Vehicle position abnormality detection method, system and vehicle

Abstract

The present application provides a method, system and vehicle for detecting abnormal vehicle position, and relates to the field of vehicle fault diagnosis technology, wherein the method comprises: obtaining vehicle status data of the current vehicle uploaded by the vehicle end; inputting the vehicle status data into a pre-created fault tree detection model, so that when the fault tree detection model detects the existence of a target bottom event that meets a preset trigger rule, the target bottom event is detected, and a target fault path corresponding to the target bottom event is generated by the fault tree detection model according to the target bottom event, and the target bottom event and the target fault path are fed back to the vehicle end and / or the back-end. The present application detects the vehicle status data sent by the vehicle end through a pre-created fault tree detection model, and can remotely and quickly locate the fault problem based on the vehicle status data, so that the root cause of the fault problem can be quickly and accurately identified without the need for localized data collection and analysis, thereby improving the speed and accuracy of fault problem location and improving the user's vehicle experience.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle fault diagnosis, and in particular to a method and system for detecting abnormal vehicle position, and a vehicle. Background Art

[0002] At present, with the continuous development of transportation networks, the application of vehicle positioning systems has played a vital role in transportation networks. Therefore, failure of vehicle positioning systems will have serious impacts on transportation networks, such as inaccurate navigation, traffic congestion, and even serious safety hazards.

[0003] The most common vehicle positioning system anomaly is abnormal vehicle position, which is caused by many factors, such as signal interference, weak satellite signal, hardware failure, etc. When a vehicle has an abnormal vehicle position, the owner needs to send the vehicle to a 4S store or service station for problem positioning detection, which is not only time-consuming and labor-intensive, but also brings a bad driving experience to users.

[0004] Therefore, a new method for detecting vehicle position anomaly is urgently needed. Summary of the invention

[0005] In view of the above problems, embodiments of the present application provide a vehicle position abnormality detection method, system and vehicle to overcome the above problems or at least partially solve the above problems.

[0006] In a first aspect of an embodiment of the present application, a method for detecting abnormal vehicle position is provided, the method comprising:

[0007] Obtain vehicle status data of the current vehicle uploaded by the vehicle end, wherein the vehicle status data includes: location data, DTC fault data and / or log data;

[0008] Inputting the vehicle status data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data through the fault tree detection model;

[0009] When a target bottom event that meets the preset trigger rule is detected, a target fault path corresponding to the target bottom event is generated through the fault tree detection model according to the target bottom event, and the target bottom event and the target fault path are fed back to the vehicle end and / or the end-sales end as detection results.

[0010] Optionally, after determining the target fault path, the method further includes:

[0011] Determine the fault type causing the abnormal fault of the current vehicle position according to the target bottom event corresponding to the target fault path, wherein the fault type includes a hardware fault type and / or a software fault type;

[0012] According to the fault type, a troubleshooting solution corresponding to the fault type is generated, and the troubleshooting solution is fed back to the back-end sales department.

[0013] Optionally, when there are multiple target bottom events satisfying preset trigger rules, generating a target fault path corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes:

[0014] According to each of the target bottom events, generating a fault path corresponding to each of the target bottom events through the fault tree detection model;

[0015] Determine whether the target events are related events to each other;

[0016] If the target bottom events are mutually related events, then determining the target fault path from the fault paths corresponding to the target bottom events that are mutually related events;

[0017] If the target bottom events are mutually unrelated events, the fault path corresponding to each target bottom event is determined as the target fault path.

[0018] Optionally, generating a target fault path corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes:

[0019] According to the target bottom event, determining a target intermediate event corresponding to the target bottom event through the fault tree detection model;

[0020] According to the target intermediate event, determining the target top event corresponding to the target intermediate event through the fault tree detection model;

[0021] According to the association relationship among the target bottom event, the target intermediate event and the target top event, a link formed by the target bottom event, the target intermediate event and the target top event is determined as the target fault path.

[0022] Optionally, in the case where there are multiple target bottom events satisfying preset trigger rules, determining the target intermediate event corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes:

[0023] According to the mapping relationship between the target base event and the target intermediate event, the target intermediate event corresponding to at least one of the target base events is determined through the fault tree detection model; wherein the mapping relationship represents that the target base event and the target intermediate event are in a one-to-one matching relationship or the target base event and the target intermediate event are in a many-to-one matching relationship.

[0024] Optionally, in the case where there are multiple target intermediate events, determining the target top event corresponding to the target intermediate event by using the fault tree detection model according to the target intermediate event includes:

[0025] According to the mapping relationship between the target intermediate event and the target top event, the target top event corresponding to at least one of the target intermediate events is determined through the fault tree detection model; wherein the mapping relationship between the target intermediate event and the target top event represents that the target intermediate event and the target top event have a one-to-one matching relationship or the target intermediate event and the target top event have a many-to-one matching relationship.

[0026] Optionally, the fault tree detection model is created by the following steps:

[0027] Taking the abnormal vehicle position fault as the top event of the fault tree detection model, and analyzing multiple intermediate events provided by the vehicle end that cause the abnormal vehicle position fault and multiple bottom events corresponding to each of the intermediate events;

[0028] According to the association relationship between the top event and each of the intermediate events, the top event is associated with each of the intermediate events respectively;

[0029] According to the association relationship between each intermediate event and each bottom event corresponding to the intermediate event, each intermediate event is associated with each bottom event corresponding to the intermediate event to obtain the fault tree detection model.

[0030] Optionally, after the fault tree detection model is created, the method further includes:

[0031] Determine, according to the historical status data of the vehicle, the target field corresponding to each bottom event in the fault tree detection model;

[0032] For each target field corresponding to each of the bottom events, a corresponding trigger rule is set, wherein the trigger rule is that the number of cumulative occurrences of the target field within a preset time period reaches a preset number;

[0033] The detecting whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data includes:

[0034] Detect all the bottom events in the vehicle status data, and determine the bottom event whose cumulative number of target field appearances reaches a preset number within a preset time length as the target bottom event.

[0035] In a second aspect of an embodiment of the present application, a vehicle position abnormality detection system is provided, the system comprising:

[0036] An acquisition module, used to acquire vehicle status data of the current vehicle uploaded by the vehicle end, wherein the vehicle status data includes: location data, DTC fault data and / or log data;

[0037] A detection module, used for inputting the vehicle status data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data through the fault tree detection model;

[0038] A generation module is used to generate a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event when a target bottom event that meets the preset trigger rule is detected, and feed back the target bottom event and the target fault path as detection results to the vehicle end and / or the end-of-sales end.

[0039] Optionally, after determining the target fault path, the method further includes:

[0040] A first determination submodule, configured to determine the fault type causing the abnormal fault of the current vehicle position according to the target bottom event corresponding to the target fault path, wherein the fault type includes a hardware fault type and / or a software fault type;

[0041] The first generating submodule is used to generate a troubleshooting solution corresponding to the fault type according to the fault type, and feed back the troubleshooting solution to the back-end sales department.

[0042] Optionally, in the case where there are multiple target bottom events that meet the preset triggering rules, the generating module generates a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event, and the generating module includes:

[0043] A second generating submodule is used to generate a fault path corresponding to each target bottom event through the fault tree detection model according to each target bottom event;

[0044] A judgment submodule, used for judging whether each of the target bottom events is a related event;

[0045] A second determination submodule is used for determining the target fault path from the fault paths corresponding to the target bottom events that are mutually related events if the target bottom events are mutually related events;

[0046] The second determining submodule is configured to determine the fault path corresponding to each of the target bottom events as a target fault path if the target bottom events are mutually unrelated events.

[0047] Optionally, generating a target fault path corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes:

[0048] A first determining subunit is used to determine a target intermediate event corresponding to the target bottom event through the fault tree detection model according to the target bottom event;

[0049] A second determination subunit is used to determine, according to the target intermediate event, a target top event corresponding to the target intermediate event through the fault tree detection model;

[0050] The third determining subunit is used to determine the link composed of the target bottom event, the target intermediate event and the target top event as the target fault path according to the association relationship among the target bottom event, the target intermediate event and the target top event.

[0051] Optionally, in the case where there are multiple target bottom events satisfying a preset trigger rule, the target intermediate event corresponding to the target bottom event is determined by the fault tree detection model according to the target bottom event, and the first determining subunit includes:

[0052] The fourth determination subunit is used to determine the target intermediate event corresponding to at least one of the target base events through the fault tree detection model according to the mapping relationship between the target base event and the target intermediate event; wherein the mapping relationship represents that the target base event and the target intermediate event are in a one-to-one matching relationship or the target base event and the target intermediate event are in a many-to-one matching relationship.

[0053] Optionally, in the case where there are multiple target intermediate events, the target top event corresponding to the target intermediate event is determined by the fault tree detection model according to the target intermediate event, and the second determining subunit includes:

[0054] The fifth determination subunit is used to determine the target top event corresponding to at least one of the target intermediate events through the fault tree detection model according to the mapping relationship between the target intermediate events and the target top events; wherein the mapping relationship between the target intermediate events and the target top events represents that the target intermediate events and the target top events have a one-to-one matching relationship or the target intermediate events and the target top events have a many-to-one matching relationship.

[0055] Optionally, the fault tree detection model is created by the following steps:

[0056] An event analysis submodule, used to take the abnormal vehicle position fault as the top event of the fault tree detection model, and analyze multiple intermediate events provided by the vehicle end that cause the abnormal vehicle position fault and multiple bottom events corresponding to each of the intermediate events;

[0057] An event association submodule, used for associating the top event with each of the intermediate events according to the association relationship between the top event and each of the intermediate events;

[0058] The model creation submodule is used to associate each intermediate event with each bottom event corresponding to the intermediate event according to the association relationship between each intermediate event and each bottom event corresponding to the intermediate event, so as to obtain the fault tree detection model.

[0059] Optionally, after the fault tree detection model is created, the method further includes:

[0060] A third determination submodule is used to determine the target field corresponding to each bottom event in the fault tree detection model according to the historical status data of the vehicle;

[0061] A setting submodule is used to set a trigger rule for each target field corresponding to each of the bottom events, wherein the trigger rule is that the number of cumulative occurrences of the target field within a preset time period reaches a preset number;

[0062] The detecting whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data includes:

[0063] Detect all the bottom events in the vehicle status data, and determine the bottom event whose cumulative number of target field appearances reaches a preset number within a preset time length as the target bottom event.

[0064] In a third aspect of an embodiment of the present application, a vehicle is provided, wherein the vehicle includes the vehicle position anomaly detection system as described in the second aspect of the present application, and / or executes the vehicle position anomaly detection method as described in the first aspect of the present application.

[0065] Beneficial effects of this application:

[0066] The present application provides a method for detecting abnormal vehicle position, the method comprising: obtaining vehicle status data of the current vehicle uploaded by the vehicle end, the vehicle status data comprising: location data, DTC fault data and / or log data; inputting the vehicle status data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event that meets the preset trigger rule in the vehicle status data through the fault tree detection model; in the case of detecting the existence of a target bottom event that meets the preset trigger rule, according to the target bottom event, generating a target fault path corresponding to the target bottom event through the fault tree detection model, and feeding back the target bottom event and the target fault path as the detection result to the vehicle end and / or the back-end. The present application detects the vehicle status data sent by the vehicle end through a pre-created fault tree detection model, and can remotely and quickly locate the fault problem according to the vehicle status data, so as to quickly and accurately identify the root cause of the fault problem, without the need for localized data collection and analysis, thereby improving the speed and accuracy of fault problem location and improving the user's car experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments of the present application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 It is a schematic diagram of the steps of a method for detecting abnormal vehicle position provided by an embodiment of the present application;

[0069] Figure 2 It is a model schematic diagram of a fault tree detection model provided in an embodiment of the present application;

[0070] Figure 3 It is a flowchart of a vehicle position abnormality detection method provided in an embodiment of the present application;

[0071] Figure 4 It is a schematic diagram of a vehicle position anomaly detection system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0072] The exemplary embodiments of the present application will be described in more detail below in conjunction with the accompanying drawings in the embodiments of the present application. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to enable the scope of the present application to be fully communicated to those skilled in the art.

[0073] In a first aspect of the embodiments of the present application, a method for detecting abnormal vehicle position is provided. Figure 1 As shown, including:

[0074] Step S101, obtaining vehicle status data of the current vehicle uploaded by the vehicle end, wherein the vehicle status data includes: location data, DTC fault data and / or log data;

[0075] In this embodiment, the vehicle side can upload the current vehicle status data to the cloud platform in real time. The vehicle status data described in this application specifically refers to the vehicle's location data, the vehicle's DTC fault data and / or the vehicle's log data.

[0076] Specifically, in this embodiment, for location data, the vehicle side can obtain the current vehicle location information in real time through the GPS module, and then encapsulate the latitude and longitude coordinates and timestamp and other data, and use the on-board communication module (such as 4G / 5G communication module) to upload the location data in the form of a data packet to the cloud platform. In actual applications, MQTT, HTTP or other communication protocols can be used to communicate with the cloud platform and transmit the location data to the cloud platform.

[0077] For DTC fault data, the vehicle side can regularly scan the status of various systems of the vehicle through the vehicle's diagnostic system, such as the status of the positioning system, the status of the engine system or the status of the transmission system, etc. When the diagnostic system detects a system fault, a DTC fault code is generated and recorded. Similarly, the vehicle side can upload the DTC fault data in the form of a data packet to the cloud platform through the on-board communication module (such as a 4G / 5G communication module).

[0078] Log data includes various event records, system operation status, fault information, etc. during vehicle operation. For log data, the vehicle can regularly record this information through the vehicle's internal system or equipment and save it in a log file. Similarly, the log file or a summary of the log data can be uploaded to the cloud platform through the vehicle communication module (such as a 4G / 5G communication module).

[0079] The embodiment of the present application provides a data interface for receiving data packets for the cloud platform, and receives the location data, DTC fault data and / or log data uploaded by the vehicle through the data interface. In practical applications, this data interface can be in the form of HTTP interface, MQTT topic subscription, WebSocket connection, etc., and can be specifically set according to the communication protocol of the communication module on the vehicle side, which is not limited in the present application.

[0080] Step S102, inputting the vehicle state data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event satisfying a preset trigger rule in the vehicle state data through the fault tree detection model;

[0081] Specifically, after the cloud platform obtains the vehicle status data uploaded by the vehicle, it needs to parse and filter the vehicle status data, retain the data related to the location, and filter out other irrelevant data.

[0082] Furthermore, the processed data is input into a pre-created fault tree detection model, and the fault tree detection model analyzes and processes the input vehicle status data to detect whether there is a target bottom event that meets a preset trigger rule.

[0083] Step S103, when a target bottom event that meets the preset trigger rule is detected, a target fault path corresponding to the target bottom event is generated through the fault tree detection model according to the target bottom event, and the target bottom event and the target fault path are fed back to the vehicle end and / or the end-sales end as detection results.

[0084] Specifically, in an embodiment of the present application, when the cloud platform detects the presence of a target bottom event that meets preset trigger rules, the cloud platform generates a target fault path corresponding to the target bottom event through a pre-created fault tree detection model.

[0085] Furthermore, the cloud platform feeds back the generated target fault path and the corresponding target bottom event as the detection result to the vehicle side and / or the back-end.

[0086] This application detects the vehicle status data sent by the vehicle end through a pre-created fault tree detection model, and can remotely and quickly locate the fault problem based on the vehicle status data, so that the root cause of the fault problem can be quickly and accurately identified without the need for localized data collection and analysis, and without the need for manual query of the maintenance manual, thereby improving the speed and accuracy of fault problem locating and improving the user's car experience.

[0087] In a preferred embodiment, the fault tree detection model can be created by the following steps:

[0088] Taking the abnormal vehicle position fault as the top event of the fault tree detection model, and analyzing multiple intermediate events provided by the vehicle end that cause the abnormal vehicle position fault and multiple bottom events corresponding to each of the intermediate events;

[0089] According to the association relationship between the top event and each of the intermediate events, the top event is associated with each of the intermediate events respectively;

[0090] According to the association relationship between each intermediate event and each bottom event corresponding to the intermediate event, each intermediate event is associated with each bottom event corresponding to the intermediate event to obtain the fault tree detection model.

[0091] Specifically, it is necessary to first determine the top event of the fault tree detection model. In this embodiment, the abnormal vehicle position fault is used as the top event of the fault tree detection model. Then, according to the historical vehicle status data uploaded by the vehicle end, all the intermediate events with the association relationship between the top event are analyzed, wherein the intermediate event is a fault event with an association relationship with the abnormal vehicle position fault. For example: the intermediate event can be a GNSS positioning problem, a problem that the vehicle position cannot be obtained due to the disagreement of the location authorization, and a TBOX problem causing an abnormal location query problem, etc. It should be noted that there can be multiple sub-level intermediate events with an association relationship in the intermediate event, for example: for the intermediate event TBOX problem causing an abnormal location query problem, multiple sub-level intermediate events with an association relationship include: cellular antenna open circuit problem, SIM card problem, EMMC memory problem, trust ring problem, and Ethernet failure problem, etc. After determining the intermediate event with an association relationship with the top event, the bottom event with an association relationship with each intermediate event is further analyzed, wherein the bottom event is the root cause of the abnormal vehicle position fault.

[0092] Furthermore, according to the association relationship between the top event and each intermediate event, the top event is associated with each intermediate event. According to the association relationship between each intermediate event and its corresponding bottom event, each intermediate event is associated with its corresponding bottom event. The above association relationships are integrated to form a fault tree detection model, wherein the nodes of the fault tree detection model include the top event, the intermediate event and the bottom event.

[0093] In a preferred embodiment, there is provided a Figure 2The model schematic diagram of the fault tree detection model shown is, wherein the top event of the fault tree detection model is the abnormal vehicle position fault, wherein the intermediate events associated with the abnormal vehicle position fault include: GNSS positioning problem, the problem of being unable to obtain the vehicle position due to disagreement of the location authorization, and the problem of abnormal location query due to the TBOX problem, wherein the multiple sub-level intermediate events associated with the intermediate event TBOX problem causing abnormal location query problem include: cellular antenna open circuit problem, SIM card problem, EMMC memory problem, trust ring problem, and Ethernet failure problem, etc. For the intermediate event GNSS positioning problem, the bottom events associated with it include: the number of GNSS search satellites is less than 3, the GPS antenna is short-circuited to the ground, the GPS antenna is open, and the GNSS positioning status is invalid; for the intermediate event location authorization is not agreed to, resulting in the inability to obtain the vehicle location, the bottom events associated with it include: vehicle authorization is not agreed, APP authorization is not agreed; for the intermediate event TBOX problem leading to abnormal location query, the bottom event directly associated with it is the battery voltage is too high; for the intermediate event cellular antenna open circuit problem, the bottom events associated with it include: cellular main antenna open circuit to the ground, cellular diversity antenna open circuit to the ground; for the intermediate event cellular network problem, the bottom events associated with it include: dialing failure, network registration failure, poor network signal strength Failure, poor network signal quality failure; for the intermediate event SIM card problem, the bottom events associated with it include: SIM card failure; for the intermediate event EMMC memory problem, the bottom events associated with it include: EMMC communication exception, EMMC mounting abnormality, EMMC life expiration; for the intermediate event trust ring problem, the bottom events associated with it include: DevList storage failure, DevList signature verification failure, and VDC certification failed, and MDC certification failed, and CDC certification failed, and VIU1 certification failed, and VIU2 certification failed, and VIU3 certification failed; for the intermediate event Ethernet network failure, the bottom events associated with it include: Ethernet link failure, Ethernet signal poor quality failure, Ethernet open circuit, and Ethernet short circuit.

[0094] In a preferred embodiment, after the fault tree detection model is created, the method further includes:

[0095] Determine, according to the historical status data of the vehicle, the target field corresponding to each bottom event in the fault tree detection model;

[0096] For each target field corresponding to each of the bottom events, a corresponding trigger rule is set, wherein the trigger rule is that the number of cumulative occurrences of the target field within a preset time period reaches a preset number;

[0097] The detecting whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data includes:

[0098] Detect all the bottom events in the vehicle status data, and determine the bottom event whose cumulative number of target field appearances reaches a preset number within a preset time length as the target bottom event.

[0099] Specifically, in this implementation, after the fault tree detection model is created, a corresponding trigger rule needs to be set for each bottom event in the fault tree detection model. The specific trigger rule is: first, according to the historical status data of the vehicle uploaded by the vehicle end, determine the target field corresponding to each bottom event, and set a trigger rule for the target field corresponding to each bottom event. The specific trigger rule is that the cumulative number of occurrences of the target field reaches a preset number of times within a preset time length. By setting the preset trigger rule, when the fault tree detection model is used to detect whether there is a target bottom event that meets the preset trigger rule in the vehicle status data, specifically, among all the bottom events in the vehicle status data, the bottom event whose cumulative number of occurrences of the target field reaches a preset number within a preset time length is detected, and the bottom event whose cumulative number of occurrences of the target field reaches a preset number within a preset time length is determined as the target bottom event.

[0100] The trigger rules preset in this application are shown in Table 1 below:

[0101]

[0102]

[0103]

[0104]

[0105] Table 1

[0106] In a preferred embodiment, after determining the target fault path, the method further includes:

[0107] Determine the fault type causing the abnormal fault of the current vehicle position according to the target bottom event corresponding to the target fault path, wherein the fault type includes a hardware fault type and / or a software fault type;

[0108] According to the fault type, a troubleshooting solution corresponding to the fault type is generated, and the troubleshooting solution is fed back to the back-end sales department.

[0109] Specifically, in this embodiment, according to the target bottom event corresponding to the target fault path, the cloud platform can determine the fault type that causes the current vehicle position abnormality. These fault types may include hardware fault types and / or software fault types. Hardware faults usually refer to problems with physical components in the vehicle, while software faults refer to errors or abnormalities in the program or algorithm in the vehicle.

[0110] Furthermore, for the determined fault type, the cloud platform will generate corresponding troubleshooting solutions, which may include diagnostic steps, repair methods or replacement parts.

[0111] Furthermore, the cloud platform feeds back the generated troubleshooting solution to the back-end sales. In this application, the back-end sales may refer to the after-sales service department of the vehicle manufacturer or an authorized repair center. In this way, after-sales service personnel can repair the vehicle according to the provided troubleshooting solution, ensure the normal operation of the vehicle, and provide users with a high-quality after-sales service experience. In actual applications, for hardware failures, the corresponding troubleshooting solution may be replacing the faulty parts; for software failures, the corresponding troubleshooting solution may be restarting the software, updating the software, calibrating the software, etc.

[0112] In the embodiment of the present application, through the pre-created fault tree detection model, not only can the fault be quickly located and the fault type be identified, but also targeted troubleshooting solutions can be provided according to the specific fault type, thereby quickly and effectively solving the problem of locating faults in the vehicle and improving the reliability of the vehicle and user satisfaction.

[0113] In a preferred embodiment, when there are multiple target bottom events that meet the preset trigger rules, generating a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event includes:

[0114] According to each of the target bottom events, generating a fault path corresponding to each of the target bottom events through the fault tree detection model;

[0115] Determine whether the target events are related events to each other;

[0116] If the target bottom events are mutually related events, then determining the target fault path from the fault paths corresponding to the target bottom events that are mutually related events;

[0117] If the target bottom events are mutually unrelated events, the fault path corresponding to each target bottom event is determined as the target fault path.

[0118] Specifically, in this embodiment, first, for each target bottom event that meets the preset trigger rules, the cloud platform generates its corresponding target fault path through the fault tree detection model. These target fault paths describe the possible faults that cause each target bottom event and the logical relationship between them. Further, the cloud platform will analyze each generated target bottom event to determine whether there is a correlation between them. In this application, correlation refers to whether there is a common root cause or logical relationship between these target bottom events. If there is a correlation between each target bottom event, it means that they may be caused by the same root cause. In this case, the system will determine the target fault path from the fault paths corresponding to each target bottom event of each mutually related event. This means that the system will integrate the target fault paths of these related events to find a common target fault path, so that the best troubleshooting solution can be generated based on this common target fault path, so that the back-end can quickly lock the fault point and solve the fault. If there is no correlation between the target bottom events, that is, they are independent events, then the cloud platform will determine the fault path corresponding to each target bottom event as the target fault path. This means that the cloud platform will treat the fault path of each target bottom event as a separate fault path, regardless of the correlation between them. The cloud platform needs to generate a corresponding troubleshooting solution for each target bottom event and its corresponding target fault path, so as to ensure that the after-sales service department has a more comprehensive understanding and solution to the positioning fault problems of the vehicle.

[0119] In a preferred embodiment, generating a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event includes:

[0120] According to the target bottom event, determining a target intermediate event corresponding to the target bottom event through the fault tree detection model;

[0121] According to the target intermediate event, determining the target top event corresponding to the target intermediate event through the fault tree detection model;

[0122] According to the association relationship among the target bottom event, the target intermediate event and the target top event, a link formed by the target bottom event, the target intermediate event and the target top event is determined as the target fault path.

[0123] Specifically, in this embodiment, the cloud platform determines the target intermediate events related to the target bottom event in the fault tree detection model. The target intermediate event is the direct cause of the bottom event, but not the final root cause. Then, the cloud platform determines the target top event related to the target bottom event in the fault tree detection model based on the determined target intermediate event, where the target top event is the root cause or final failure of the intermediate event. Finally, the cloud platform determines the link composed of the target bottom event, the target intermediate event and the target top event as the target fault path based on the association relationship between them. This link describes the complete path from the top event to the intermediate event and then to the bottom event, revealing the full picture of the fault.

[0124] In this embodiment, through the pre-created fault tree detection model, the cloud platform can analyze the causes of target bottom events in detail and determine the root causes of these events, so as to better understand the faults in the system and provide guidance and basis for fault diagnosis and troubleshooting.

[0125] In a preferred embodiment, when there are multiple target bottom events that meet the preset trigger rules, determining the target intermediate event corresponding to the target bottom event by the fault tree detection model according to the target bottom event includes:

[0126] According to the mapping relationship between the target base event and the target intermediate event, the target intermediate event corresponding to at least one of the target base events is determined through the fault tree detection model; wherein the mapping relationship represents that the target base event and the target intermediate event are in a one-to-one matching relationship or the target base event and the target intermediate event are in a many-to-one matching relationship.

[0127] Specifically, in this embodiment, when there are multiple target bottom events, first, the cloud platform determines the target intermediate event corresponding to at least one target bottom event through the fault tree detection model according to the mapping relationship between each target bottom event and the target intermediate event. The mapping relationship may represent a one-to-one matching relationship between the target bottom event and the target intermediate event, or a many-to-one matching relationship.

[0128] If the mapping relationship represents a many-to-one matching relationship between the target bottom event and the target intermediate event, the cloud platform will map multiple target bottom events to the same target intermediate event. For a one-to-one matching relationship, the cloud platform can find the corresponding target intermediate event through a target bottom event, while for a many-to-one matching relationship, the cloud platform needs to determine the target intermediate event corresponding to the multiple target bottom events through multiple target bottom events. In this embodiment, once the target intermediate event corresponding to the target bottom event is determined, the cloud platform can further determine the corresponding target top event based on these target intermediate events, as well as the association relationship between them, thereby generating a complete target fault path.

[0129] In this embodiment, the cloud platform can capture multiple target bottom events according to preset trigger rules and map them to corresponding target intermediate events, thereby establishing the association of fault paths, which helps the system to more accurately identify faults and provide effective diagnosis and troubleshooting solutions.

[0130] In a preferred embodiment, when there are multiple target intermediate events, determining the target top event corresponding to the target intermediate event by using the fault tree detection model according to the target intermediate event includes:

[0131] According to the mapping relationship between the target intermediate event and the target top event, the target top event corresponding to at least one of the target intermediate events is determined through the fault tree detection model; wherein the mapping relationship between the target intermediate event and the target top event represents that the target intermediate event and the target top event have a one-to-one matching relationship or the target intermediate event and the target top event have a many-to-one matching relationship.

[0132] Specifically, in this embodiment, when there are multiple target intermediate events, first, the cloud platform determines the target top event corresponding to at least one target intermediate event through the fault tree detection model according to the mapping relationship between each target intermediate event and the target top event. The mapping relationship may represent a one-to-one matching relationship between the target intermediate event and the target top event, or a many-to-one matching relationship.

[0133] If the mapping relationship represents a many-to-one matching relationship between the target intermediate event and the target top event, the system may map multiple target intermediate events to the same target top event. For a one-to-one matching relationship, the cloud platform can find the corresponding target top event through a target intermediate event, while for a many-to-one matching relationship, the cloud platform needs to determine the target top event corresponding to the multiple target intermediate events through multiple target intermediate events. In this embodiment, once the target top event corresponding to the target intermediate event is determined, the cloud platform can generate a complete target fault path based on these target top events and the target intermediate events associated with them.

[0134] In this embodiment, the cloud platform can map multiple target intermediate events to corresponding target top events based on the existence of multiple target intermediate events, establish the association of fault paths, and help the system to more accurately identify faults and provide effective diagnosis and processing solutions.

[0135] In a preferred embodiment, there is provided a Figure 3 The flow chart of the vehicle position abnormality detection method shown in FIG. Figure 3 As shown: First, create a fault tree detection model, and after the fault tree detection model is created, configure trigger rules for each bottom event in the fault tree detection model. After the above steps are completed, the fault tree detection model is put online on the cloud platform, and then, the data uploaded by the vehicle is obtained through the cloud platform. The specific data type can be location data, fault data and / or log data, among which the location data comes from the location data report of the vehicle, the fault data comes from the fault report of the vehicle, and the log data comes from the log report of the vehicle. It should be noted that in order to obtain the location data, it is necessary to obtain the location reporting authorization first.

[0136] Further, after obtaining the data reported by the vehicle side, determine whether there is a target bottom event that triggers the rule. If there is a target bottom event that triggers the rule, then continue to determine the corresponding target intermediate event based on the triggered target bottom event. If there is no target bottom event that triggers the rule, it means that there is no abnormal vehicle position fault at present; further, if there is a target intermediate event corresponding to the target bottom event, then continue to determine the corresponding target top event based on the triggered target intermediate event. If there is no target intermediate event corresponding to the target bottom event, then establish a partial fault path based on the currently triggered target bottom event; further, if there is a target top event corresponding to the target intermediate event, then create a complete fault path based on the currently triggered target bottom event, target intermediate event and target top event. If there is no target top event corresponding to the target intermediate event, then create a partial fault path based on the currently triggered target bottom event and target intermediate event. Further, push the vehicle position abnormality according to the created fault path, and push the fault detection result to the vehicle side and / or the back-end.

[0137] The present application provides a method for detecting abnormal vehicle position, the method comprising: obtaining vehicle status data of the current vehicle uploaded by the vehicle end, the vehicle status data comprising: location data, DTC fault data and / or log data; inputting the vehicle status data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event that meets the preset trigger rule in the vehicle status data through the fault tree detection model; in the case of detecting the existence of a target bottom event that meets the preset trigger rule, according to the target bottom event, generating a target fault path corresponding to the target bottom event through the fault tree detection model, and feeding back the target bottom event and the target fault path as the detection result to the vehicle end and / or the back-end. The present application detects the vehicle status data sent by the vehicle end through a pre-created fault tree detection model, and can remotely and quickly locate the fault problem according to the vehicle status data, so as to quickly and accurately identify the root cause of the fault problem, without the need for localized data collection and analysis, thereby improving the speed and accuracy of fault problem location and improving the user's car experience.

[0138] Based on the same inventive concept, in a second aspect of the embodiment of the present application, a vehicle position abnormality detection system is provided. Figure 4 As shown, including:

[0139] The acquisition module 201 is used to acquire the vehicle status data of the current vehicle uploaded by the vehicle end, and the vehicle status data includes: location data, DTC fault data and / or log data;

[0140] A detection module 202 is used to input the vehicle state data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event that meets a preset trigger rule in the vehicle state data through the fault tree detection model;

[0141] The generation module 203 is used to generate a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event when a target bottom event that meets the preset trigger rule is detected, and feed back the target bottom event and the target fault path as detection results to the vehicle end and / or the end-of-sales end.

[0142] Optionally, after determining the target fault path, the method further includes:

[0143] A first determination submodule, configured to determine the fault type causing the abnormal fault of the current vehicle position according to the target bottom event corresponding to the target fault path, wherein the fault type includes a hardware fault type and / or a software fault type;

[0144] The first generating submodule is used to generate a troubleshooting solution corresponding to the fault type according to the fault type, and feed back the troubleshooting solution to the back-end sales department.

[0145] Optionally, in the case where there are multiple target bottom events that meet the preset triggering rules, the generating module 203 generates a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event, and includes:

[0146] A second generating submodule is used to generate a fault path corresponding to each target bottom event through the fault tree detection model according to each target bottom event;

[0147] A judgment submodule, used for judging whether each of the target bottom events is a related event;

[0148] A second determination submodule is used to determine the target fault path from the fault paths corresponding to the target bottom events that are mutually related events if the target bottom events are mutually related events;

[0149] The second determining submodule is configured to determine the fault path corresponding to each of the target bottom events as a target fault path if the target bottom events are mutually unrelated events.

[0150] Optionally, generating a target fault path corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes:

[0151] A first determining subunit is used to determine a target intermediate event corresponding to the target bottom event through the fault tree detection model according to the target bottom event;

[0152] A second determination subunit is used to determine a target top event corresponding to the target intermediate event through the fault tree detection model according to the target intermediate event;

[0153] The third determining subunit is used to determine a link composed of the target bottom event, the target intermediate event and the target top event as the target fault path according to the association relationship among the target bottom event, the target intermediate event and the target top event.

[0154] Optionally, in the case where there are multiple target bottom events satisfying a preset trigger rule, the target intermediate event corresponding to the target bottom event is determined by the fault tree detection model according to the target bottom event, and the first determining subunit includes:

[0155] The fourth determination subunit is used to determine the target intermediate event corresponding to at least one of the target base events through the fault tree detection model according to the mapping relationship between the target base event and the target intermediate event; wherein the mapping relationship represents that the target base event and the target intermediate event are in a one-to-one matching relationship or the target base event and the target intermediate event are in a many-to-one matching relationship.

[0156] Optionally, in the case where there are multiple target intermediate events, the target top event corresponding to the target intermediate event is determined by the fault tree detection model according to the target intermediate event, and the second determining subunit includes:

[0157] The fifth determination subunit is used to determine the target top event corresponding to at least one of the target intermediate events through the fault tree detection model according to the mapping relationship between the target intermediate events and the target top events; wherein the mapping relationship between the target intermediate events and the target top events represents that the target intermediate events and the target top events have a one-to-one matching relationship or the target intermediate events and the target top events have a many-to-one matching relationship.

[0158] Optionally, the fault tree detection model is created by the following steps:

[0159] An event analysis submodule, used to take the abnormal vehicle position fault as the top event of the fault tree detection model, and analyze multiple intermediate events provided by the vehicle end that cause the abnormal vehicle position fault and multiple bottom events corresponding to each of the intermediate events;

[0160] An event association submodule, used for associating the top event with each of the intermediate events according to the association relationship between the top event and each of the intermediate events;

[0161] The model creation submodule is used to associate each intermediate event with each bottom event corresponding to the intermediate event according to the association relationship between each intermediate event and each bottom event corresponding to the intermediate event, so as to obtain the fault tree detection model.

[0162] Optionally, after the fault tree detection model is created, the method further includes:

[0163] A third determination submodule is used to determine the target field corresponding to each bottom event in the fault tree detection model according to the historical status data of the vehicle;

[0164] A setting submodule is used to set a trigger rule for each target field corresponding to each of the bottom events, wherein the trigger rule is that the number of cumulative occurrences of the target field within a preset time period reaches a preset number;

[0165] The detecting whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data includes:

[0166] Detect all the bottom events in the vehicle status data, and determine the bottom event whose cumulative number of target field appearances reaches a preset number within a preset time length as the target bottom event.

[0167] Based on the same inventive concept, a third aspect of an embodiment of the present application provides a vehicle, which includes a vehicle position anomaly detection system as described in the second aspect of the present application, and / or executes a vehicle position anomaly detection method as described in the first aspect of the present application.

[0168] Each embodiment in this specification focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referenced to each other.

[0169] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the embodiments of the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0170] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0171] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0172] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0173] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present application.

[0174] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0175] The above is a detailed introduction to a vehicle position abnormality detection method, system and vehicle provided. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A method for detecting abnormal vehicle position, characterized in that: The method comprises: Obtain vehicle status data of the current vehicle uploaded by the vehicle end, wherein the vehicle status data includes: location data, DTC fault data and / or log data; Inputting the vehicle status data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data through the fault tree detection model; In the case where a target bottom event that meets the preset trigger rule is detected, a target fault path corresponding to the target bottom event is generated through the fault tree detection model according to the target bottom event, and the target bottom event and the target fault path are fed back to the vehicle end and / or the sales end as the detection result; The target fault path is a link consisting of a target bottom event, a target intermediate event and a target top event, wherein the target top event is an abnormal vehicle position fault, the target intermediate event includes at least GNSS positioning problem, a problem in which the vehicle position cannot be obtained due to disagreement of the position authorization, and a problem in which the position query is abnormal due to a TBOX problem, and the target bottom event associated with the GNSS positioning problem includes at least GNSS search satellite number less than 3 and GPS antenna short circuit to ground, the target bottom event associated with the problem in which the vehicle position cannot be obtained due to disagreement of the position authorization includes at least vehicle machine authorization disagreement and APP authorization disagreement, and the target bottom event associated with the problem in which the position query is abnormal due to a TBOX problem includes at least battery voltage too high.

2. A vehicle position abnormality detection method according to claim 1, characterized in that: After determining the target fault path, the method further includes: Determine the fault type causing the abnormal fault of the current vehicle position according to the target bottom event corresponding to the target fault path, wherein the fault type includes a hardware fault type and / or a software fault type; According to the fault type, a troubleshooting solution corresponding to the fault type is generated, and the troubleshooting solution is fed back to the back-end sales department.

3. A vehicle position abnormality detection method according to claim 1, characterized in that: In the case where there are multiple target bottom events that meet the preset triggering rules, generating a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event includes: According to each of the target bottom events, generating a fault path corresponding to each of the target bottom events through the fault tree detection model; Determine whether the target events are related events to each other; If the target bottom events are mutually related events, then determining the target fault path from the fault paths corresponding to the target bottom events that are mutually related events; If the target bottom events are mutually unrelated events, the fault path corresponding to each target bottom event is determined as the target fault path.

4. A vehicle position abnormality detection method according to claim 3, characterized in that: The step of generating a target fault path corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes: According to the target bottom event, determining a target intermediate event corresponding to the target bottom event through the fault tree detection model; According to the target intermediate event, determining the target top event corresponding to the target intermediate event through the fault tree detection model; According to the association relationship among the target bottom event, the target intermediate event and the target top event, a link formed by the target bottom event, the target intermediate event and the target top event is determined as the target fault path.

5. A vehicle position abnormality detection method according to claim 4, characterized in that: In the case where there are multiple target bottom events that meet the preset trigger rules, determining the target intermediate event corresponding to the target bottom event by using the fault tree detection model according to the target bottom event includes: According to the mapping relationship between the target base event and the target intermediate event, the target intermediate event corresponding to at least one of the target base events is determined through the fault tree detection model; wherein the mapping relationship represents that the target base event and the target intermediate event are in a one-to-one matching relationship or the target base event and the target intermediate event are in a many-to-one matching relationship.

6. A vehicle position abnormality detection method according to claim 4, characterized in that: In the case where there are multiple target intermediate events, determining the target top event corresponding to the target intermediate event by using the fault tree detection model according to the target intermediate event includes: According to the mapping relationship between the target intermediate event and the target top event, the target top event corresponding to at least one of the target intermediate events is determined through the fault tree detection model; wherein the mapping relationship between the target intermediate event and the target top event represents that the target intermediate event and the target top event have a one-to-one matching relationship or the target intermediate event and the target top event have a many-to-one matching relationship.

7. A vehicle position abnormality detection method according to any one of claims 1 to 6, characterized in that: The fault tree detection model is created by the following steps: Taking the abnormal vehicle position fault as the top event of the fault tree detection model, and analyzing multiple intermediate events provided by the vehicle end that cause the abnormal vehicle position fault and multiple bottom events corresponding to each of the intermediate events; According to the association relationship between the top event and each of the intermediate events, the top event is associated with each of the intermediate events respectively; According to the association relationship between each intermediate event and each bottom event corresponding to the intermediate event, each intermediate event is associated with each bottom event corresponding to the intermediate event to obtain the fault tree detection model.

8. A vehicle position abnormality detection method according to claim 7, characterized in that: After the fault tree detection model is created, the method further includes: Determine, according to the historical status data of the vehicle, the target field corresponding to each bottom event in the fault tree detection model; For each target field corresponding to each of the bottom events, a corresponding trigger rule is set, wherein the trigger rule is that the number of cumulative occurrences of the target field within a preset time period reaches a preset number; The detecting whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data includes: Detect all the bottom events in the vehicle status data, and determine the bottom event whose cumulative number of target field appearances reaches a preset number within a preset time length as the target bottom event.

9. A vehicle position abnormality detection system, characterized in that: The system comprises: An acquisition module, used to acquire vehicle status data of the current vehicle uploaded by the vehicle end, wherein the vehicle status data includes: location data, DTC fault data and / or log data; A detection module, used for inputting the vehicle status data into a pre-created fault tree detection model, so as to detect whether there is a target bottom event satisfying a preset trigger rule in the vehicle status data through the fault tree detection model; A generating module is used for, when a target bottom event satisfying the preset trigger rule is detected, generating a target fault path corresponding to the target bottom event through the fault tree detection model according to the target bottom event, and feeding back the target bottom event and the target fault path as detection results to the vehicle end and / or the after-sales end; the target fault path is a link composed of a target bottom event, a target intermediate event and a target top event, the target top event is an abnormal vehicle position fault, the target intermediate event at least includes GNSS positioning problem, the problem that the vehicle position cannot be obtained due to disagreement of the position authorization, and the problem that the position query is abnormal due to the TBOX problem, the target bottom event associated with the GNSS positioning problem at least includes GNSS search number less than 3, GPS antenna short circuit to ground, the target bottom event associated with the problem that the vehicle position cannot be obtained due to disagreement of the position authorization at least includes vehicle machine authorization disagreement, APP authorization disagreement, and the target bottom event associated with the problem that the position query is abnormal due to the TBOX problem at least includes battery voltage too high.

10. A vehicle, characterized in that: The vehicle comprises the vehicle position abnormality detection system as claimed in claim 9, and / or executes the vehicle position abnormality detection method as claimed in any one of claims 1-8.

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