CN114296426A - Remote diagnosis method and device for vehicle, server and storage medium - Google Patents
Remote diagnosis method and device for vehicle, server and storage medium Download PDFInfo
- Publication number
- CN114296426A CN114296426A CN202111492980.8A CN202111492980A CN114296426A CN 114296426 A CN114296426 A CN 114296426A CN 202111492980 A CN202111492980 A CN 202111492980A CN 114296426 A CN114296426 A CN 114296426A
- Authority
- CN
- China
- Prior art keywords
- fault
- vehicle
- diagnosed
- prediction model
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 46
- 238000004171 remote diagnosis Methods 0.000 title claims abstract description 33
- 238000003745 diagnosis Methods 0.000 claims abstract description 73
- 238000012549 training Methods 0.000 claims abstract description 20
- 238000013144 data compression Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 238000005516 engineering process Methods 0.000 abstract description 6
- 238000004891 communication Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 5
- 238000012545 processing Methods 0.000 description 5
- 238000009825 accumulation Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 4
- 238000013523 data management Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000002405 diagnostic procedure Methods 0.000 description 1
- 238000005538 encapsulation Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
Images
Landscapes
- Vehicle Cleaning, Maintenance, Repair, Refitting, And Outriggers (AREA)
Abstract
The application relates to the technical field of vehicles, in particular to a remote diagnosis method, a remote diagnosis device, a remote diagnosis server and a storage medium of a vehicle, wherein the method comprises the following steps: acquiring current fault data and identity identification information uploaded by a vehicle to be diagnosed; matching a fault prediction model of the vehicle to be diagnosed based on the identity identification information, wherein the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed; and substituting the current fault data into the fault prediction model, and predicting the diagnosis result of the vehicle to be diagnosed by the fault prediction model. Therefore, the problem that diagnosis is usually carried out after a vehicle breaks down in the related technology is solved, certain hysteresis is achieved, the diagnosis intelligence and the vehicle safety are greatly reduced, and the use experience of a user is reduced.
Description
Technical Field
The present application relates to the field of vehicle technologies, and in particular, to a method, an apparatus, a server, and a storage medium for remote diagnosis of a vehicle.
Background
At present, the main mode of vehicle diagnosis is mainly an off-line diagnosis mode, that is, after a vehicle has a fault, the vehicle is communicated with an external host computer, such as a diagnosis device, through an On-Board Diagnostics (On-Board Diagnostics) port, so as to analyze and determine the fault of the vehicle according to the obtained fault information of the vehicle.
However, in the related art, the vehicle is usually diagnosed after the vehicle has a fault, which has a certain hysteresis, greatly reduces the intelligence of diagnosis and the safety of the vehicle, and reduces the user experience.
Disclosure of Invention
The application provides a remote diagnosis method, a remote diagnosis device, a server and a storage medium of a vehicle, which are used for solving the problems that diagnosis is usually carried out after the vehicle breaks down in the related art, certain hysteresis exists, the intelligence of diagnosis and the safety of the vehicle are reduced, the use experience of a user is poor, and the like.
An embodiment of a first aspect of the present application provides a remote diagnosis method for a vehicle, including the following steps:
acquiring current fault data and identity identification information uploaded by the vehicle to be diagnosed;
matching a fault prediction model of the vehicle to be diagnosed based on the identity identification information, wherein the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed;
and substituting the current fault data into the fault prediction model, and predicting the diagnosis result of the vehicle to be diagnosed by the fault prediction model.
Further, before the predicting the diagnosis result of the vehicle to be diagnosed by the fault prediction model, the method further includes:
and updating the fault prediction model according to the current fault data, and predicting the diagnosis result of the vehicle to be diagnosed by the updated fault prediction model.
Further, the fault prediction model is trained from historical fault data of the vehicle to be diagnosed, and includes:
acquiring historical fault data uploaded and stored by the vehicle to be diagnosed;
analyzing a fault zone bit and counting fault parameters of the historical fault data to obtain at least one historical fault parameter of the vehicle to be diagnosed;
and training to obtain a fault prediction model of the vehicle to be diagnosed based on the at least one historical fault parameter.
Further, after outputting the result of predicting the failure of the vehicle to be diagnosed, the method further includes:
if the fault prediction result is a first fault degree, generating first reminding information, and sending the first reminding information to the vehicle to be diagnosed;
and if the fault prediction result is a second fault degree, generating second reminding information, sending the second reminding information to the vehicle to be diagnosed, and sending the second reminding information to a preset terminal, wherein the severity of the second fault degree is greater than the severity of the first fault degree.
Further, after outputting the result of predicting the failure of the vehicle to be diagnosed, the method further includes:
and performing data compression on the current fault data and the diagnosis result, and storing the compressed data to preset storage equipment.
An embodiment of a second aspect of the present application provides a remote diagnosis device for a vehicle, including:
the acquisition module is used for acquiring the current fault data and the identity identification information uploaded by the vehicle to be diagnosed;
the matching module is used for matching a fault prediction model of the vehicle to be diagnosed based on the identity identification information, wherein the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed;
and the prediction module is used for substituting the current fault data into the fault prediction model, and predicting the diagnosis result of the vehicle to be diagnosed by the fault prediction model.
Further, still include:
the updating module is used for updating the fault prediction model according to the current fault data before the fault prediction model predicts the diagnosis result of the vehicle to be diagnosed, and predicting the diagnosis result of the vehicle to be diagnosed by the updated fault prediction model;
the reminding module is used for generating first reminding information and sending the first reminding information to the vehicle to be diagnosed if the fault prediction result is a first fault degree after the fault prediction result of the vehicle to be diagnosed is output; if the fault prediction result is a second fault degree, generating second reminding information, sending the second reminding information to the vehicle to be diagnosed, and sending the second reminding information to a preset terminal, wherein the severity of the second fault degree is greater than the severity of the first fault degree;
and the storage module is used for performing data compression on the current fault data and the diagnosis result after outputting the fault prediction result of the vehicle to be diagnosed, and storing the compressed data into preset storage equipment.
Further, still include:
the training module is used for acquiring historical fault data uploaded by the vehicle to be diagnosed; analyzing a fault zone bit and counting fault parameters of the historical fault data to obtain at least one historical fault parameter of the vehicle to be diagnosed; and training to obtain a fault prediction model of the vehicle to be diagnosed based on the at least one historical fault parameter.
An embodiment of a third aspect of the present application provides a server, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the remote diagnosis method of the vehicle described above.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon a computer program for execution by a processor for implementing a remote diagnosis method for a vehicle as claimed above.
Therefore, the application has at least the following beneficial effects:
the fault prediction model is established according to the uploaded data, the diagnosis result of the vehicle is predicted on line by the fault prediction model, the fault can be predicted before occurring, passive waiting for the fault is not needed, the fault occurrence probability is greatly reduced, the diagnosis intelligence and the vehicle safety are improved, and the use experience of a user is improved. Therefore, the problems that diagnosis is usually carried out after a vehicle breaks down, certain hysteresis exists, the diagnosis intelligence and the vehicle safety are greatly reduced, the use experience of a user is reduced and the like in the related technology are solved.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flowchart of a remote diagnosis method for a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic flow chart diagram illustrating a method for remote vehicle diagnostics according to an embodiment of the present application;
fig. 3 is an example diagram of a remote diagnosis apparatus of a vehicle according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a server according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.
Often can only choose to go to 4S shop nearby when the vehicle breaks down, maintain through traditional diagnostic mode, and traditional diagnostic process includes: the method comprises the steps that communication is carried out with an external upper computer (such as a diagnosis device) through a vehicle-mounted OBD port, the external upper computer obtains specific ECU (Electronic Control Unit) diagnosis information by sending specific diagnosis messages, the diagnosis information is transmitted in a plaintext mode on a vehicle-mounted CAN (Controller Area Network) bus, and after the upper computer receives the diagnosis messages, the diagnosis messages are analyzed into specific fault information according to a diagnosis protocol released by a host factory and displayed on the upper computer.
However, when a conventional diagnosis mode is adopted, only when a diagnosis request is made on an upper computer, the ECU feeds back diagnosis information, cannot monitor fault information in real time, can only report specific fault code information, cannot acquire real-time parameters of the whole vehicle when a fault occurs, such as fault accumulation/accumulation parameters, fault trigger thresholds and other specific fault information, and cannot predict the fault. Therefore, the remote diagnosis method for the vehicle is provided by the embodiment of the application, and the problems that diagnosis is usually performed after the vehicle breaks down in the related technology, certain hysteresis exists, the diagnosis intelligence and the vehicle safety are greatly reduced, and the use experience of a user is reduced are solved.
A remote diagnosis method, apparatus, server, and storage medium for a vehicle according to an embodiment of the present application will be described below with reference to the accompanying drawings. In view of the above-mentioned related technologies, diagnosis is usually performed after a vehicle has a fault, which has a certain hysteresis, and thus the intelligence of diagnosis and the safety of the vehicle are greatly reduced, and the user experience is reduced. Therefore, the problems that diagnosis is usually carried out after a vehicle breaks down, certain hysteresis exists, the diagnosis intelligence and the vehicle safety are greatly reduced, the use experience of a user is reduced and the like in the related technology are solved.
Specifically, fig. 1 is a schematic flowchart of a remote diagnosis method for a vehicle according to an embodiment of the present disclosure.
As shown in fig. 1, the remote diagnosis method of a vehicle includes the steps of:
in step S101, current fault data and identification information uploaded by a vehicle to be diagnosed are acquired.
The current fault data may include fault specific parameters, such as fault accumulation/subtraction parameters, fault trigger thresholds, current values, and other parameter information.
The identification information may include information such as an identification code of the vehicle, and is used to uniquely identify the vehicle.
It can be understood that the embodiment of the application can communicate with the vehicle to be diagnosed through networks such as WIFI/5G and the like so as to acquire current fault data and identity information. According to the embodiment of the application, the data transmission time is shortened through high-speed communication modes such as 5G/WIFI, the uploading success rate can be effectively improved, and the data integrity is guaranteed.
Specifically, a vehicle to be diagnosed CAN collect fault parameter information of different vehicle-mounted CAN controllers, package the fault parameter information according to a remote communication protocol defined by a host factory, and upload the packaged data according to a preset reporting frequency, so that the current fault data and the identity information CAN be acquired by the embodiment of the application. The preset reporting frequency may be set according to an actual situation, which is not specifically limited.
In step S102, a fault prediction model of the vehicle to be diagnosed is matched based on the identification information, wherein the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed.
It can be understood that the fault prediction model corresponding to the vehicle to be diagnosed can be determined according to the identification information, wherein the fault prediction model is established based on specific fault parameters.
In this embodiment, the fault prediction model is trained from historical fault data of a vehicle to be diagnosed, and includes: acquiring historical fault data uploaded by a vehicle to be diagnosed; analyzing a fault zone bit and counting fault parameters of historical fault data to obtain at least one historical fault parameter of the vehicle to be diagnosed; and training to obtain a fault prediction model of the vehicle to be diagnosed based on at least one historical fault parameter.
It can be understood that the data uploaded by the vehicle to be diagnosed can be processed by the embodiment of the application, and the analysis of the fault zone bit and the statistics of the fault parameters are included to establish the fault prediction model, so that a uniform fault data management model can be established, and the efficient management of the fault data is realized. The embodiment of the present application may use various training methods, such as neural network training, and the like, which are not limited in this respect.
In step S103, the current failure data is substituted into a failure prediction model, and a diagnosis result of the vehicle to be diagnosed is predicted by the failure prediction model.
It can be understood that the embodiment of the application can realize real-time monitoring of the faults of the whole vehicle, can predict in advance instead of passively waiting for the faults to occur, and can give early warning to a host factory or a vehicle owner in advance, thereby effectively reducing the actual fault occurrence probability of the vehicle and improving the safety of the vehicle.
In this embodiment, before predicting the diagnosis result of the vehicle to be diagnosed by the failure prediction model, the method further includes: and updating the fault prediction model according to the current fault data, and predicting the diagnosis result of the vehicle to be diagnosed by the updated fault prediction model.
It can be understood that when the fault triggering fault parameters are accumulated, the fault prediction model can update the fault parameters in real time, and the updated fault prediction model can predict the diagnosis result of the vehicle to be diagnosed, so that the accuracy of predicting the diagnosis result can be improved, and the safety of the vehicle can be improved.
In the present embodiment, after outputting the result of predicting the failure of the vehicle to be diagnosed, the method further includes: if the fault prediction result is the first fault degree, generating first reminding information and sending the first reminding information to the vehicle to be diagnosed; and if the fault prediction result is a second fault degree, generating second reminding information, sending the second reminding information to the vehicle to be diagnosed, and sending the second reminding information to the preset terminal, wherein the fault severity hazard degree of the second fault degree is greater than the fault severity hazard degree of the first fault degree.
The preset terminal may be a personal computer, a mobile terminal, and the like, which is not limited in the present application, wherein the mobile terminal may be a hardware device having various operating systems, such as a mobile phone, a tablet computer, a personal digital assistant, and an electronic book.
The first reminding information and the second reminding information can be specifically set according to the actual reminding requirements, and are not specifically limited to this.
The first failure degree and the second failure degree may be specifically identified according to an actual situation, for example, the first failure degree is determined when the failure parameter is greater than a first threshold, the second failure degree is determined when the failure parameter is greater than a second threshold, and the like, which is not specifically limited.
It can be understood that when the fault prediction result is the first fault degree, the fault degree is relatively low, and the embodiment of the application can timely remind a driver of the vehicle of the fault so as to remind the driver that the vehicle is likely to be in fault and pay attention to driving safety, so that the driver can be effectively reminded before the fault occurs, the probability of the fault occurrence is reduced, and the safety of the vehicle is improved; when the fault prediction result is the second degree of failure, it is heavier to show the degree of failure, and this application embodiment can in time carry out sending to predetermineeing the terminal, for example car owner mobile terminal to the driver of vehicle when carrying out the trouble warning to make the car owner in time know the trouble condition of vehicle, promote to use and experience.
In the present embodiment, after outputting the result of predicting the failure of the vehicle to be diagnosed, the method further includes: and performing data compression on the current fault data and the diagnosis result, and storing the compressed data to preset storage equipment.
The preset storage device may be a memory or the like, and those skilled in the art may specifically select the preset storage device according to actual storage requirements, which is not specifically limited.
It can be understood that, because the number of the vehicle-mounted ECUs is increasing at present, the data information to be uploaded is increasing, and therefore, the embodiment of the application compresses the uploaded data to save the storage space, and the compressed data can be conveniently called in a later period, for example, the stored data can be monitored by a fault prediction model in real time.
It should be noted that an execution subject of the remote diagnosis method for the vehicle according to the embodiment of the present application may be a server, where the service may be, for example, a cloud diagnosis platform. The remote diagnosis method of the vehicle will be explained by taking the cloud diagnosis platform as shown in fig. 2 as an example.
As shown in fig. 2, the cloud diagnosis system includes a cloud diagnosis platform and a vehicle to be diagnosed, wherein the cloud diagnosis system includes a cloud service data processing unit, a data compression unit and a cloud storage management unit, and the vehicle to be diagnosed includes a remote control module, a central gateway and an on-vehicle CAN control, specifically: the cloud service data processing unit analyzes the data uploaded in the remote control mode and establishes a fault prediction model; the data compression unit compresses and stores data to be stored into the cloud storage unit, so that the storage space is saved; the remote control module collects fault information uploaded by each vehicle-mounted CAN controller of the whole vehicle, performs data encapsulation according to a signal protocol, and sends the fault information to the cloud diagnosis platform at a preset reporting frequency through 4G/5G/WIFI; the central gateway transmits the fault information to be reported by the vehicle-mounted CAN controllers on different CAN lines of the whole vehicle to the remote control module; and the vehicle-mounted CAN controller encapsulates the fault information data according to a signal protocol and sends the fault information data to the central gateway through the CAN network at a preset reporting frequency.
The remote vehicle diagnosis method based on the cloud diagnosis system of the embodiment specifically comprises the following steps:
1. the vehicle-mounted CAN controller encapsulates fault types including no fault, current fault, historical fault and the like and fault specific parameters including parameter information such as fault accumulation/subtraction parameters, fault trigger thresholds, current values and the like according to a signal protocol released by a host factory, and transmits encapsulated data to a central gateway according to a preset reporting frequency;
2. the central gateway forwards the data uploaded by each vehicle-mounted controller to the remote control module;
3. the remote control module receives the forwarded data of the central gateway, packages the data again according to a signal protocol released by a host factory, and sends the packaged data to the cloud diagnosis platform through 4G/5G/WIFI according to a preset reporting frequency;
4. the cloud diagnosis platform receives data uploaded by the remote control module through 4G/5G/WIFI, the cloud service data processing unit analyzes and processes the data, and fault states are judged through fault zone bits and are respectively no fault, current fault and historical fault; establishing a fault prediction model through specific fault parameters, updating in real time, and constructing a unified fault data management system to realize efficient management of fault data;
5. the remote control module uploads the data to the cloud diagnosis platform in real time through the 4G/5G/WIFI remote control module according to a preset reporting frequency, the data volume is accumulated in real time, the data compression unit compresses the data processed by the cloud diagnosis service processing unit, and the compressed data is stored in the cloud storage management unit.
According to the remote diagnosis method for the vehicle, the fault prediction model is established according to the uploaded data, the diagnosis result of the vehicle is predicted on line by using the fault prediction model, the vehicle can be predicted before the fault occurs, the fault does not need to wait passively, the fault occurrence probability is greatly reduced, the diagnosis intelligence and the vehicle safety are improved, and the use experience of a user is improved.
Next, a remote diagnosis apparatus of a vehicle according to an embodiment of the present application is described with reference to the drawings.
Fig. 3 is a block schematic diagram of a remote diagnosis apparatus of a vehicle according to an embodiment of the present application.
As shown in fig. 3, the remote diagnosis apparatus 10 for a vehicle includes: an acquisition module 100, a matching module 200 and a prediction module 300.
The acquiring module 100 is configured to acquire current fault data and identity information uploaded by a vehicle to be diagnosed; the matching module 200 is used for matching a fault prediction model of the vehicle to be diagnosed based on the identity identification information; the prediction module 300 is configured to substitute the current fault data into a fault prediction model, and predict a diagnosis result of the vehicle to be diagnosed by the fault prediction model, where the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed.
Further, the apparatus 10 of the embodiment of the present application further includes: the device comprises an updating module, a reminding module and a storage module.
The updating module is used for updating the fault prediction model according to current fault data before the fault prediction model predicts the diagnosis result of the vehicle to be diagnosed, and predicting the diagnosis result of the vehicle to be diagnosed by the updated fault prediction model; the reminding module is used for generating first reminding information and sending the first reminding information to the vehicle to be diagnosed if the fault prediction result is the first fault degree after the fault prediction result of the vehicle to be diagnosed is output; if the fault prediction result is a second fault degree, generating second reminding information, sending the second reminding information to the vehicle to be diagnosed, and sending the second reminding information to the preset terminal, wherein the fault severity hazard degree of the second fault degree is greater than the fault severity hazard degree of the first fault degree; and the storage module is used for performing data compression on the current fault data and the diagnosis result after outputting the fault prediction result of the vehicle to be diagnosed, and storing the compressed data into preset storage equipment.
Further, the apparatus 10 of the embodiment of the present application further includes: and a training module. The system comprises a training module, a diagnosis module and a fault diagnosis module, wherein the training module is used for acquiring historical fault data uploaded by a vehicle to be diagnosed; analyzing a fault zone bit and counting fault parameters of historical fault data to obtain at least one historical fault parameter of the vehicle to be diagnosed; and training to obtain a fault prediction model of the vehicle to be diagnosed based on at least one historical fault parameter.
It should be noted that the foregoing explanation of the embodiment of the remote diagnosis method for a vehicle is also applicable to the remote diagnosis device for a vehicle of this embodiment, and is not repeated herein.
According to the remote diagnosis device for the vehicle, the fault prediction model is established according to the uploaded data, the diagnosis result of the vehicle is predicted on line by using the fault prediction model, the vehicle can be predicted before the fault occurs, the fault does not need to be passively waited for to occur, the fault occurrence probability is greatly reduced, the diagnosis intelligence and the vehicle safety are improved, and the use experience of a user is improved.
Fig. 4 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:
The processor 402, when executing the program, implements the remote diagnosis method of the server provided in the above-described embodiment.
Further, the server further comprises:
a communication interface 403 for communication between the memory 401 and the processor 402.
A memory 401 for storing computer programs executable on the processor 402.
The Memory 401 may include a high-speed RAM (Random Access Memory) Memory, and may also include a non-volatile Memory, such as at least one disk Memory.
If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 may be connected to each other through a bus and perform communication with each other. The bus may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 403 are integrated on a chip, the memory 401, the processor 402, and the communication interface 403 may complete mutual communication through an internal interface.
Embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the remote diagnosis method of the vehicle as above.
In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present application, "N" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of implementing the embodiments of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a programmable gate array, a field programmable gate array, or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
Claims (10)
1. A remote diagnosis method for a vehicle, characterized by comprising the steps of:
acquiring current fault data and identity identification information uploaded by the vehicle to be diagnosed;
matching a fault prediction model of the vehicle to be diagnosed based on the identity identification information, wherein the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed; and
and substituting the current fault data into the fault prediction model, and predicting the diagnosis result of the vehicle to be diagnosed by the fault prediction model.
2. The method according to claim 1, before predicting the diagnosis result of the vehicle to be diagnosed by the failure prediction model, further comprising:
and updating the fault prediction model according to the current fault data, and predicting the diagnosis result of the vehicle to be diagnosed by the updated fault prediction model.
3. The method of claim 1, wherein the fault prediction model is trained from historical fault data of the vehicle to be diagnosed, comprising:
acquiring historical fault data uploaded and stored by the vehicle to be diagnosed;
analyzing a fault zone bit and counting fault parameters of the historical fault data to obtain at least one historical fault parameter of the vehicle to be diagnosed;
and training to obtain a fault prediction model of the vehicle to be diagnosed based on the at least one historical fault parameter.
4. The method according to claim 1, further comprising, after outputting the result of the failure prediction of the vehicle to be diagnosed,:
if the fault prediction result is a first fault degree, generating first reminding information, and sending the first reminding information to the vehicle to be diagnosed;
and if the fault prediction result is a second fault degree, generating second reminding information, sending the second reminding information to the vehicle to be diagnosed, and sending the second reminding information to a preset terminal, wherein the severity of the second fault degree is greater than the severity of the first fault degree.
5. The method according to any one of claims 1 to 4, characterized by, after outputting the result of failure prediction of the vehicle to be diagnosed, further comprising:
and performing data compression on the current fault data and the diagnosis result, and storing the compressed data to preset storage equipment.
6. A remote diagnosis apparatus for a vehicle, comprising:
the acquisition module is used for acquiring the current fault data and the identity identification information uploaded by the vehicle to be diagnosed;
the matching module is used for matching a fault prediction model of the vehicle to be diagnosed based on the identity identification information, wherein the fault prediction model is obtained by training historical fault data of the vehicle to be diagnosed; and
and the prediction module is used for substituting the current fault data into the fault prediction model, and predicting the diagnosis result of the vehicle to be diagnosed by the fault prediction model.
7. The method of claim 1, further comprising:
the updating module is used for updating the fault prediction model according to the current fault data before the fault prediction model predicts the diagnosis result of the vehicle to be diagnosed, and predicting the diagnosis result of the vehicle to be diagnosed by the updated fault prediction model;
the reminding module is used for generating first reminding information and sending the first reminding information to the vehicle to be diagnosed if the fault prediction result is a first fault degree after the fault prediction result of the vehicle to be diagnosed is output; if the fault prediction result is a second fault degree, generating second reminding information, sending the second reminding information to the vehicle to be diagnosed, and sending the second reminding information to a preset terminal, wherein the severity of the second fault degree is greater than the severity of the first fault degree;
and the storage module is used for performing data compression on the current fault data and the diagnosis result after outputting the fault prediction result of the vehicle to be diagnosed, and storing the compressed data into preset storage equipment.
8. The apparatus of claim 6, further comprising:
the training module is used for acquiring historical fault data uploaded by the vehicle to be diagnosed; analyzing a fault zone bit and counting fault parameters of the historical fault data to obtain at least one historical fault parameter of the vehicle to be diagnosed; and training to obtain a fault prediction model of the vehicle to be diagnosed based on the at least one historical fault parameter.
9. A server, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the remote diagnosis method of a vehicle according to any one of claims 1 to 5.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing a remote diagnosis method of a vehicle according to any one of claims 1 to 5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111492980.8A CN114296426A (en) | 2021-12-08 | 2021-12-08 | Remote diagnosis method and device for vehicle, server and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111492980.8A CN114296426A (en) | 2021-12-08 | 2021-12-08 | Remote diagnosis method and device for vehicle, server and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114296426A true CN114296426A (en) | 2022-04-08 |
Family
ID=80965518
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111492980.8A Pending CN114296426A (en) | 2021-12-08 | 2021-12-08 | Remote diagnosis method and device for vehicle, server and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114296426A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116257039A (en) * | 2023-05-16 | 2023-06-13 | 江西五十铃汽车有限公司 | Vehicle fault diagnosis hierarchical processing method and program product based on big data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160035150A1 (en) * | 2014-07-30 | 2016-02-04 | Verizon Patent And Licensing Inc. | Analysis of vehicle data to predict component failure |
| CN107403480A (en) * | 2017-06-15 | 2017-11-28 | 北汽福田汽车股份有限公司 | A kind of vehicle trouble method for early warning, system and vehicle |
| CN108693868A (en) * | 2018-05-25 | 2018-10-23 | 深圳市轱辘车联数据技术有限公司 | The method of failure predication model training, the method and device of vehicle trouble prediction |
| CN110519382A (en) * | 2019-08-30 | 2019-11-29 | 成都康普斯北斗科技有限公司 | A kind of automobile intelligent monitoring system |
| CN111795835A (en) * | 2020-06-30 | 2020-10-20 | 北京汽车研究总院有限公司 | Vehicle monitoring method and device and vehicle-mounted equipment |
| CN112363488A (en) * | 2020-12-07 | 2021-02-12 | 东风柳州汽车有限公司 | Vehicle fault processing system and method |
-
2021
- 2021-12-08 CN CN202111492980.8A patent/CN114296426A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160035150A1 (en) * | 2014-07-30 | 2016-02-04 | Verizon Patent And Licensing Inc. | Analysis of vehicle data to predict component failure |
| CN107403480A (en) * | 2017-06-15 | 2017-11-28 | 北汽福田汽车股份有限公司 | A kind of vehicle trouble method for early warning, system and vehicle |
| CN108693868A (en) * | 2018-05-25 | 2018-10-23 | 深圳市轱辘车联数据技术有限公司 | The method of failure predication model training, the method and device of vehicle trouble prediction |
| CN110519382A (en) * | 2019-08-30 | 2019-11-29 | 成都康普斯北斗科技有限公司 | A kind of automobile intelligent monitoring system |
| CN111795835A (en) * | 2020-06-30 | 2020-10-20 | 北京汽车研究总院有限公司 | Vehicle monitoring method and device and vehicle-mounted equipment |
| CN112363488A (en) * | 2020-12-07 | 2021-02-12 | 东风柳州汽车有限公司 | Vehicle fault processing system and method |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116257039A (en) * | 2023-05-16 | 2023-06-13 | 江西五十铃汽车有限公司 | Vehicle fault diagnosis hierarchical processing method and program product based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zaldivar et al. | Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones | |
| CN108415409B (en) | Multi-stage automobile fault diagnosis system and diagnosis method | |
| KR20190107080A (en) | Cloud-based vehicle fault diagnosis method, apparatus and system | |
| WO2020110446A1 (en) | Vehicle malfunction prediction system, monitoring device, vehicle malfunction prediction method, and vehicle malfunction prediction program | |
| CN112068536A (en) | Universal self-adaptive vehicle remote diagnosis method, device and system | |
| US20200111272A1 (en) | Health self learning system and method for electrical distribution systems for automated driving vehicles | |
| CN108919776A (en) | A kind of assessment of failure method and terminal | |
| CN112650201B (en) | Vehicle fault diagnosis method and device, vehicle and storage medium | |
| CN113296493A (en) | Remote silence diagnosis method and device for vehicle, electronic equipment and storage medium | |
| CN114296426A (en) | Remote diagnosis method and device for vehicle, server and storage medium | |
| KR20220125689A (en) | Method of determining the operational condition of vehicle components | |
| CN113505056A (en) | Vehicle diagnosis method, system, device and storage medium | |
| CN113787914A (en) | Power battery monitoring method and device, server and storage medium | |
| US12192286B2 (en) | Systems and methods for remote industrial machine communciation system diagnostics and solutions | |
| CN112702204A (en) | Equipment monitoring method, device, server and storage medium | |
| CN116886727A (en) | Vehicle data acquisition method and device based on vehicle cloud cooperation | |
| CN114415646B (en) | Remote vehicle diagnosis method, system and terminal equipment based on DoIP protocol | |
| CN116774050A (en) | Battery fault processing method and device, cloud server and storage medium | |
| CN114967632B (en) | IoV network signal diagnosis system, method and vehicle | |
| JP4315073B2 (en) | Failure analysis system | |
| CN113263913B (en) | Method and device for transmitting automobile fault, electronic control unit and storage medium | |
| CN114167846A (en) | A remote fault diagnosis system and method for a new energy vehicle | |
| CN114995353A (en) | Vehicle diagnosis method, device and system | |
| CN114422506A (en) | Data transmission method of working machine and data transmission device of working machine | |
| CN212243228U (en) | Vehicle-mounted T-BOX monitoring system and vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |