Method, device and equipment for positioning network anomaly maintenance node of vehicle and medium

Abstract

The invention relates to the technical field of vehicles, in particular to a positioning method, device, equipment and medium for a network anomaly maintenance node of a vehicle, and the method comprises the steps: carrying out the data analysis based on the obtained data, obtaining the to-be-marked network data of the vehicle according to an analysis result, carrying out the data segment division, and obtaining at least one sample segment, if an abnormal wake-up source exists in at least one sample segment, recording abnormal wake-up source information and a network anomaly maintenance time period corresponding to a target sample segment with the abnormal wake-up source, performing key index analysis on the to-be-marked network data in each sample segment, and determining a network anomaly maintenance reason of the vehicle according to an analysis result, and the network abnormity maintaining node of the vehicle is positioned. Therefore, the problems that a vehicle network cannot normally sleep and power shortage is caused due to the fact that an abnormal network maintaining and positioning method in the related technology cannot accurately position specific nodes and reasons thereof which cause abnormal network maintenance are solved.

Description

Technical Field

[0001] The present application relates to the technical field of vehicles, and particularly relates to a method, device, equipment and medium for positioning a network abnormal maintenance node of a vehicle. Background Art

[0002] With the improvement of the electronic level of automobiles, modern automobiles are equipped with dozens of nodes responsible for different tasks. These nodes need to communicate with each other through the in-vehicle network to maintain the normal operation of the entire system. However, in practical applications, due to software design problems, there may be a situation where some nodes or multiple nodes abnormally maintain the network state, resulting in the vehicle being unable to power off normally and ultimately causing a power loss phenomenon. Therefore, it is very necessary to accurately locate the nodes that cause the network abnormal maintenance and their reasons.

[0003] In the related art, mainly by collecting the network management messages of each control module, it is judged whether the vehicle network is abnormally awakened from sleep, but it is impossible to accurately locate the specific nodes that cause the network abnormal maintenance and their reasons. At the same time, it is also impossible to provide effective support for software repair, which urgently needs to be solved. Summary of the Invention

[0004] The present application provides a method, device, equipment and medium for positioning a network abnormal maintenance node of a vehicle to solve the problems that the abnormal network maintenance positioning method in the related art cannot accurately locate the specific nodes that cause the network abnormal maintenance and their reasons, resulting in the vehicle network being unable to sleep normally and causing power loss and other problems.

[0005] The first aspect embodiment of the present application provides a method for positioning a network abnormal maintenance node of a vehicle, including the following steps:

[0006] Obtain the current network state data of the vehicle, and perform data analysis on the current network state data to obtain the to-be-labeled network data of the vehicle according to the parsing result, and divide the to-be-labeled network data into data segments to obtain at least one sample segment;

[0007] Judge whether there is an abnormal wake-up source in the at least one sample segment. If there is the abnormal wake-up source in the at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source, and determine the network abnormal maintenance time period of the target sample segment;

[0008] Based on the at least one sample segment, perform key index analysis on the to-be-labeled network data in each sample segment, and determine the network abnormal maintenance reason of the vehicle according to the analysis result, so as to locate the network abnormal maintenance node of the vehicle based on the network abnormal maintenance time period and the network abnormal maintenance reason.

[0009] Through the above technical solution, by performing steps such as data analysis, segment division, and abnormal network determination on the current network status data of the vehicle, network anomalies can be distinguished, and the network anomaly maintenance nodes of the vehicle can be located, thereby improving the accuracy of network anomaly detection, maliciously discovering abnormal nodes in advance, and reducing the test cost.

[0010] According to an embodiment of the present application, the data analysis of the current network status data to obtain the network data to be marked of the vehicle according to the parsing result includes:

[0011] Judge whether the current network status data has a jump, or whether the time interval between the first-frame network status data and the second-frame network status data in the current network status data is greater than a preset time interval;

[0012] If the current network status data has a jump, or the time interval between the first-frame network status data and the second-frame network status data is greater than the preset time interval, it is determined that the current network status data is in an abnormal state;

[0013] Based on the first time point when the current network status data has a jump or the second time point when the time interval is greater than the preset time interval, the current network status data is marked according to the time sorting of the first time point and the second time, and the network data to be marked of the vehicle is obtained.

[0014] Through the above technical solution, based on state jump detection and time threshold detection, important time nodes in the vehicle network behavior can be accurately identified. At the same time, through time sorting and flag bit generation, the original data is converted into structured segment data, and a clear time range is provided for the location of network anomaly maintenance nodes, which is convenient for subsequent in-depth analysis and rapid troubleshooting of anomalies.

[0015] According to an embodiment of the present application, the data segment division of the network data to be marked to obtain at least one sample segment includes:

[0016] Based on the network data to be marked of the vehicle, data segment division is performed on two adjacent current network status data in the network data to be marked, and at least one sample segment in the network data to be marked is obtained.

[0017] Through the above technical solution, based on the division of data segments, not only can the calculation amount be reduced and the problem be quickly located, but also the efficiency and accuracy of data analysis are improved, providing a solid foundation for subsequent network anomaly maintenance node location and optimization.

[0018] According to an embodiment of the present application, determining whether there is an abnormal wake-up source in the at least one sample segment includes:

[0019] Perform data analysis on each sample segment in the at least one sample segment, and determine whether the first-frame network status data and the second-frame network status data in each sample segment are in a continuous state;

[0020] If the first-frame network status data and the second-frame network status data are not in the continuous state, it is determined that there is an abnormal wake-up source in the at least one sample segment.

[0021] Through the above technical solutions, through fragmentation processing and frame-by-frame analysis, complex time series data is decomposed into multiple independent segments, focusing on the behavior within the critical time period, which can improve the troubleshooting efficiency. In addition, by combining data continuity detection and wake-up source recording, the vehicle network behavior can be analyzed more comprehensively, improving the detection accuracy.

[0022] According to an embodiment of the present application, determining the network anomaly maintenance time period of the target sample segment includes:

[0023] Perform data analysis on each sample segment in the at least one sample segment to obtain the proportion of the vehicle's key state being in the non-started state;

[0024] Among the proportions of the vehicle's key state being in the non-started state, screen at least one abnormal network data segment that meets the preset proportion condition, so as to determine the network anomaly maintenance time period based on the at least one abnormal network data segment.

[0025] Through the above technical solutions, through clear proportion conditions, the time period when the vehicle should enter the sleep state but the network still remains is effectively screened out, which can narrow the analysis scope, reduce the computational complexity, quickly locate the network anomaly maintenance time period, improve the analysis efficiency, and based on the strict screening conditions of the key state, avoid misjudgment and enhance the analysis reliability.

[0026] According to an embodiment of the present application, performing key index analysis on the network data to be marked in each sample segment and determining the reason for the network anomaly maintenance of the vehicle according to the analysis result includes:

[0027] Obtain the start time and end time of each sample segment in the at least one sample segment, and based on the start time and the end time, perform interaction analysis on the abnormal object of the vehicle to determine the abnormal target object of the vehicle;

[0028] When the abnormal target object of the vehicle is the target vehicle end, obtain the active reporting alarm times of the vehicle in the at least one sample segment and the proportion of the maintenance source information of all working nodes; when the abnormal target object of the vehicle is the target server end, obtain the remote control times of the vehicle. At the same time, obtain the wake-up times of the vehicle when there is the abnormal wake-up source in the at least one sample segment, the proportion of the on-vehicle device opening duration in the at least one sample segment, and the proportion of the OTA (Over-the-Air Technology) duration.

[0029] Judge whether the wake-up times of the vehicle are greater than a preset wake-up times threshold, or whether the proportion of the on-vehicle device opening duration is greater than a first preset duration proportion, or whether the OTA duration proportion is greater than a second preset duration proportion, or whether the remote control times of the vehicle are greater than a preset remote control times threshold, or whether the active reporting alarm times are greater than an active reporting alarm times threshold, or whether the proportion of the maintenance source information of all working nodes is greater than a preset proportion threshold.

[0030] If the wake-up times of the vehicle are greater than the preset wake-up times threshold, it is determined that the reason for the network anomaly maintenance of the vehicle is frequent wake-up. Or, if the proportion of the on-vehicle device opening duration is greater than the first preset duration proportion, it is determined that the reason for the network anomaly maintenance of the vehicle is manual intervention wake-up. Or, if the OTA duration proportion is greater than the second preset duration proportion or the remote control times of the vehicle are greater than the preset remote control times threshold, it is determined that the reason for the network anomaly maintenance of the vehicle is manual remote control wake-up. Or, if the active reporting alarm times are greater than the active reporting alarm times threshold or the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold, it is determined that the reason for the network anomaly maintenance of the vehicle is abnormal wake-up of the target vehicle end.

[0031] Through the above technical solution, by detailed analysis of multiple key indicators, it is possible to avoid misjudgment caused by a single indicator anomaly, be able to clarify the specific reasons for network anomaly maintenance, and ensure that the results have a high degree of credibility.

[0032] The positioning method of the network anomaly maintenance node of a vehicle according to an embodiment of the present application performs data analysis based on the acquired current network status data, obtains the to-be-marked network data of the vehicle according to the parsing result, and performs data segment division to obtain at least one sample segment. If there is an abnormal wake-up source in the at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source and the network anomaly maintenance time period, and perform key index analysis on the to-be-marked network data in each sample segment. Determine the reason for the network anomaly maintenance of the vehicle according to the analysis result, and then locate the network anomaly maintenance node of the vehicle. Thus, it solves the problem that the abnormal network maintenance positioning method in the related technology cannot accurately locate the specific node and its reason causing the network anomaly maintenance, resulting in the vehicle network being unable to normally sleep and causing problems such as power loss. By performing steps such as data analysis, segment division, and abnormal network determination on the current network status data of the vehicle, the network anomaly can be distinguished, and the network anomaly maintenance node of the vehicle can be located, thereby improving the network anomaly detection accuracy, maliciously discovering abnormal nodes in advance, and reducing the test cost.

[0033] The second aspect of the present application provides a positioning device for a network anomaly maintenance node of a vehicle, including:

[0034] A data division module, configured to obtain the current network status data of the vehicle, perform data analysis on the current network status data, obtain the to-be-marked network data of the vehicle according to the parsing result, and perform data segment division on the to-be-marked network data to obtain at least one sample segment;

[0035] A determination module, configured to determine whether there is an abnormal wake-up source in the at least one sample segment. If there is the abnormal wake-up source in the at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source, and determine the network anomaly maintenance time period of the target sample segment;

[0036] A positioning module, configured to perform key index analysis on the to-be-marked network data in each sample segment based on the at least one sample segment, and determine the reason for the network anomaly maintenance of the vehicle according to the analysis result, so as to locate the network anomaly maintenance node of the vehicle based on the network anomaly maintenance time period and the reason for the network anomaly maintenance.

[0037] According to an embodiment of the present application, the data division module includes:

[0038] A first judgment unit, configured to judge whether the current network status data has a jump, or whether the time interval between the first frame of network status data and the second frame of network status data in the current network status data is greater than a preset time interval;

[0039] A first determination unit, configured to determine that the current network status data is in an abnormal state if the current network status data jumps, or if the time interval between the first-frame network status data and the second-frame network status data is greater than the preset time interval;

[0040] A marking unit, configured to mark the current network status data based on the first time point when the current network status data jumps or the second time point when the time interval is greater than the preset time interval, and perform time sorting on the first time point and the second time point to obtain the network data to be marked of the vehicle.

[0041] According to an embodiment of the present application, the data partitioning module includes:

[0042] A data partitioning unit, configured to perform data segment partitioning on two adjacent current network status data in the network data to be marked based on the network data to be marked of the vehicle, to obtain at least one sample segment in the network data to be marked.

[0043] According to an embodiment of the present application, the determination module includes:

[0044] A first data analysis unit, configured to perform data analysis on each sample segment in the at least one sample segment, and determine whether the first-frame network status data and the second-frame network status data in each sample segment are in a continuous state;

[0045] A second determination unit, configured to determine that there is an abnormal wake-up source in the at least one sample segment if the first-frame network status data and the second-frame network status data are not in the continuous state.

[0046] According to an embodiment of the present application, the determination module includes:

[0047] A second data analysis unit, configured to perform data analysis on each sample segment in the at least one sample segment to obtain the proportion of the vehicle's key status being in an unstarted state;

[0048] A screening unit, configured to screen at least one abnormal network data segment that meets the preset proportion condition from the proportion of the vehicle's key status being in an unstarted state, so as to determine the network anomaly maintenance time period based on the at least one abnormal network data segment.

[0049] According to an embodiment of the present application, the positioning module includes:

[0050] A determination unit for obtaining the start time and end time of each sample segment in the at least one sample segment, and based on the start time and the end time, performing interaction analysis on the abnormal object of the vehicle to determine the abnormal target object of the vehicle;

[0051] A first acquisition unit for, when the abnormal target object of the vehicle is the target vehicle terminal, acquiring the number of active alarm reports of the vehicle in the at least one sample segment and the proportion of the maintenance source information of all working nodes; when the abnormal target object of the vehicle is the target server terminal, acquiring the number of remote controls of the vehicle, and at the same time, acquiring the number of awakenings of the vehicle when there is the abnormal wake-up source in the at least one sample segment, the proportion of the on-vehicle device opening duration in the at least one sample segment, and the proportion of the OTA duration;

[0052] A second judgment unit for judging whether the number of awakenings of the vehicle is greater than a preset awakening number threshold, or whether the proportion of the on-vehicle device opening duration is greater than a first preset duration proportion, or whether the proportion of the OTA duration is greater than a second preset duration proportion, or whether the number of remote controls of the vehicle is greater than a preset remote control number threshold, or whether the number of active alarm reports is greater than an active alarm report number threshold, or whether the proportion of the maintenance source information of all working nodes is greater than a preset proportion threshold;

[0053] A third determination unit for, if the number of awakenings of the vehicle is greater than the preset awakening number threshold, determining that the reason for the abnormal network maintenance of the vehicle is frequent awakening, or, if the proportion of the on-vehicle device opening duration is greater than the first preset duration proportion, determining that the reason for the abnormal network maintenance of the vehicle is manual intervention awakening, or if the proportion of the OTA duration is greater than the second preset duration proportion or the number of remote controls of the vehicle is greater than the preset remote control number threshold, determining that the reason for the abnormal network maintenance of the vehicle is manual remote control awakening, or, if the number of active alarm reports is greater than the active alarm report number threshold or the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold, determining that the reason for the abnormal network maintenance of the vehicle is abnormal awakening of the target vehicle terminal.

[0054] The positioning device for the network anomaly maintenance node of a vehicle according to an embodiment of the present application performs data analysis based on the acquired current network status data, obtains the to-be-labeled network data of the vehicle according to the parsing result, and performs data segment division to obtain at least one sample segment. If there is an abnormal wake-up source in the at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source and the network anomaly maintenance time period, and perform key index analysis on the to-be-labeled network data in each sample segment. Determine the cause of the vehicle's network anomaly maintenance according to the analysis result, and then locate the network anomaly maintenance node of the vehicle. Thereby, it solves the problem that the abnormal network maintenance positioning method in the related art cannot accurately locate the specific node and its cause leading to network anomaly maintenance, resulting in the vehicle network being unable to normally sleep and causing problems such as power loss. By performing steps such as data analysis, segment division, and abnormal network determination on the current network status data of the vehicle, the network anomaly can be distinguished, and the network anomaly maintenance node of the vehicle can be located, thereby improving the accuracy of network anomaly detection, maliciously discovering abnormal nodes in advance, and reducing the test cost.

[0055] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. The processor executes the program to implement the positioning method for the network anomaly maintenance node of the vehicle as described in the above embodiment.

[0056] The fourth aspect of the present application provides a computer-readable storage medium storing computer instructions for causing the computer to execute the positioning method for the network anomaly maintenance node of the vehicle as described in the above embodiment.

[0057] The fifth aspect of the present application provides a computer program product including a computer program which is executed to implement the positioning method for the network anomaly maintenance node of the vehicle as described in the above embodiment.

[0058] The additional aspects and advantages of the present application will be partially given in the following description, partially become obvious from the following description, or be understood through the practice of the present application. Description of the Drawings

[0059] The above and / or additional aspects and advantages of the present application will become obvious and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0060] Figure 1 It is a flowchart of a positioning method for a network anomaly maintenance node of a vehicle according to an embodiment of the present application;

[0061] Figure 2Schematic diagram of a positioning device for a network anomaly maintenance node of a vehicle according to an embodiment of the present application;

[0062] Figure 3 Schematic structural diagram of an electronic device according to an embodiment of the present application. Detailed implementation manners

[0063] Embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0064] The positioning method, device, equipment and medium of the network anomaly maintenance node of the vehicle according to the embodiment of the present application will be described below with reference to the accompanying drawings. Aiming at the problem that the abnormal network maintenance positioning method in the related technology mentioned in the above background technology cannot accurately locate the specific node and its cause leading to network anomaly maintenance, resulting in the vehicle network being unable to sleep normally and causing power loss, the present application provides a positioning method for the network anomaly maintenance node of the vehicle. In this method, data analysis is performed based on the obtained current network state data, the to-be-labeled network data of the vehicle is obtained according to the parsing result, and data segment division is performed to obtain at least one sample segment. If there is an abnormal wake-up source in at least one sample segment, the abnormal wake-up source information and the network anomaly maintenance time period corresponding to the target sample segment with the abnormal wake-up source are recorded, and key index analysis is performed on the to-be-labeled network data in each sample segment. According to the analysis result, the cause of the network anomaly maintenance of the vehicle is determined, and then the network anomaly maintenance node of the vehicle is located. Thereby, the problems that the abnormal network maintenance positioning method in the related technology cannot accurately locate the specific node and its cause leading to network anomaly maintenance, resulting in the vehicle network being unable to sleep normally and causing power loss, etc. are solved. By performing steps such as data analysis, segment division, and abnormal network determination on the current network state data of the vehicle, network anomalies can be distinguished, and the network anomaly maintenance node of the vehicle can be located, thereby improving the accuracy of network anomaly detection, maliciously discovering abnormal nodes in advance, and reducing the test cost.

[0065] Specifically, Figure 1 It is a schematic flowchart of a positioning method for a network anomaly maintenance node of a vehicle provided by an embodiment of the present application.

[0066] As Figure 1 shown, the positioning method for the network anomaly maintenance node of the vehicle includes the following steps:

[0067] In step S101, obtain the current network status data of the vehicle, and perform data analysis on the current network status data to obtain the to-be-labeled network data of the vehicle according to the parsing result, and perform data segment division on the to-be-labeled network data to obtain at least one sample segment.

[0068] According to an embodiment of the present application, performing data analysis on the current network status data to obtain the to-be-labeled network data of the vehicle according to the parsing result includes: determining whether the current network status data has a jump, or whether the time interval between the first-frame network status data and the second-frame network status data in the current network status data is greater than a preset time interval; if the current network status data has a jump, or the time interval between the first-frame network status data and the second-frame network status data is greater than the preset time interval, it is determined that the current network status data is in an abnormal state; based on the first time point when the current network status data has a jump or the second time point when the time interval is greater than the preset time interval, mark the current network status data according to the time sorting of the first time point and the second time point to obtain the to-be-labeled network data of the vehicle.

[0069] According to an embodiment of the present application, performing data segment division on the to-be-labeled network data to obtain at least one sample segment includes: based on the to-be-labeled network data of the vehicle, performing data segment division on two adjacent current network status data in the to-be-labeled network data to obtain at least one sample segment in the to-be-labeled network data.

[0070] Wherein, the preset time interval can be determined by those skilled in the art based on the actual time interval of vehicle dormancy, or can be obtained through a limited number of computer simulations, and no specific limitation is made here.

[0071] Specifically, since the vehicle as a whole has dozens of working nodes, each node is responsible for a different working area and needs to communicate with each other to maintain the network for operation. Among them, if there are software design problems, there will be abnormal network maintenance of one or more nodes, resulting in the vehicle being unable to power off, continuously consuming the battery power, and finally causing a power shortage. Therefore, in order to solve the problems in the related art that the specific nodes and reasons for abnormal network maintenance cannot be accurately located, resulting in the vehicle network being unable to normally enter dormancy and causing power shortage, etc., the embodiments of the present application restore the working conditions at the problem moment through big data analysis, distinguish normal and abnormal network maintenance, and locate the specific reasons for the abnormal maintained network, so as to effectively solve the power shortage problem caused by abnormal network maintenance of the vehicle.

[0072] Among them, to achieve the interconnection and interoperability of each node of the vehicle, the target vehicle terminal and the target server terminal of the embodiments of the present application need to have the following functions: (1) Each target vehicle terminal has the ability to collect and upload big data; (2) Each target vehicle terminal has a vehicle network communication function, that is, it has a communicable Tbox component; (3) The maintenance source and wake-up source of each node of the target vehicle terminal have the ability to periodically report to the server, that is, it has the ability to periodically send to the Tbox; (4) The target server terminal has a large enough memory to store the data uploaded by all mass-produced vehicles in one day.

[0073] Further, by setting up multiple threads in the embodiments of the present application, the current network status data of multiple vehicles can be processed simultaneously to avoid the delay problem caused by processing one by one in a single thread. In the vehicle networking scenario, a large number of vehicles may upload data simultaneously. Therefore, using multiple threads can significantly improve the concurrent processing ability of the system and ensure that the data is analyzed in a timely manner.

[0074] Specifically, the embodiments of the present application first obtain the current network status data of the vehicle and sort the current network status data of each vehicle by time, so as to facilitate subsequent data segment division and abnormal event detection, and at the same time ensure the logical consistency of data analysis and avoid misjudgment caused by out-of-order data. Secondly, perform data analysis on the current network status data. If the current network status data undergoes a jump, or the time interval between the first frame of network status data and the second frame of network status data is greater than the preset time interval, it is determined that the current network status data is in an abnormal state. That is to say, if the states of the four doors, car locks, windows, engine hood, trunk, and key of the vehicle change, this indicates a change in the user's behavior or the vehicle's working mode. For example, when the car lock is locked, the vehicle should enter the sleep state. If the network still remains at this time, it can be determined that the current network status data is in an abnormal state. Or, even if there is no obvious state jump in the current network status data, but the time interval between the first frame of network status data and the second frame of network status data is greater than the preset time interval, that is, when it exceeds the time threshold of normal sleep, it can also be determined that the current network status data is in an abnormal state. At this time, based on the first time point when the current network status data undergoes a jump or the second time point when the time interval is greater than the preset time interval, mark the current network status data according to the time sorting of the first time point and the second time. That is, sort the current network status data in the abnormal state by time and mark the current network status data, which can divide the entire time series into multiple independent segments, each segment corresponding to a specific event or time period, so as to facilitate the rapid positioning of the subsequent network anomaly maintenance nodes. Its flag bits can be expressed as T1, T2...Ti, thus obtaining the network data to be marked of the vehicle.

[0075] Thus, through the above-mentioned state transition detection and time threshold detection, important time nodes in vehicle network behavior can be accurately identified. At the same time, through time sorting and flag generation, the original data is converted into structured fragment data, and a clear time range is provided for the positioning of network anomaly maintenance nodes, facilitating subsequent in-depth analysis and rapid troubleshooting of anomalies.

[0076] Further, based on the to-be-labeled network data of the vehicle obtained above, data segment division is performed on two adjacent current network state data in the to-be-labeled network data, and at least one sample segment in the to-be-labeled network data is obtained. That is to say, by segmenting the to-be-labeled network data labeled as T1, T2...Ti, separate sample segments of the to-be-labeled network data between T1 and T2, T2 and T3,..., Ti and Tj can be obtained. The corresponding identifiers of each sample segment can be P1, P2...Pi respectively. Among them, each sample segment represents the state or behavior of the vehicle within a specific time period. Indicator information such as wake-up source information and maintenance source ratio can be further statistically analyzed within each sample segment, so as to deeply explore the cause of anomalies. Thus, based on data segment division, the behavior within a specific time period can be focused on, redundant interference of global data can be avoided, and at the same time, through the sample segment identifier, the specific time period where the problem occurs can be directly pointed to, so as to improve the troubleshooting efficiency.

[0077] For example, when segmenting the to-be-labeled network data labeled as T1, T2...Ti, separate sample segments of the to-be-labeled network data between T1 and T2, T2 and T3,..., Ti and Tj will be obtained. The corresponding identifiers of each sample segment can be correspondingly P1, P2...Pi. For example, P1: can be represented as from T1 to T2 (the vehicle lock is locked to the hood is closed), P2: can be represented as from T2 to T3 (the hood is closed to the car door is opened the next morning). Then, the sample segment of P1 can be analyzed. After the analysis, if it is detected that the network does not enter the sleep state in time after the vehicle lock is locked, at this time, the specific time period where the problem occurs can be directly pointed to as the time period from T1 to T2. Or, the sample segment of P2 can be analyzed. After the analysis, if it is detected that there are multiple abnormal wake-ups during the night parking period, at this time, the specific time period where the problem occurs can be directly pointed to as the time period from T2 to T3. Thus, based on the division of data segments, not only can the calculation amount be reduced, the problem can be quickly located, but also the efficiency and accuracy of data analysis are improved, providing a solid foundation for subsequent network anomaly maintenance node positioning and optimization.

[0078] In step S102, it is judged whether there is an abnormal wake-up source in at least one sample segment. If there is an abnormal wake-up source in at least one sample segment, the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source is recorded, and the network anomaly maintenance time period of the target sample segment is determined.

[0079] According to an embodiment of the present application, determining whether there is an abnormal wake-up source in at least one sample segment includes: performing data analysis on each of the at least one sample segment, and determining whether the first-frame network status data and the second-frame network status data in each sample segment are in a continuous state; if the first-frame network status data and the second-frame network status data are not in a continuous state, it is determined that there is an abnormal wake-up source in the at least one sample segment.

[0080] Specifically, after obtaining at least one sample segment in the network data to be marked in the embodiment of the present application, further perform data analysis on each of the at least one sample segment, that is, analyze sample segments P1, P2... Pi. Since the data continuity detection method can effectively avoid misjudgment caused by noise or other interferences, therefore, in the embodiment of the present application, based on the analysis result, if it is detected that the first-frame network status data and the second-frame network status data in any sample segment are not in a continuous state, it can be determined that there is an abnormal wake-up source in the sample segment. At this time, it can be indicated that the vehicle has an abnormal wake-up, and record the abnormal wake-up source information PiH1, PiH2... PiHj corresponding to the target sample segment with the abnormal wake-up source. The abnormal wake-up source information may include the source node and specific type of the abnormal wake-up signal, providing basic data for subsequent statistical analysis. Among them, the non-continuity detection method can be flexibly adjusted according to actual needs, such as modifying the time interval threshold or flag bit judgment rule, etc.

[0081] Thus, through fragmentation processing and frame-by-frame analysis, the complex time series data is decomposed into multiple independent segments, focusing on the behavior in the key time period, which can improve the troubleshooting efficiency. In addition, by combining data continuity detection and wake-up source recording, the vehicle network behavior can be analyzed more comprehensively, improving the detection accuracy.

[0082] According to an embodiment of the present application, determining the network anomaly maintenance time period of the target sample segment includes: performing data analysis on each of the at least one sample segment to obtain the proportion of the vehicle's key state being in the unstarted state; among the proportions of the vehicle's key state being in the unstarted state, screening at least one abnormal network data segment that meets the preset proportion condition, so as to determine the network anomaly maintenance time period based on the at least one abnormal network data segment.

[0083] Among them, the preset proportion condition can be set by those skilled in the art based on the actual test requirements of the vehicle, and no specific limitation is made here.

[0084] Specifically, after obtaining the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source in the embodiment of the present application, further determine the network anomaly maintenance time period of the target sample segment.

[0085] Specifically, under normal circumstances, when the vehicle key status is not started, the vehicle should enter the sleep state and network activities should significantly decrease or stop. If the network still persists during the time when the key status is not started, it may be abnormal behavior. Therefore, data analysis is performed on each sample segment in at least one sample segment to obtain the proportion of the vehicle's key status being in the not-started state. Among the proportions of the vehicle's key status being in the not-started state, all target sample segments P1, P2... Pi are screened to select at least one abnormal network data segment that meets the preset proportion condition. For example, an abnormal network data segment Pj with a proportion of the key status being not started reaching 95% is selected. At this time, the time period Pj when the vehicle is in a stationary state but the network still persists can be effectively selected. For example, when screening at least one abnormal network data segment, it is detected that the proportion of the key status being not started in the abnormal network data segment P2 (during night parking) reaches more than 95%. At this time, the abnormal network data segment P2 is locked as the network abnormal persistence time period that occurs in the vehicle.

[0086] Thus, through the clear proportion condition, the time period when the vehicle should enter the sleep state but the network still persists can be effectively selected, which can narrow the analysis scope, reduce the computational complexity, quickly locate the network abnormal persistence time period, improve the analysis efficiency, and avoid misjudgment and enhance the analysis reliability based on the strict screening condition of the key status.

[0087] In step S103, based on at least one sample segment, key index analysis is performed on the network data to be marked in each sample segment, and the reason for the vehicle's network abnormal persistence is determined according to the analysis result, so as to locate the network abnormal persistence node of the vehicle based on the network abnormal persistence time period and the reason for the network abnormal persistence.

[0088] According to an embodiment of the present application, key index analysis is performed on the network data to be marked in each sample segment, and the cause of the vehicle's network anomaly maintenance is determined according to the analysis results, including: obtaining the start time and end time of each sample segment in at least one sample segment, and based on the start time and end time, performing interaction analysis on the abnormal objects of the vehicle to determine the abnormal target object of the vehicle; when the abnormal target object of the vehicle is the target vehicle end, obtaining the number of active alarm reports of the vehicle in at least one sample segment and the proportion of the maintenance source information of all working nodes; when the abnormal target object of the vehicle is the target server end, obtaining the number of remote control times of the vehicle, and at the same time, obtaining the number of wake-up times of the vehicle when there is an abnormal wake-up source in at least one sample segment, the proportion of the on-vehicle device's on-time duration in at least one sample segment, and the proportion of the OTA duration; determining whether the number of wake-up times of the vehicle is greater than the preset wake-up times threshold, or whether the proportion of the on-vehicle device's on-time duration is greater than the first preset duration proportion, or whether the proportion of the OTA duration is greater than the second preset duration proportion, or whether the number of remote control times of the vehicle is greater than the preset remote control times threshold, or whether the number of active alarm reports is greater than the active alarm report times threshold, or whether the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold; if the number of wake-up times of the vehicle is greater than the preset wake-up times threshold, it is determined that the cause of the vehicle's network anomaly maintenance is frequent wake-up, or, if the proportion of the on-vehicle device's on-time duration is greater than the first preset duration proportion, it is determined that the cause of the vehicle's network anomaly maintenance is manual intervention wake-up, or if the proportion of the OTA duration is greater than the second preset duration proportion or the number of remote control times of the vehicle is greater than the preset remote control times threshold, it is determined that the cause of the vehicle's network anomaly maintenance is manual remote control wake-up, or, if the number of active alarm reports is greater than the active alarm report times threshold or the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold, it is determined that the cause of the vehicle's network anomaly maintenance is abnormal wake-up of the target vehicle end.

[0089] Among them, the preset wake-up times threshold, the first preset duration proportion, the second preset duration proportion, the preset remote control times threshold, the active alarm report times threshold, and the preset proportion threshold can all be set by those skilled in the art based on the actual test requirements of the vehicle, or obtained through a limited number of computer simulations, and no specific limitations are made here.

[0090] Specifically, to ensure the accurate positioning of network anomaly maintenance, in the embodiment of the present application, it is necessary to further perform interaction analysis between the target vehicle end and the target server end on the abnormal network data segment, that is, perform key index analysis on the network data to be marked in each sample segment, and obtain the abnormal target object, the cause of the vehicle's network anomaly maintenance, and the network anomaly maintenance node according to the analysis results.

[0091] Specifically, first, obtain the start time and end time of each sample segment Pi in at least one sample segment; second, based on the start time and end time, associate the interaction information between the target vehicle end and the target server end, and conduct interaction analysis on the abnormal objects of the vehicle to determine the abnormal target object of the vehicle. When the abnormal target object of the vehicle is the target vehicle end, obtain the number of active reported alarms of the vehicle in at least one sample segment, denoted as PiA1, PiA2... PiAj, and the proportion of the maintenance source information of all working nodes, PiW1, PiW2... PiWj; when the abnormal target object of the vehicle is the target server end, obtain the number of remote control times of the vehicle, denoted as PiC1, PiC2... PiCj; third, obtain the wake-up times of the vehicle when there is an abnormal wake-up source in at least one sample segment, the proportion of the on-vehicle device startup duration and the OTA duration in at least one sample segment. Among them, the proportion of the on-vehicle device startup duration may include, but is not limited to, the proportion of the lighting startup duration, the proportion of the air conditioner startup, and the proportion of the OBD (On-Board Diagnostics) port usage duration; finally, conduct analysis based on the above key indicators, and determine the reason for the network anomaly maintenance of the vehicle according to the analysis results.

[0092] For example, if the wake-up times of the vehicle are greater than the preset wake-up times threshold, it can be determined that the reason for the network anomaly maintenance of the vehicle is frequent wake-up. At this time, the node PiHk with the largest proportion of the abnormal wake-up source is the main reason for the network anomaly maintenance; if the proportion of the on-vehicle device startup duration is greater than the first preset duration proportion, it is determined that the reason for the network anomaly maintenance of the vehicle is manual intervention wake-up. For example, when the lighting is turned on, or the air conditioner is turned on, or the OBD port usage proportion is greater than 95%, at this time, the manual intervention wake-up is the main reason for the network anomaly maintenance; if the OTA duration proportion is greater than the second preset duration proportion or the number of remote control times of the vehicle is greater than the preset remote control times threshold, it is determined that the reason for the network anomaly maintenance of the vehicle is manual remote control wake-up. For example, when the OTA duration proportion is greater than 95%, at this time, the manual remote control wake-up is the main reason for the network anomaly maintenance; if the number of active reported alarms is greater than the active reported alarm number threshold or the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold, it is determined that the reason for the network anomaly maintenance of the vehicle is abnormal wake-up of the target vehicle end. For example, when the proportion of the maintenance source information of all working nodes is greater than 95%, at this time, the maintenance source information PiWk with the maintenance source information of all working nodes greater than 95% is the main reason for the network anomaly maintenance.

[0093] Further, to improve the efficiency of problem troubleshooting and enhance the accuracy and applicability of analysis results, based on the obtained abnormal wake-up source information PiH1, PiH2... PiHj, the proportion of vehicle keys in the unstarted state, the proportion of maintenance source information of all working nodes PiW1, PiW2... PiWj, the proportion of the on-vehicle device startup duration, the OTA duration, the number of vehicle remote control times PiC1, PiC2... PiCj, and the number of vehicle active report alarms PiA1, PiA2... PiAj in at least one sample segment, secondary analysis and adjustment can be performed to predict the problem cause in advance and conduct refined diagnosis, thereby providing strong technical support for vehicle management and after-sales service.

[0094] To facilitate a more intuitive understanding of this application for those skilled in the art, the following is described based on a specific implementation:

[0095] Specifically, if a fleet management system needs to troubleshoot abnormal network maintenance during night parking, first, data collection and preliminary analysis are carried out. The current network status data of all vehicles are collected, and adjacent two current network status data are segmented in chronological order to obtain at least one sample segment. Secondly, all sample segments P1, P2... Pi are analyzed to confirm and record the abnormal wake-up source information PiH1, PiH2... PiHj corresponding to the target sample segments with abnormal wake-up sources. Data analysis is performed on each sample segment in at least one sample segment to screen out abnormal network data segments where the proportion of the unstarted key state exceeds 95%, thereby determining the network abnormal maintenance time period. Thirdly, key index analysis is performed on the to-be-labeled network data in each sample segment, and based on the analysis results, the vehicles with network abnormal maintenance and the reasons for network abnormal maintenance are determined to locate the network abnormal maintenance nodes of the vehicles based on the network abnormal maintenance time period and the reasons for network abnormal maintenance. Finally, if it is confirmed that node 1 of vehicle A frequently sends wake-up signals, it can be confirmed at this time that the network abnormal maintenance node of vehicle A is caused by a software design defect resulting in frequent wake-up. If it is confirmed that the proportion of the lighting startup duration of vehicle B is as high as 95%, it can be confirmed at this time that the network abnormal maintenance node of vehicle B is caused by manual intervention wake-up. If it is confirmed that the number of remote control times of vehicle C increases significantly, or the proportion of the OTA duration is greater than 95%, it can be confirmed at this time that the network abnormal maintenance node of vehicle C is caused by manual remote control wake-up.

[0096] Thus, based on the above network abnormal maintenance node positioning method, normal maintenance and abnormal maintenance in the vehicle network can be distinguished. Through steps such as data collection and upload of the target vehicle terminal, data processing of the target server terminal, interactive analysis, statistics, and classification, accurate positioning of the network abnormal maintenance node is achieved.

[0097] The positioning method of the network anomaly maintenance node of a vehicle according to an embodiment of the present application performs data analysis based on the acquired current network status data, obtains the network data to be marked of the vehicle according to the parsing result, and performs data segment division to obtain at least one sample segment. If there is an abnormal wake-up source in at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source and the network anomaly maintenance time period, and perform key index analysis on the network data to be marked in each sample segment. Determine the cause of the network anomaly maintenance of the vehicle according to the analysis result, and then locate the network anomaly maintenance node of the vehicle. Thus, it solves the problem that the abnormal network maintenance positioning method in the related technology cannot accurately locate the specific node and its cause leading to network anomaly maintenance, resulting in the vehicle network being unable to normally sleep and causing problems such as power loss. By performing steps such as data analysis, segment division, and abnormal network determination on the current network status data of the vehicle, the network anomaly can be distinguished, and the network anomaly maintenance node of the vehicle can be located, thereby improving the network anomaly detection accuracy, maliciously discovering abnormal nodes in advance, and reducing the test cost.

[0098] Next, a positioning device for the network anomaly maintenance node of a vehicle according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0099] Figure 2 It is a block diagram of a positioning device for the network anomaly maintenance node of a vehicle according to an embodiment of the present application.

[0100] As Figure 2 shown, the positioning device 10 for the network anomaly maintenance node of the vehicle includes: a data division module 100, a determination module 200, and a positioning module 300.

[0101] Among them, the data division module 100 is used to acquire the current network status data of the vehicle, perform data analysis on the current network status data, obtain the network data to be marked of the vehicle according to the parsing result, and perform data segment division on the network data to be marked to obtain at least one sample segment;

[0102] The determination module 200 is used to determine whether there is an abnormal wake-up source in at least one sample segment. If there is an abnormal wake-up source in at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source, and determine the network anomaly maintenance time period of the target sample segment;

[0103] The positioning module 300 is used to perform key index analysis on the network data to be marked in each sample segment based on at least one sample segment, and determine the cause of the network anomaly maintenance of the vehicle according to the analysis result, so as to locate the network anomaly maintenance node of the vehicle based on the network anomaly maintenance time period and the cause of the network anomaly maintenance.

[0104] According to an embodiment of the present application, the data partitioning module 100 includes:

[0105] A first determination unit, configured to determine whether the current network status data has a jump, or whether the time interval between the first frame of network status data and the second frame of network status data in the current network status data is greater than a preset time interval;

[0106] A first judgment unit, configured to determine that the current network status data is in an abnormal state if the current network status data has a jump, or if the time interval between the first frame of network status data and the second frame of network status data is greater than a preset time interval;

[0107] A marking unit, configured to mark the current network status data according to the time sorting of the first time point when the current network status data has a jump or the second time point when the time interval is greater than the preset time interval, so as to obtain the network data to be marked of the vehicle.

[0108] According to an embodiment of the present application, the data partitioning module 100 includes:

[0109] A data partitioning unit, configured to partition data segments of two adjacent current network status data in the network data to be marked based on the network data to be marked of the vehicle, so as to obtain at least one sample segment in the network data to be marked.

[0110] According to an embodiment of the present application, the determination module includes:

[0111] A first data analysis unit, configured to perform data analysis on each of at least one sample segment, and determine whether the first frame of network status data and the second frame of network status data in each sample segment are in a continuous state;

[0112] A second judgment unit, configured to determine that there is an abnormal wake-up source in at least one sample segment if the first frame of network status data and the second frame of network status data are not in a continuous state.

[0113] According to an embodiment of the present application, the determination module 200 includes:

[0114] A second data analysis unit, configured to perform data analysis on each of at least one sample segment to obtain the proportion of the vehicle's key state being in an unstarted state;

[0115] A screening unit, configured to screen at least one abnormal network data segment that meets the preset proportion condition from the proportion of the vehicle's key state being in an unstarted state, so as to determine the network anomaly maintenance time period based on at least one abnormal network data segment.

[0116] According to an embodiment of the present application, the positioning module 300 includes:

[0117] A determination unit, configured to obtain the start time and end time of each sample segment in at least one sample segment, and based on the start time and end time, perform interaction analysis on the abnormal objects of the vehicle to determine the abnormal target object of the vehicle;

[0118] A first acquisition unit, configured to, when the abnormal target object of the vehicle is the target vehicle end, acquire the number of active alarm reports of the vehicle in at least one sample segment and the proportion of the maintenance source information of all working nodes; when the abnormal target object of the vehicle is the target server end, acquire the number of remote controls of the vehicle, and at the same time, acquire the number of awakenings of the vehicle when there is an abnormal awakening source in at least one sample segment, the proportion of the on-vehicle device activation duration in at least one sample segment, and the proportion of the OTA duration;

[0119] A second judgment unit, configured to judge whether the number of awakenings of the vehicle is greater than a preset awakening number threshold, or whether the proportion of the on-vehicle device activation duration is greater than a first preset duration proportion, or whether the proportion of the OTA duration is greater than a second preset duration proportion, or whether the number of remote controls of the vehicle is greater than a preset remote control number threshold, or whether the number of active alarm reports is greater than the active alarm report number threshold, or whether the proportion of the maintenance source information of all working nodes is greater than a preset proportion threshold;

[0120] A third determination unit, configured to, if the number of awakenings of the vehicle is greater than the preset awakening number threshold, determine that the reason for the abnormal network maintenance of the vehicle is frequent awakening, or, if the proportion of the on-vehicle device activation duration is greater than the first preset duration proportion, determine that the reason for the abnormal network maintenance of the vehicle is manual intervention awakening, or if the proportion of the OTA duration is greater than the second preset duration proportion or the number of remote controls of the vehicle is greater than the preset remote control number threshold, determine that the reason for the abnormal network maintenance of the vehicle is manual remote control awakening, or, if the number of active alarm reports is greater than the active alarm report number threshold or the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold, determine that the reason for the abnormal network maintenance of the vehicle is abnormal awakening of the target vehicle end.

[0121] The positioning device of the network anomaly maintenance node of the vehicle according to the embodiment of the present application performs data analysis based on the acquired current network status data, obtains the network data to be marked of the vehicle according to the parsing result, and performs data segment division to obtain at least one sample segment. If there is an abnormal wake-up source in the at least one sample segment, record the abnormal wake-up source information and the network anomaly maintenance time period corresponding to the target sample segment with the abnormal wake-up source, and perform key index analysis on the network data to be marked in each sample segment. Determine the reason for the network anomaly maintenance of the vehicle according to the analysis result, and then locate the network anomaly maintenance node of the vehicle. Thereby, it solves the problem that the abnormal network maintenance positioning method in the related technology cannot accurately locate the specific node and its reason that cause the network anomaly maintenance, resulting in the vehicle network unable to sleep normally and causing problems such as power loss. By performing steps such as data analysis, segment division, and abnormal network determination on the current network status data of the vehicle, the network anomaly can be distinguished, and the network anomaly maintenance node of the vehicle can be located, thereby improving the network anomaly detection accuracy, and maliciously discovering abnormal nodes in advance and reducing the test cost.

[0122] Figure 3 It is a schematic structural diagram of an electronic device provided by an embodiment of the present application. The electronic device may include:

[0123] A memory 301, a processor 302, and a computer program stored on the memory 301 and executable on the processor 302.

[0124] When the processor 302 executes the program, it implements the positioning method of the network anomaly maintenance node of the vehicle provided in the above embodiment.

[0125] Further, the electronic device further includes:

[0126] A communication interface 303 for communication between the memory 301 and the processor 302.

[0127] The memory 301 is used to store a computer program executable on the processor 302.

[0128] The memory 301 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0129] If the memory 301, the processor 302, and the communication interface 303 are implemented independently, the communication interface 303, the memory 301, and the processor 302 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 only a thick line is used to represent it in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0130] Optionally, in a specific implementation, if the memory 301, the processor 302, and the communication interface 303 are integrated on a single chip, the memory 301, the processor 302, and the communication interface 303 can communicate with each other through an internal interface.

[0131] The processor 302 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0132] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the positioning method of the network anomaly maintenance node of the vehicle as described above is implemented.

[0133] This embodiment also provides a computer program product, including a computer program, and the computer program is executed to implement the positioning method of the network anomaly maintenance node of the vehicle in the above embodiment.

[0134] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc., mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0135] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0136] Any process or method description shown in the flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or more executable instructions for implementing a customized logical function or process. And the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a manner that may not be in the order shown or discussed, including in a substantially simultaneous manner according to the functions involved or in the reverse order, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0137] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definable sequence list of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or used in combination with these instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in combination with an instruction execution system, apparatus, or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0138] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0139] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0140] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist separately physically for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0141] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A positioning method for a network anomaly maintenance node of a vehicle, characterized in that, Including the following steps: Obtain the current network status data of the vehicle, perform data analysis on the current network status data, obtain the network data to be marked of the vehicle according to the parsing result, and perform data segment division on the network data to be marked to obtain at least one sample segment; Determine whether there is an abnormal wake-up source in the at least one sample segment. If there is an abnormal wake-up source in the at least one sample segment, record the abnormal wake-up source information corresponding to the target sample segment with the abnormal wake-up source, and determine the network anomaly maintenance time period of the target sample segment; Based on the at least one sample segment, perform key index analysis on the network data to be marked in each sample segment, and determine the reason for the network anomaly maintenance of the vehicle according to the analysis result, so as to locate the network anomaly maintenance node of the vehicle based on the network anomaly maintenance time period and the reason for the network anomaly maintenance.

2. The method according to claim 1, characterized in that, The performing data analysis on the current network status data to obtain the network data to be marked of the vehicle according to the parsing result includes: Determine whether the current network status data has a jump, or whether the time interval between the first frame of network status data and the second frame of network status data in the current network status data is greater than a preset time interval; If the current network status data has a jump, or the time interval between the first frame of network status data and the second frame of network status data is greater than the preset time interval, it is determined that the current network status data is in an abnormal state; Based on the first time point when the current network status data has a jump or the second time point when the time interval is greater than the preset time interval, mark the current network status data according to the time sorting of the first time point and the second time point to obtain the network data to be marked of the vehicle.

3. The method according to claim 1, wherein The performing data segment division on the network data to be marked to obtain at least one sample segment includes: Based on the network data to be marked of the vehicle, perform data segment division on two adjacent current network status data in the network data to be marked to obtain at least one sample segment in the network data to be marked.

4. The method according to claim 1, characterized in that, The determining whether there is an abnormal wake-up source in the at least one sample segment includes: Perform data analysis on each sample segment in the at least one sample segment, and determine whether the first frame of network status data and the second frame of network status data in each sample segment are in a continuous state; If the first frame of network status data and the second frame of network status data are not in the continuous state, it is determined that there is an abnormal wake-up source in the at least one sample segment.

5. The method according to claim 1, wherein The determining the network anomaly maintenance time period of the target sample segment includes: Perform data analysis on each sample segment in the at least one sample segment to obtain the proportion of the key state of the vehicle being in the non-started state; Among the proportions of the key state of the vehicle being in the non-started state, screen at least one abnormal network data segment that meets the preset proportion condition, so as to determine the network anomaly maintenance time period based on the at least one abnormal network data segment.

6. The method according to claim 1, wherein Performing key index analysis on the network data to be marked in each sample segment, and determining the reason for maintaining network anomalies of the vehicle according to the analysis results, including: Obtaining the start time and end time of each sample segment in the at least one sample segment, and based on the start time and the end time, performing interaction analysis on the abnormal objects of the vehicle to determine the abnormal target object of the vehicle; When the abnormal target object of the vehicle is the target vehicle end, obtaining the number of active reported alarms of the vehicle in the at least one sample segment and the proportion of the maintenance source information of all working nodes; when the abnormal target object of the vehicle is the target server end, obtaining the number of remote controls of the vehicle, and at the same time, obtaining the number of awakenings of the vehicle when there is the abnormal awakening source in the at least one sample segment, the proportion of the opening duration of in-vehicle devices in the at least one sample segment, and the proportion of the OTA duration of remote upgrade technology; Judging whether the number of awakenings of the vehicle is greater than a preset awakening number threshold, or whether the proportion of the opening duration of in-vehicle devices is greater than a first preset duration proportion, or whether the OTA duration proportion is greater than a second preset duration proportion, or whether the number of remote controls of the vehicle is greater than a preset remote control number threshold, or whether the number of active reported alarms is greater than an active reported alarm number threshold, or whether the proportion of the maintenance source information of all working nodes is greater than a preset proportion threshold; If the number of awakenings of the vehicle is greater than the preset awakening number threshold, it is determined that the reason for maintaining the network anomaly of the vehicle is frequent awakening, or if the proportion of the opening duration of in-vehicle devices is greater than the first preset duration proportion, it is determined that the reason for maintaining the network anomaly of the vehicle is manual intervention awakening, or if the OTA duration proportion is greater than the second preset duration proportion or the number of remote controls of the vehicle is greater than the preset remote control number threshold, it is determined that the reason for maintaining the network anomaly of the vehicle is manual remote control awakening, or if the number of active reported alarms is greater than the active reported alarm number threshold or the proportion of the maintenance source information of all working nodes is greater than the preset proportion threshold, it is determined that the reason for maintaining the network anomaly of the vehicle is abnormal awakening of the target vehicle end.

7. A positioning device for a network anomaly maintenance node of a vehicle, characterized in that, Including: A data division module, configured to obtain the current network state data of the vehicle, perform data analysis on the current network state data, obtain the network data to be marked of the vehicle according to the analysis result, and perform data segment division on the network data to be marked to obtain at least one sample segment; A determination module, configured to judge whether there is an abnormal awakening source in the at least one sample segment. If there is the abnormal awakening source in the at least one sample segment, record the abnormal awakening source information corresponding to the target sample segment with the abnormal awakening source, and determine the network anomaly maintenance time period of the target sample segment; A positioning module, configured to perform key indicator analysis on the network data to be marked in each sample segment based on the at least one sample segment, and determine the reason for the network anomaly maintenance of the vehicle according to the analysis result, so as to locate the network anomaly maintenance node of the vehicle based on the network anomaly maintenance time period and the reason for the network anomaly maintenance.

8. The device according to claim 7, characterized in that, The obtaining module includes: A judgment unit, configured to judge whether the current network status data has a jump, or whether the time interval between the first frame of network status data and the second frame of network status data in the current network status data is greater than a preset time interval; A determination unit, configured to determine that the current network status data is in an abnormal state if the current network status data has a jump, or the time interval between the first frame of network status data and the second frame of network status data is greater than the preset time interval; A marking unit, configured to mark the current network status data according to the time sorting of the first time point at which the current network status data has a jump or the second time point at which the time interval is greater than the preset time interval, so as to obtain the network data to be marked of the vehicle.

9. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the method for locating the network anomaly maintenance node of the vehicle according to any one of claims 1-6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used to implement the method for locating the network anomaly maintenance node of the vehicle according to any one of claims 1-6.

Images