and analyzing operational and environmental data from multiple dimensions, it fully considers the correlation between vehicles and the influence of environmental factors, facilitating rapid location of group vehicle faults. Attached Figure Description

[0040] Figure 1 This is a flowchart illustrating a method for locating group vehicle faults based on the Internet of Vehicles according to the present invention.

[0041] Figure 2 This is a flowchart illustrating a second embodiment of the vehicle group fault location method based on the Internet of Vehicles of the present invention.

[0042] Figure 3 This is a schematic diagram of the architecture of a vehicle group fault location system based on the Internet of Vehicles according to the present invention;

[0043] Figure 4 This is a structural block diagram of an embodiment of a vehicle group fault location device based on the Internet of Vehicles according to the present invention;

[0044] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0045] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0046] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0047] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0048] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0049] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0050] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0051] Figure 1 This is a flowchart illustrating an embodiment of a method for locating group vehicle faults based on the Internet of Vehicles (IoV) of the present invention. Figure 1 As shown, the vehicle-to-everything (V2X) fault location method and apparatus based on the Internet of Vehicles (IoV) provided in this embodiment may include:

[0052] Step S101: Acquire the target vehicle's operating data and environmental data in real time.

[0053] In this embodiment, the edge computing unit of the target vehicle terminal can acquire real-time operating and environmental data of the target vehicle through various on-board sensors and electronic control units (ECUs). Operating data includes, but is not limited to, engine speed, coolant temperature, voltage, and fault codes; environmental data includes, but is not limited to, vehicle location, time, temperature, humidity, and road conditions.

[0054] Step S102: Analyze the target vehicle's operating data and environmental data according to the preset fault feature database to determine whether the target vehicle has any suspected faults.

[0055] After obtaining the target vehicle's operational and environmental data, it is possible to analyze whether the target vehicle has a fault. To further improve the accuracy of the analysis, the acquired data can be preprocessed before analysis. That is, before analyzing the target vehicle's operational and environmental data according to a preset fault feature library, the target vehicle's operational and environmental data can be preprocessed by data cleaning, format conversion, and data compression.

[0056] The edge computing unit of the target vehicle terminal can analyze the target vehicle's operating data and environmental data based on a preset fault feature database to determine whether the target vehicle has any suspected faults. The preset fault feature database is generated based on historical faults.

[0057] Step S103: If the target vehicle has a suspected fault, generate a suspected fault feature vector of the target vehicle based on the target vehicle's operating data and environmental data.

[0058] If a suspected fault is identified in the target vehicle based on a preset fault feature library, a suspected fault feature vector can be generated based on the target vehicle's operating data and environmental data. Furthermore, the suspected fault feature vector can be labeled with vehicle identification, time, location, and other information. It should be noted that this embodiment does not limit the specific method used to generate the feature vector; for example, principal component analysis (PCA), independent component analysis (ICA), and deep learning feature extraction can be used.

[0059] Step S104: Based on the Internet of Vehicles, obtain the suspected fault feature vectors shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period, and form a set of suspected fault feature vectors.

[0060] In this embodiment, the target vehicle can obtain the suspected fault feature vector shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period through the Internet of Vehicles. For example, it can obtain the suspected fault feature vector shared by vehicles of the same type within a radius of two kilometers centered on the target vehicle during the current time period.

[0061] Step S105: Calculate the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the suspected fault feature vector set.

[0062] Furthermore, the suspected fault feature vector of the target vehicle is compared with the set of suspected fault feature vectors, and the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the set of suspected fault feature vectors is calculated. It should be noted that this embodiment does not limit the specific method for calculating the similarity, such as cosine similarity, Manhattan distance, Euclidean distance, etc.

[0063] Step S106: If the number of similarities greater than or equal to the similarity threshold is greater than the preset number, then it is determined that the target vehicle is suspected of having a group malfunction.

[0064] When the similarity reaches a preset similarity threshold (e.g., 80%) and the number exceeds a preset number (e.g., 10 vehicles), it is determined that the target vehicles are suspected of having a group malfunction.

[0065] This embodiment provides a vehicle cluster fault location method based on the Internet of Vehicles (IoV). It acquires real-time operational and environmental data of the target vehicle; analyzes this data according to a pre-defined fault feature library to determine if the target vehicle has a suspected fault; if so, it generates a suspected fault feature vector based on the target vehicle's operational and environmental data; it acquires suspected fault feature vectors shared by similar vehicles within a pre-defined range of the target vehicle's location during the same time period, forming a suspected fault feature vector set; it calculates the similarity between the target vehicle's suspected fault feature vector and each suspected fault feature vector in the set; if the number of similarities greater than or equal to a similarity threshold exceeds a pre-defined number, it determines that the target vehicle is suspected of having a cluster fault. By interacting with surrounding vehicles through the IoV and analyzing operational and environmental data from multiple dimensions, it fully considers the correlation between vehicles and the influence of environmental factors, facilitating rapid location of vehicle cluster faults. Furthermore, by performing preliminary local analysis through the target vehicle's edge computing unit and interacting with surrounding vehicles through the IoV, it reduces the amount of data processing in the cloud, helping to accelerate the identification and location of cluster faults.

[0066] Figure 2 This is a flowchart illustrating a second embodiment of the vehicle cluster fault location method based on the Internet of Vehicles (IoV) of the present invention. Figure 2 As shown, the vehicle-to-everything (V2X) fault location method based on the Internet of Vehicles (IoV) provided in this embodiment, in... Figure 1 The embodiments shown may also include:

[0067] Step S107: Send the suspected fault feature vector of the target vehicle to the cloud platform.

[0068] When a target vehicle is suspected of having a cluster malfunction, the onboard terminal displays a pop-up window asking "Confirm to report suspected malfunction?" If the driver clicks "Confirm," the vehicle uploads malfunction information (including malfunction codes, operating data, location information, etc.) to the edge node and cloud platform. If the driver clicks "Cancel," no reporting occurs. In other words, in response to a user's suspected malfunction reporting command, the suspected malfunction feature vector of the target vehicle is sent to the cloud platform.

[0069] In step S108, the cloud platform performs cluster analysis on the received suspected fault feature vectors using a clustering algorithm.

[0070] The cloud platform receives suspected fault feature vectors uploaded by edge nodes of various vehicle terminals, and then performs clustering analysis on the received suspected fault feature vectors using a clustering algorithm. To further improve the accuracy of fault identification, the cloud platform combines its own stored historical fault data, vehicle information (model, production batch, maintenance records, etc.), and environmental data to perform multi-dimensional data fusion on the received suspected fault feature vectors, and then performs in-depth analysis of the fused data using a clustering algorithm. It should be noted that this embodiment does not limit the specific implementation of the clustering algorithm; for example, the K-means algorithm, etc., can be used.

[0071] Step S109: If the number of suspected fault feature vectors in the same cluster is greater than or equal to the threshold for determining a group fault, and the time and location of each suspected fault feature vector in the cluster are correlated, then a group fault is confirmed to exist.

[0072] After confirming the existence of a cluster failure, the correlation between the cluster failure and vehicle parameters, environmental factors, and driving habits can be further determined. Fault tree analysis and Bayesian networks are used to locate the cause of the cluster failure. Based on the correlation and cause, a cluster failure location report is generated and sent to the target vehicles. Vehicle parameters include, but are not limited to, production batch and parts suppliers; environmental factors include, but are not limited to, specific road sections and climate conditions; and driving habits include, but are not limited to, mileage and charging methods. The location report may include the failure type, the range of vehicles involved, the cause, and the degree of impact.

[0073] In an alternative implementation, the fault feature database can also be updated based on the location report to further improve the accuracy of fault identification.

[0074] This embodiment provides a vehicle cluster fault location method based on the Internet of Vehicles (IoV). Building upon the previous embodiment, it further enhances the method by sending suspected fault feature vectors of the target vehicle to a cloud platform. The cloud platform then performs cluster analysis on the received suspected fault feature vectors using a clustering algorithm. If the number of suspected fault feature vectors in the same cluster is greater than or equal to the cluster fault determination threshold, and the time and location of each suspected fault feature vector in the cluster are correlated, then a cluster fault is confirmed. The method determines the correlation between the cluster fault and vehicle parameters, environmental factors, and driving habits. Fault tree analysis and Bayesian networks are used to locate the causes of the cluster fault. Based on the correlation and causes, a cluster fault location report is generated and sent to the target vehicle. By combining multi-dimensional data for fusion analysis and employing multiple algorithms for fault tracing, the accuracy of cluster fault location is improved. Rapid and accurate location reduces the workload of manual troubleshooting, lowering the time and economic costs of fault investigation. It enables timely detection and location of cluster faults, facilitating repair measures and ensuring vehicle driving safety. The adoption of an edge computing and cloud-based collaborative architecture facilitates subsequent functional upgrades and expansions, adapting to the development of IoV technology.

[0075] Figure 3 This is a schematic diagram of the architecture of a vehicle-to-everything (V2X) fault location system based on the Internet of Vehicles (IoV) of the present invention. The following will use... Figure 3 Taking the provided system as an example, this application provides a detailed explanation of the method for locating group vehicle faults based on the Internet of Vehicles.

[0076] This application addresses group faults in a vehicle-to-everything (V2X) environment by employing an architecture that integrates edge computing units and a cloud platform for rapid fault location. The control logic for rapid location of group faults includes HMI (Human-Machine Interface) logic, edge node data processing logic, cloud platform data analysis and location logic, and vehicle status-based control logic. The HMI logic includes: a "Group Fault Reporting" button on the vehicle's HMI interface. When a suspected fault is detected, the driver can click this button, and a pop-up window on the vehicle terminal asks "Confirm to report suspected fault?" Clicking "Confirm" uploads fault information (including fault codes, operating data, location information, etc.) to the edge node and cloud platform; clicking "Cancel" prevents reporting. After identifying a group fault, the cloud platform pushes a "Group fault detected, location analysis in progress" message to the HMI of the relevant vehicles, and pushes the fault location results and suggested measures after location is complete. When the group fault is resolved, the cloud platform pushes a "Group fault resolved" message to the relevant vehicles, and the corresponding message on the HMI interface disappears.

[0077] The edge node data processing logic includes: data acquisition and preprocessing, preliminary local fault assessment, and preliminary identification of group faults. Data acquisition and preprocessing includes: the edge computing unit of the vehicle terminal collects real-time operating data from onboard sensors and ECUs (such as engine speed, coolant temperature, voltage, fault codes, etc.) as well as vehicle location, time, and environmental parameters (temperature, humidity, road conditions, etc.). The collected data is preprocessed, including data cleaning (noise removal, outlier removal), format conversion, and compression. Preliminary local fault assessment includes: the edge computing unit analyzes the preprocessed data based on a pre-defined fault feature library to determine if the vehicle has any suspected faults. If a suspected fault is found, a fault feature vector is generated, and the vehicle identifier, time, and location information are marked. Preliminary identification of group faults includes: the edge computing unit compares the suspected fault feature vector of its own vehicle with suspected fault feature vectors shared by other vehicles within a certain radius (such as the same area and time period) via V2X communication, and calculates the similarity. When the similarity reaches a preset threshold (e.g., 80%) and the number of suspected faulty vehicles exceeds a set value (e.g., 10 vehicles), it is determined that there may be a group of faults, and the relevant information is packaged and uploaded to the cloud platform.

[0078] The cloud platform's data analysis and location logic includes: data fusion, cluster fault confirmation, fault location, and dynamic updates and feedback. Data fusion involves the cloud platform receiving suspected cluster fault information uploaded by various edge nodes and combining it with its stored historical fault data, vehicle information (model, production batch, maintenance records, etc.), and environmental data to perform multi-dimensional data fusion. Cluster fault confirmation involves the cloud platform conducting in-depth analysis of the fused data, clustering fault feature vectors using clustering algorithms (such as the K-means algorithm). When the number of vehicles in the same cluster reaches the cluster fault determination threshold, and the time and area of ​​the fault occurrence are correlated, a cluster fault is confirmed. Fault location includes correlation analysis, source tracing algorithms, and location result generation. Correlation analysis involves analyzing the correlation between cluster faults and vehicle parameters (such as production batch, parts supplier), environmental factors (such as specific road sections, climate conditions), and usage habits (such as mileage, charging method) to identify potential influencing factors. The source tracing algorithm includes: employing methods such as Fault Tree Analysis (FTA) and Bayesian networks to trace the root causes of confirmed group faults, identifying the root causes such as component defects, software vulnerabilities, and environmental interference. The source tracing result generation includes: generating a source tracing report for the group fault based on correlation analysis and source tracing results, including fault type, affected vehicles, root cause, and impact level. Dynamic updates and feedback include: the cloud platform feeds back the source tracing results to relevant edge nodes and vehicle terminals, and updates the fault feature database and source tracing model, improving the accuracy and efficiency of subsequent source tracing.

[0079] The above positioning logic is executed only if the vehicle is in a normal communication state (normal network connection and normal V2X communication). If a vehicle communication interruption is detected, the following control logic based on the vehicle status is executed: the edge computing unit continues to store fault data and suspected fault information locally, and uploads them to the cloud platform as soon as communication is restored; when the cloud platform detects that some vehicles have communication interruptions, it performs preliminary analysis based on the received information and marks the vehicles that have not uploaded information, and conducts supplementary analysis after their communication is restored.

[0080] In summary, the vehicle cluster fault location method based on the Internet of Vehicles (IoV) provided in this application reduces the amount of data processing in the cloud by using edge computing for local preliminary analysis and interaction with information from surrounding vehicles, thus accelerating the identification and location of cluster faults. By combining multi-dimensional data (vehicle data, environmental data, historical data, etc.) for fusion analysis and employing multiple algorithms for fault tracing, the accuracy of cluster fault location is improved. Rapid and accurate location reduces the workload of manual troubleshooting, lowering the time and economic costs of fault investigation. It enables timely detection and location of cluster faults, facilitating repair measures and ensuring vehicle driving safety, thereby enhancing vehicle safety. The edge computing and cloud-based collaborative architecture facilitates subsequent functional upgrades and expansions, and the system has strong scalability, adapting to the development of IoV technology.

[0081] Figure 4 This is a structural block diagram of an embodiment of a vehicle group fault location device based on the Internet of Vehicles (IoV) of the present invention. Figure 4 As shown, the vehicle-to-everything (V2X) fault location device based on the Internet of Vehicles (IoV) provided in this embodiment may include:

[0082] The first acquisition module 401 is used to acquire the target vehicle's operating data and environmental data in real time;

[0083] The analysis module 402 is used to analyze the operating data and environmental data of the target vehicle based on a preset fault feature library to determine whether the target vehicle has any suspected faults.

[0084] The generation module 403 is used to generate a suspected fault feature vector of the target vehicle based on the target vehicle's operating data and environmental data if the target vehicle has a suspected fault.

[0085] The second acquisition module 404 is used to acquire suspected fault feature vectors shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period based on the vehicle network, and form a set of suspected fault feature vectors.

[0086] The calculation module 405 is used to calculate the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the set of suspected fault feature vectors.

[0087] The processing module 406 is used to determine that the target vehicle is suspected of having a group fault if the number of similarities greater than or equal to the similarity threshold is greater than a preset number.

[0088] The apparatus of this embodiment can be used to perform Figure 1 The technical solutions of the method embodiments shown are similar in principle and in effect, and will not be described again here.

[0089] In an optional implementation, the vehicle-to-everything (V2X) fault location device based on the Internet of Vehicles provided in this embodiment may further include:

[0090] The sending module is used to send the suspected fault feature vector of the target vehicle to the cloud platform;

[0091] The cloud platform is used to perform cluster analysis on the received suspected fault feature vectors using clustering algorithms. If the number of suspected fault feature vectors in the same cluster is greater than or equal to the threshold for determining a group fault, and the time and location of each suspected fault feature vector in the cluster are correlated, then a group fault is confirmed to exist.

[0092] In an optional implementation, the vehicle-to-everything (V2X) fault location device based on the Internet of Vehicles provided in this embodiment may further include:

[0093] The location module is used to determine the correlation between mass failures and vehicle parameters, environmental factors, and driving habits; it uses fault tree analysis and Bayesian networks to locate the causes of mass failures; it generates a location report of the mass failures based on the correlation and causes, and sends the location report to the target vehicle.

[0094] In an optional implementation, the vehicle-to-everything (V2X) fault location device based on the Internet of Vehicles provided in this embodiment may further include:

[0095] The update module is used to update the fault feature database based on the location report.

[0096] In an optional implementation, the vehicle-to-everything (V2X) fault location device based on the Internet of Vehicles provided in this embodiment may further include:

[0097] The preprocessing module is used to clean, convert, and compress the target vehicle's operating data and environmental data before analyzing them based on a preset fault feature library.

[0098] In one alternative implementation, the cloud platform is also used to perform multi-dimensional data fusion on the received suspected fault feature vectors by combining its own stored historical fault data, vehicle information and environmental data before performing cluster analysis on the received suspected fault feature vectors through clustering algorithms.

[0099] In one optional implementation, the sending module is used to send the suspected fault feature vector of the target vehicle to the cloud platform, specifically including:

[0100] In response to a user's command to report a suspected fault, the suspected fault feature vector of the target vehicle is sent to the cloud platform.

[0101] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5 As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement any of the vehicle network-based vehicle cluster fault location methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0102] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface (read / write interface) 103 is connected between the processor 101 and the memory 102, and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus).

[0103] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0104] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0105] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps in any of the vehicle-to-everything (V2X) fault location methods described in the above embodiments. The computer-readable storage medium can be volatile or non-volatile.

[0106] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described vehicle group fault location method based on the Internet of Vehicles.

[0107] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0108] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0109] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0110] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0111] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0112] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0113] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0114] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0115] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0116] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for locating group vehicle faults based on the Internet of Vehicles, characterized in that, include: Real-time acquisition of target vehicle's operational and environmental data; The operating data and environmental data of the target vehicle are analyzed based on a preset fault feature database to determine whether the target vehicle has any suspected faults. If the target vehicle has a suspected fault, a suspected fault feature vector of the target vehicle is generated based on the target vehicle's operating data and environmental data. Based on the Internet of Vehicles, suspected fault feature vectors shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period are obtained, forming a set of suspected fault feature vectors. Calculate the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the set of suspected fault feature vectors; If the number of similarities greater than or equal to the similarity threshold is greater than a preset number, then the target vehicle is suspected of having a group malfunction.

2. The method according to claim 1, characterized in that, The method further includes: The suspected fault feature vector of the target vehicle is sent to the cloud platform; The cloud platform performs cluster analysis on the received suspected fault feature vectors using a clustering algorithm; If the number of suspected fault feature vectors in the same cluster is greater than or equal to the threshold for determining a group fault, and the time and location of each suspected fault feature vector in the cluster are correlated, then a group fault is confirmed to exist.

3. The method according to claim 2, characterized in that, The method further includes: Determine the correlation between the aforementioned group failures and vehicle parameters, environmental factors, and driving habits; Fault tree analysis and Bayesian networks were used to locate the causes of the mass failure. A location report of the mass failure is generated based on the correlation and cause, and the location report is sent to the target vehicle.

4. The method according to claim 3, characterized in that, The method further includes: The fault feature database is updated based on the location report.

5. The method according to any one of claims 1-4, characterized in that, Before analyzing the operating data and environmental data of the target vehicle based on a preset fault feature database, the method further includes: The operating data and environmental data of the target vehicle are cleaned, converted in format, and compressed.

6. The method according to claim 2, characterized in that, Before the cloud platform performs clustering analysis on the received suspected fault feature vectors using a clustering algorithm, the method further includes: The cloud platform combines its stored historical fault data, vehicle information, and environmental data to perform multi-dimensional data fusion on the received suspected fault feature vectors.

7. The method according to claim 2, characterized in that, Sending the suspected fault feature vector of the target vehicle to the cloud platform includes: In response to a user's command to report a suspected fault, the suspected fault feature vector of the target vehicle is sent to the cloud platform.

8. A vehicle cluster fault location device based on the Internet of Vehicles, characterized in that, include: The first acquisition module is used to acquire the target vehicle's operating data and environmental data in real time; The analysis module is used to analyze the operating data and environmental data of the target vehicle according to a preset fault feature library to determine whether the target vehicle has any suspected faults. The generation module is used to generate a suspected fault feature vector of the target vehicle based on the target vehicle's operating data and environmental data if the target vehicle has a suspected fault. The second acquisition module is used to acquire, based on the vehicle network, suspected fault feature vectors shared by vehicles of the same type within a preset range of the target vehicle's location during the same time period, forming a suspected fault feature vector set. The calculation module is used to calculate the similarity between the suspected fault feature vector of the target vehicle and each suspected fault feature vector in the set of suspected fault feature vectors; The processing module is used to determine that the target vehicle is suspected of having a group malfunction if the number of similarities greater than or equal to the similarity threshold is greater than a preset number.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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