Vehicle fault early warning method and electronic equipment

Abstract

The invention discloses a vehicle fault early warning method and electronic equipment, and is applied to the technical field of vehicles. The vehicle fault early warning method comprises the following steps: acquiring vehicle condition information of a target vehicle, wherein the vehicle condition information comprises CAN network data and fault data in the driving process of the target vehicle; according to a pre-constructed early warning model and the vehicle condition information, whether a preset type of faults occur in the target vehicle or not is pre-judged, the early warning model is a logical relation combination of M target faults and N early warning judgment conditions, the early warning judgment conditions are whether the target CAN data items meet threshold conditions set for the target CAN data items or not, and if yes, the target CAN data items meet the threshold conditions set for the target CAN data items; m and N are integers greater than or equal to 1; and if yes, early warning information used for early warning that the target vehicle will have the preset type of fault is pushed. According to the invention, the technical problem that the driving safety is affected due to the fact that the vehicle fault solving period is prolonged in the prior art is solved at least to a certain extent.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicles, and in particular relates to a vehicle fault warning method and electronic equipment. Background Art

[0002] Software-defined cars are now in a period of rapid development, and intelligence, electrification and networking have become a consensus trend in the industry. As more and more vehicle functions are transformed into software, the value of software in the whole vehicle is rapidly increasing, but the software problems caused by this are also increasing. Serious software problems even pose a hidden danger to driving safety.

[0003] The relevant technologies all solve the existing faults through maintenance or OTA (Over-the-Air Technology) upgrades after the vehicle breaks down, and cannot effectively monitor the preset types of faults while the vehicle is driving, resulting in a longer period of solving vehicle faults and affecting driving safety. Summary of the invention

[0004] A vehicle fault warning method and electronic device provided in accordance with an embodiment of the present invention solve, at least to a certain extent, the technical problem in related technologies of extending the cycle of vehicle faults and affecting driving safety.

[0005] In a first aspect of the present invention, a vehicle fault warning method is provided, comprising: obtaining vehicle condition information of a target vehicle, the vehicle condition information comprising CAN network data and fault data of the target vehicle during driving; predicting whether a preset type of fault will occur to the target vehicle based on a pre-constructed warning model and the vehicle condition information, wherein the warning model is a logical relationship combination of M types of target faults and N warning judgment conditions, the warning judgment condition is to judge whether a target CAN data item meets a threshold condition set for the target CAN data item, M and N are integers greater than or equal to 1; if so, pushing a warning message, the warning message being used to warn the target vehicle of a preset type of fault.

[0006] In combination with the first aspect, in some embodiments, predicting whether the target vehicle will have a preset type of fault based on a pre-constructed early warning model and the vehicle condition information includes: obtaining N target CAN data items related to the N early warning judgment conditions from the CAN network data; processing the N target CAN data items and the fault data through the early warning model to obtain a prediction result, and the prediction result characterizes whether the target vehicle will have a preset type of fault.

[0007] In combination with the first aspect, in some embodiments, the N target CAN data items and the fault data are processed by the early warning model to obtain a prediction result, including: determining each target CAN data item that meets the N early warning judgment conditions from the N target CAN data items; if each target CAN data item that meets the N early warning judgment conditions and the fault data meet any one of the logical relationship combinations defined in the early warning model, it is determined that the target vehicle will have a preset type of fault.

[0008] In combination with the first aspect, in some embodiments, the early warning model is also pre-built through the following steps: obtaining custom data reported by the operation and maintenance end, the custom data including M target faults and N target CAN data items related to driving safety selected by the design user, N threshold conditions customized for the N target CAN data items, and design information of the logical relationship combination of the M target faults and the N early warning judgment conditions; generating the early warning model according to the custom data.

[0009] In combination with the first aspect, in some embodiments, it also includes: obtaining a vehicle fault data set, the vehicle fault data set including multiple fault data reported by multiple vehicles, each fault data including data of the vehicle within a preset time period before and after the fault occurs; obtaining a new early warning model based on the vehicle fault data set.

[0010] In combination with the first aspect, in some embodiments, obtaining a new early warning model based on the vehicle fault data set includes: obtaining model optimization information based on the vehicle fault data set, the model optimization information including new target faults and / or new target CAN data items; modifying the logical relationship combination in the early warning model and / or adding new logical relationship combinations according to the model optimization information to obtain the new early warning model.

[0011] In combination with the first aspect, in some embodiments, obtaining a new warning model based on the vehicle fault data set includes: matching a new warning model from a plurality of pre-built candidate models based on the vehicle fault data set; and replacing the warning model with the new warning model.

[0012] In combination with the first aspect, in some embodiments, the pushing of warning information includes: pushing first warning information to the operation and maintenance end, and / or if it is predicted that the preset type of failure that will occur in the target vehicle includes a first type of failure, pushing second warning information to the target vehicle, the second warning information is used to warn the target vehicle that a failure that affects driving safety will occur, and the first type of failure is a failure that affects driving safety.

[0013] In combination with the first aspect, in some embodiments, it also includes: determining the fault level of each existing DTC fault in the fault data based on pre-defined fault level information; for each existing DTC fault in the fault data, determining a handling measure based on the fault level of the existing DTC fault, and handling the existing DTC fault based on the handling measure.

[0014] In the second aspect of the present invention, an electronic device is also provided, comprising: a processor, and a memory communicatively connected to the processor; the memory stores computer-executable instructions; the processor executes the computer-executable instructions stored in the memory to implement the vehicle fault warning method described in any embodiment of the first aspect.

[0015] In the third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored. The computer program includes executable instructions. When the executable instructions are executed by a processor, the vehicle fault warning method described in any embodiment of the first aspect is implemented.

[0016] According to one or more technical solutions provided by the embodiments of the present invention, at least the following technical effects or advantages are achieved: By obtaining the vehicle condition information of the target vehicle, which includes the CAN network data and fault data of the target vehicle during driving; according to the pre-built warning model and vehicle condition information, it is predicted whether the target vehicle will have a preset type of fault, where the warning model is a logical combination of M types of target faults and N warning judgment conditions, and the warning judgment condition is to judge whether the target CAN data item meets the pre-set threshold conditions. If so, the warning information used to warn the target vehicle of the occurrence of a preset type of fault is pushed. It is possible to predict whether the vehicle will have a preset type of fault and issue a warning in advance before the preset fault occurs, and to achieve more effective monitoring and warning of the vehicle's expected possible faults, which is conducive to improving driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0018] Figure 1 The application scenarios of the vehicle fault warning method according to some embodiments of the present invention are shown; Figure 2 A flow chart showing a vehicle fault warning method according to some embodiments of the present invention; Figure 3a A flow chart showing a method for early warning of vehicle failure according to some embodiments of the present invention for obtaining a new early warning model; Figure 3b A flow chart showing a method for early warning of vehicle failure according to other embodiments of the present invention for obtaining a new early warning model; Figure 4 A flow chart showing a vehicle fault warning method according to other embodiments of the present invention; Figure 5 A schematic structural diagram of an electronic device according to some embodiments of the present invention is shown. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] First of all, the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0021] Figure 1 The following illustrates the application scenarios of the vehicle fault warning method according to some embodiments of the present invention. Figure 1 As shown, the application scenario includes a server and multiple vehicles that communicate with the server to exchange information. Each vehicle is used to report its own vehicle condition information to the server. The server is used to obtain the vehicle condition information reported by the target vehicle. The vehicle condition information reported by the target vehicle includes CAN (Controller Area Network) network data and fault data of the target vehicle during driving. The target vehicle is any vehicle that communicates with the server to exchange information. The server is also used to predict whether the target vehicle will have a preset type of fault based on a pre-built warning model and the vehicle condition information reported by the target vehicle. The warning model is a logical relationship combination of M types of target faults and N warning judgment conditions. The warning judgment condition is to judge whether the target CAN data item meets the threshold condition set for the target CAN data item. M and N are integers greater than or equal to 1; if so, push the warning information, and the warning information is used to warn the target vehicle of a preset type of fault.

[0022] like Figure 1 As shown, in some embodiments, the application scenario may also include an operation and maintenance end that communicates with the server end to exchange information.

[0023] The vehicle fault warning method provided by the embodiment of the present invention can be Figure 1 The server shown executes, and the server interacts with the target vehicle and / or the operation and maintenance end to provide an early warning before a preset type of failure occurs in the target vehicle, so as to improve the driving safety of the target vehicle.

[0024] Figure 2 FIG. 1 is a flow chart showing a vehicle fault warning method according to some embodiments of the present invention. Figure 2 As shown, the vehicle fault warning method provided by the embodiment of the present invention includes the following steps S201 to S203: S201: Acquire vehicle condition information of a target vehicle, where the vehicle condition information includes CAN network data and fault data of the target vehicle during driving.

[0025] It is understandable that the vehicle condition information reported by the target vehicle is received, wherein the fault data includes existing faults detected by the target vehicle.

[0026] In some embodiments, the target vehicle continuously monitors the vehicle operating status during operation to obtain and report the vehicle condition information. The server obtains the fault data and CAN network data of the target vehicle within the current time window from the vehicle condition information reported by the target vehicle according to a preset time window to monitor the current vehicle condition of the target vehicle, so that the server can understand the actual vehicle condition of the target vehicle in a timely manner.

[0027] In some embodiments, the fault data includes a DTC (Diagnostic Trouble Code) fault code of an existing fault detected by the target vehicle's onboard ECU (Electronic Control Unit). The target vehicle's onboard ECU identifies the target vehicle's existing fault and records it as a corresponding DTC fault code. The DTC fault code is generated by the onboard ECU and is used to indicate a fault or abnormality in the engine, transmission system or other system. The target vehicle reports the recorded DTC fault code to the server so that the server can obtain the DTC fault code of the target vehicle's existing fault.

[0028] It is understandable that when a target vehicle fails, the on-board ECU can monitor the fault and record it as a corresponding DTC fault code. The target vehicle is detected through the on-board ECU, and the location and cause of the target vehicle failure are detected without disassembly (or only disassembly of a single part), including fault detection of the target vehicle's engine, chassis, body and accessories, as well as detection of vehicle exhaust pollutants and noise. The recorded fault code includes a prefix letter and a numeric string, where the prefix letter represents the fault location, such as: power system (P), body system (B), chassis system (C), and network or data communication transmission system (U), etc., and the numeric string represents the specific fault type.

[0029] In some embodiments, the operation and maintenance end can customize the safety monitoring frequency of the target vehicle, perform fault detection on the target vehicle according to the customized safety monitoring frequency and record it as a corresponding DTC fault code, so that the service end can obtain the DTC fault code that appears in the current time window of the target vehicle during driving.

[0030] In some embodiments, the CAN network data includes the operating status of each subsystem of the target vehicle collected from the CAN bus, sensor data, etc. Exemplarily, the CAN network data may include: motor speed and torque, battery temperature, key status, gear position, vehicle speed, brake, clutch, throttle, steering wheel steering angle, horn, seat belt, door, left and right turn signal, double flash, high beam, low beam, width indicator, front fog light, rear fog light, reverse light, total mileage and remaining fuel, remaining power, etc.

[0031] S202: Predict whether a preset type of fault will occur in the target vehicle based on a pre-built warning model and vehicle condition information, wherein the warning model is a logical combination of M types of target faults and N warning judgment conditions, and the warning judgment condition is to determine whether the target CAN data item meets the threshold condition set for the target CAN data item, and M and N are integers greater than or equal to 1.

[0032] In some embodiments, the preset faults include first-class faults and / or second-class faults. The first-class faults refer to faults that affect driving safety. The second-class faults refer to faults that cannot be self-repaired by the target vehicle, for example: the second-class faults are faults that cannot be self-repaired by OTA upgrades.

[0033] It can be understood that the N early warning judgment conditions refer to whether the N target CAN data items meet the N threshold conditions one by one. For each target CAN data item, it is necessary to set a suitable threshold condition according to the data characteristics of the target CAN data item. For example, since the ideal temperature range of the battery of an electric vehicle should be maintained between 0-40°C, the corresponding threshold condition is set for the target CAN data of battery temperature as "whether the battery temperature is greater than 40°". If the battery temperature is greater than 40°, it indicates that the early warning judgment condition is met, otherwise, the early warning judgment condition is not met.

[0034] It is understandable that the vehicle-mounted ECU can detect many types of faults, and the CAN network data also includes multiple CAN data items. Not every fault detected by the vehicle-mounted ECU is related to driving safety, and not every CAN data item is related to driving safety. Some faults detected by the vehicle-mounted ECU can be self-repaired by the target vehicle. Therefore, the M types of target faults are only the faults screened out from all the faults detected by the vehicle-mounted ECU. The screened faults are related to the driving safety of the target vehicle and / or cannot be self-repaired by the target vehicle. The N target CAN data items are only the various CAN data items related to the driving safety of the target vehicle screened out from the CAN network data.

[0035] It can be understood that the logical relationship combinations defined in the early warning model based on M target faults and N early warning judgment conditions include one or more. If the vehicle condition information obtained in step S201 can meet any logical relationship combination defined in the early warning model, it is determined that the target vehicle will have a preset type of fault and a corresponding prediction result is given.

[0036] In some embodiments, each logical relationship combination defined by M target faults and N early warning judgment conditions is composed of one or more logical relationships of "and" logical relationship, "or" logical relationship, and "not" logical relationship. Exemplarily, the following logical relationship combination may be included: the occurrence of DTC fault code Pxxxx "and" (battery temperature is greater than 40° "or" vehicle speed is greater than 100km / h).

[0037] In some embodiments, predicting whether a preset type of fault will occur in a target vehicle based on a pre-built warning model and vehicle condition information can include: obtaining N target CAN data items related to N warning judgment conditions from the CAN network data; processing the N target CAN data items and fault data through the warning model to obtain a prediction result, wherein the prediction result characterizes whether a preset type of fault will occur in the target vehicle.

[0038] In some embodiments, processing N target CAN data items and fault data through a warning model to obtain a prediction result may include: determining each target CAN data item that meets N warning judgment conditions from the N target CAN data items; if each target CAN data item and fault data that meets the N warning judgment conditions conforms to any combination of logical relationships defined in the warning model, it is determined that a preset type of fault will occur in the target vehicle.

[0039] It can be understood that the N target CAN data items related to the N warning judgment conditions refer to the CAN data items required for the judgment process of the N warning judgment conditions, wherein one warning judgment condition corresponds to one target CAN data item. For example, if the warning model only defines three warning judgment conditions: "whether the battery temperature is greater than 40°", "whether the vehicle speed is greater than 100km / h", and "whether the remaining battery power is less than 20%", and the warning model defines the logical relationship combination of DTC fault code Pxxxx "and" (battery temperature is greater than 40°" or "vehicle speed is greater than 100km / h), then it is necessary to obtain the three target CAN data items of the battery temperature, battery remaining power and vehicle speed of the target vehicle from the CAN network data. If the battery temperature of the target vehicle obtained from the CAN network data is greater than 40°, and the target vehicle reports the DTC fault code Pxxxx, it is determined that the target vehicle will have a preset type of fault. If the battery temperature of the target vehicle obtained from the CAN network data is not greater than 40°, but the target vehicle reports the DTC fault code Pxxxx, it will not be determined that the target vehicle will have a preset type of fault.

[0040] In some embodiments, the early warning model may also pre-define mapping relationships between various logical relationship combinations and fault types. After predicting that each target CAN data item and fault data that meets N early warning judgment conditions conforms to any logical relationship combination defined in the early warning model, the fault type that matches the logical relationship combination is determined based on the mapping relationship between the logical relationship combination and the fault type, thereby achieving more accurate early warning.

[0041] According to any of the above embodiments, since a single rule based on threshold conditions and a multidimensional rule based on a combination of logical relationships are defined in the early warning model at the same time, an effective analysis of the vehicle condition information is achieved by combining the two rules, and the vehicle condition information of the target vehicle is analyzed from multiple dimensions, and an effective early warning is issued when the single rule and the multidimensional rules are met.

[0042] In some embodiments, the vehicle fault warning method provided by the embodiments of the present invention may further include the step of pre-building a warning model: obtaining custom data reported by the operation and maintenance end, and generating a warning model based on the custom data.

[0043] In some embodiments, the obtained custom data includes M target faults and N target CAN data items related to driving safety selected by the design user (developer), N threshold conditions customized for the N target CAN data items, and design information of the logical relationship between the M target faults and the N warning judgment conditions. By customizing various parameters and rules in the warning model, the abnormal situations that may occur in each vehicle can be more comprehensively covered.

[0044] In order to improve the prediction accuracy of the early warning model and more accurately predict and warn of preset types of faults that will occur in the vehicle, in some embodiments, the vehicle fault early warning method provided by the embodiments of the present invention may also include: obtaining a vehicle fault data set, the vehicle fault data set including multiple fault data reported by multiple vehicles interacting with the server, each fault data including data of the vehicle within a preset time period before and after the fault occurs; obtaining a new early warning model based on the vehicle fault data set.

[0045] In some embodiments, obtaining a new early warning model based on a vehicle fault data set may include: obtaining model optimization information based on the vehicle fault data set, the model optimization information including a new target fault and / or a new target CAN data item; modifying the logical relationship combination in the early warning model and / or adding a new logical relationship combination based on the model optimization information to obtain a new early warning model. It should be noted that the new target fault is a DTC fault code that can be detected and recorded by the vehicle ECU.

[0046] Figure 3a FIG. 1 is a flow chart showing a method for early warning of vehicle failure according to some embodiments of the present invention, and obtaining a new early warning model. Figure 3a As shown, in other embodiments, information matching is performed based on the vehicle fault data set to match model optimization information from a plurality of pre-set logical relationship combinations, and the model optimization information is pushed to the operation and maintenance end. The model optimization information is used by the operation and maintenance personnel to optimize the early warning model at the operation and maintenance end to obtain a new early warning model.

[0047] Figure 3b FIG. 4 shows a flow chart of a vehicle fault warning method according to other embodiments of the present invention, and obtaining a new warning model. Figure 3b As shown, in some other embodiments, obtaining a new warning model based on a vehicle fault data set may include: performing model matching based on the vehicle fault data set to match a new warning model from a plurality of pre-set candidate models; and replacing the warning model with the new warning model.

[0048] like Figure 3a and Figure 3bAs shown, in some embodiments, each piece of fault data in the vehicle fault data set is generated by the vehicle actively forming a fault snapshot of the data within a period of time before and after the fault occurs and actively reporting it to the server when the fault occurs. The fault snapshot is the snapshot data recorded when the vehicle fails, also known as a frozen frame or frozen data frame.

[0049] S203: If yes, push warning information, where the warning information is used to warn the target vehicle that a preset fault will occur.

[0050] In some embodiments, the pushing of warning information in step S203 may include: pushing first warning information to the operation and maintenance end and / or pushing second warning information to the target vehicle. The first warning information is used to warn the target vehicle of a preset type of fault, and the second warning information is used to warn the target vehicle of a first type of fault that affects driving safety.

[0051] In some embodiments, pushing a second warning message to a target vehicle includes: determining whether a preset type of fault that may occur in the target vehicle includes a first type of fault; if it is predicted that the preset type of fault that may occur in the target vehicle includes a first type of fault, pushing a second warning message to the target vehicle; the second warning message is used to warn the target vehicle that a first type of fault that may affect driving safety may occur.

[0052] According to one or more of the above embodiments, by pushing the first warning information to the operation and maintenance end, it is possible to track minor problems of the target vehicle that cannot be self-repaired, effectively prevent the problem from expanding, and more reasonably issue warnings before the problem becomes larger and more serious. It is convenient for operation and maintenance personnel to repair abnormal situations that are expected to occur in a timely and accurate manner. By pushing the second warning information to the target vehicle, it is possible to remind the owner and / or driver that the target vehicle is about to have a fault that affects driving safety, which is conducive to ensuring driving safety.

[0053] In some embodiments, the vehicle fault warning method provided by the embodiments of the present invention may also include: determining the fault level of each existing DTC fault in the fault data according to predefined fault level information; for each existing DTC fault in the fault data, determining a disposal measure according to the fault level of the existing DTC fault, and disposing of the existing DTC fault based on the disposal measure. It is understandable that the predefined fault level information is a plurality of fault levels obtained by pre-classifying various DTC fault codes, and each fault level is configured with corresponding disposal measures, and different disposal measures are set for different fault levels. Taking corresponding disposal measures for different fault levels can repair minor problems of the target vehicle without feeling, effectively track risk problems, and warn of major problems in real time.

[0054] Figure 4FIG. 1 is a flow chart showing a vehicle fault warning method according to other embodiments of the present invention. Figure 4 A control logic of the vehicle fault early warning method in an embodiment of the present invention is given: Step S1: Obtain the DTC fault code, CAN data item and threshold condition customized by the developer; Step S2: Obtain the logical relationship combination customized by the developer and generate an early warning model; Step S3: Obtain vehicle condition information reported by the target vehicle; Step S4: storing vehicle condition information on the server; Step S5: Analyze the vehicle condition information in the current time window based on the early warning model to see if it meets any logical relationship combination defined in the early warning model. If so, it indicates that the target vehicle has a first type of fault that affects driving safety and / or a second type of fault that the target vehicle cannot self-repair, then proceed to steps S6-S7. If not, end the process. Step S6: Pushing the first warning information to the operation and maintenance end, so that the operation and maintenance personnel can know the abnormal condition of the target vehicle and repair the target vehicle in time; Step S7: Determine whether there is a first type of fault that affects the driving safety of the target vehicle, and if so, proceeds to step S8; Step S8: Pushing the second warning information to the target vehicle so that the driver and / or the vehicle owner can be aware of the abnormal condition of the target vehicle and improve driving safety.

[0055] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, Figure 5 FIG. 1 shows a schematic diagram of the structure of an electronic device according to some embodiments of the present invention. Figure 5 As shown, the electronic device includes one or more memories 504, one or more processors 502, and at least one computer program (program code) stored in the memory 504 and executable on the processor 502. When the processor 502 executes the computer program, the vehicle fault warning method of any of the above-mentioned embodiments is implemented.

[0056] Among them, Figure 5In the embodiment of the present invention, a bus architecture (represented by bus 500) is shown, and bus 500 may include any number of interconnected buses and bridges, and bus 500 links various circuits including one or more processors represented by processor 502 and memory represented by memory 504. Bus 500 may also link various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. Bus interface 505 provides an interface between bus 500 and receiver 501 and transmitter 503. Receiver 501 and transmitter 503 may be the same element, i.e., a transceiver, providing a unit for communicating with various other devices on a transmission medium. Processor 502 is responsible for managing bus 500 and general processing, while memory 504 may be used to store data used by processor 502 when performing operations.

[0057] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. The computer program includes executable instructions. When the executable instructions are executed by a processor, the vehicle fault warning method of any of the above-mentioned embodiments is implemented.

[0058] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take 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 code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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 device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing 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.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing 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.

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other 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 present invention.

[0063] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A vehicle fault warning method, characterized in that: include: Acquire vehicle condition information of the target vehicle, wherein the vehicle condition information includes CAN network data and fault data of the target vehicle during driving; Predicting whether a preset type of fault will occur to the target vehicle according to a pre-built warning model and the vehicle condition information, wherein the warning model is a logical relationship combination of M types of target faults and N warning judgment conditions, and the warning judgment condition is to judge whether the target CAN data item meets the threshold condition set for the target CAN data item, and M and N are integers greater than or equal to 1; If so, a warning message is pushed, where the warning message is used to warn the target vehicle that a preset type of failure will occur.

2. The method according to claim 1, characterized in that The predicting whether the target vehicle will have a preset type of fault according to the pre-built warning model and the vehicle condition information includes: Acquire N target CAN data items related to the N early warning judgment conditions from the CAN network data; The N target CAN data items and the fault data are processed by the early warning model to obtain a prediction result, wherein the prediction result indicates whether a preset type of fault will occur in the target vehicle.

3. The method according to claim 2, characterized in that The processing of the N target CAN data items and the fault data by the early warning model to obtain a prediction result includes: Determine each target CAN data item that satisfies the N early warning judgment conditions from the N target CAN data items; If each target CAN data item that satisfies the N warning judgment conditions and the fault data conform to any combination of logical relationships defined in the warning model, it is determined that a preset type of fault will occur in the target vehicle.

4. The method according to claim 1, characterized in that It also includes pre-building the early warning model through the following steps: Obtaining custom data reported by the operation and maintenance end, the custom data including M target faults and N target CAN data items related to driving safety selected by a design user, N threshold conditions customized for the N target CAN data items, and design information of a logical relationship combination of the M target faults and the N warning judgment conditions; The early warning model is generated according to the custom data.

5. The method according to claim 4, characterized in that Also includes: Acquire a vehicle fault data set, wherein the vehicle fault data set includes multiple fault data reported by multiple vehicles, and each fault data includes data of the vehicle within a preset time period before and after the fault occurs; A new early warning model is obtained according to the vehicle fault data set.

6. The method according to claim 4, characterized in that The acquiring a new early warning model according to the vehicle fault data set includes: Acquire model optimization information according to the vehicle fault data set, wherein the model optimization information includes a new target fault and / or a new target CAN data item; The logical relationship combinations in the early warning model are modified and / or new logical relationship combinations are added according to the model optimization information to obtain the new early warning model.

7. The method according to claim 4, characterized in that The acquiring a new early warning model according to the vehicle fault data set includes: According to the vehicle fault data set, matching the new early warning model from a plurality of pre-built candidate models; The early warning model is replaced with the new early warning model.

8. The method according to claim 1, characterized in that The push warning information includes: Push the first warning information to the operation and maintenance end, and / or If it is predicted that the preset type of fault that may occur in the target vehicle includes a first type of fault, a second warning information is pushed to the target vehicle, and the second warning information is used to warn the target vehicle that a fault that affects driving safety will occur, and the first type of fault is a fault that affects driving safety.

9. The method according to claim 1, characterized in that Also includes: Determining the fault level of each existing DTC fault in the fault data according to predefined fault level information; For each existing DTC fault in the fault data, a treatment measure is determined according to the fault level of the existing DTC fault, and the existing DTC fault is treated based on the treatment measure.

10. An electronic device, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the vehicle fault warning method as described in any one of claims 1-9.

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