CN117037328A - Abnormality detection method for vehicle, related equipment and vehicle - Google Patents
Abnormality detection method for vehicle, related equipment and vehicle Download PDFInfo
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- G07C5/0808—Diagnosing performance data
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
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Abstract
The application provides a vehicle abnormality detection method, related equipment and a vehicle, wherein the method comprises the following steps: and in response to determining that the log data of the vehicle has the preset abnormal data identifier, calling a printing function in the control software code to print the log data associated with the abnormal data identifier, so as to obtain the abnormal log data. And sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle. After the cloud receives the abnormal log data, the vehicle is subjected to abnormal detection analysis by combining the running data of the vehicle, so that the accuracy of the abnormal detection analysis is improved, and better after-sales tracking service is provided for users.
Description
Technical Field
The present application relates to the field of vehicle technologies, and in particular, to a method for detecting an abnormality of a vehicle, a related device, and a vehicle.
Background
After the vehicle is delivered offline to a user, abnormal problems can occur in the running process of the vehicle, in order to track and analyze the abnormal problems, vehicle bus data are uploaded to the cloud, and the abnormal problems of the vehicle are analyzed through a data recorder. However, the vehicle bus data cannot cover all abnormal data in the running process of the vehicle, and technicians cannot acquire the abnormal data generated during the running of the software in part of the controllers, so that the accuracy of the subsequent abnormality detection analysis on the vehicle is affected, and good after-sales tracking service cannot be provided for users.
Disclosure of Invention
In view of the above, the present application aims to provide a vehicle abnormality detection method, a related device and a vehicle, so as to solve the problem that the accuracy of subsequent vehicle abnormality detection analysis is low because abnormal data generated during the running of software in a part of controllers cannot be obtained.
In view of the above object, a first aspect of the present application provides an abnormality detection method for a vehicle, applied to a vehicle-end controller, including:
in response to determining that a preset abnormal data identifier exists in the log data of the vehicle, calling a printing function in a control software code to print the log data associated with the abnormal data identifier to obtain abnormal log data;
and sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle.
Optionally, the method further comprises:
and in response to determining that abnormal content exists in the log data, adding a corresponding abnormal data identifier for the abnormal content.
Optionally, before sending the anomaly log data to the cloud, the method further includes:
and sending and storing the abnormal log data to a storage unit through an Ethernet port.
The second aspect of the present application provides a method for detecting abnormality of a vehicle, applied to a cloud, including:
the method comprises the steps of receiving abnormal log data sent by a vehicle-end controller, wherein the abnormal log data are obtained by calling a printing function in a control software code by the vehicle-end controller to print log data associated with an abnormal data identifier;
determining corresponding vehicle driving data according to the abnormal log data;
and performing abnormality detection analysis on the vehicle based on the abnormality log data and the vehicle running data.
Optionally, the determining corresponding vehicle driving data according to the anomaly log data includes:
extracting a timestamp in the exception log data;
and determining the vehicle driving data according to the time stamp.
A third aspect of the present application provides an abnormality detection apparatus for a vehicle, applied to a vehicle-end controller, including:
the printing module is configured to respond to the fact that the preset abnormal data identification exists in the log data of the vehicle, and a printing function in a control software code is called to print the log data associated with the abnormal data identification, so that the abnormal log data are obtained;
the sending module is configured to send the abnormal log data to a cloud end so that the cloud end can conduct abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle.
A fourth aspect of the present application provides an abnormality detection apparatus for a vehicle, applied to a cloud, including:
the receiving module is configured to receive abnormal log data sent by the vehicle-end controller, wherein the abnormal log data is obtained by calling a printing function in a control software code by the vehicle-end controller to print log data associated with the abnormal data identifier;
a determining module configured to determine corresponding vehicle running data from the abnormality log data;
an analysis module configured to perform abnormality detection analysis on the vehicle based on the abnormality log data and the vehicle running data.
A fifth aspect of the application also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable by the processor, the processor implementing the method according to the first aspect when executing the computer program.
The sixth aspect of the application also provides a non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method as described above.
A seventh aspect of the application also provides a vehicle comprising an electronic device as claimed in the fifth aspect.
As can be seen from the above, the abnormality detection method for a vehicle, the related device and the vehicle provided by the application, the method comprises the following steps: and in response to determining that the log data of the vehicle has the preset abnormal data identifier, calling a printing function in the control software code to print the log data associated with the abnormal data identifier, so as to obtain the abnormal log data. By means of the abnormal data identification preset in the log data, abnormal log data can be quickly identified, the printing function is timely called to print the abnormal log data, the abnormal log data is visualized, and the abnormal running state of the vehicle can be conveniently analyzed according to the abnormal log data. And sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle. After the cloud receives the abnormal log data, the vehicle is subjected to abnormal detection analysis by combining the running data of the vehicle, so that the accuracy of the abnormal detection analysis is improved, and better after-sales tracking service is provided for users.
Drawings
In order to more clearly illustrate the technical solutions of the present application or related art, the drawings that are required to be used in the description of the embodiments or related art will be briefly described below, and it is apparent that the drawings in the following description are only embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort to those of ordinary skill in the art.
Fig. 1 is a flowchart of a method for detecting an abnormality of a vehicle according to an embodiment of the present application;
fig. 2 is a flowchart of a method for detecting an abnormality of a vehicle according to another embodiment of the present application;
fig. 3 is a schematic structural view of an abnormality detection apparatus of a vehicle according to an embodiment of the present application;
fig. 4 is a schematic structural view of an abnormality detection device of a vehicle according to another embodiment of the present application;
fig. 5 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
The present application will be further described in detail below with reference to specific embodiments and with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent.
It should be noted that unless otherwise defined, technical or scientific terms used in the embodiments of the present application should be given the ordinary meaning as understood by one of ordinary skill in the art to which the present application belongs. The terms "first," "second," and the like, as used in embodiments of the present application, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As described in the background art, the data recorder is used to analyze the abnormal state of the vehicle during running, and the bus data of the vehicle needs to be uploaded to the cloud in advance. The bus is used to enable data exchange between a plurality of electronic control modules in the vehicle. But the data recorded by the bus is limited and for many electronic control modules anomalies in the run-time logic cannot be recorded. Therefore, these abnormalities cannot be analyzed specifically. In view of the above, the application provides a vehicle abnormality detection method, which can timely print abnormality log data generated in the running process of a vehicle, record the change process of the vehicle entering an abnormal state, and facilitate the subsequent analysis and treatment of the abnormal state of the vehicle.
Embodiments of the present application are described in detail below with reference to the accompanying drawings.
The application provides an anomaly detection method of a vehicle, which is applied to a vehicle end controller, and referring to FIG. 1, the anomaly detection method comprises the following steps:
step 102, in response to determining that a preset abnormal data identifier exists in the log data of the vehicle, calling a printing function in a control software code to print the log data associated with the abnormal data identifier, and obtaining the abnormal log data.
Specifically, the abnormal data identifier is preset in the log data, and when the same data as the data in the pre-calibrated abnormal data table appears in the log data of the vehicle, the abnormal data identifier identifies the part of data, and the abnormal data identifier represents that the vehicle has an abnormal working condition or an abnormal state. And after the abnormal data identification is detected in the log data, calling a printing function in the control software code to print the log data associated with the abnormal data identification. The printing function can output the content to the file for storage, the abnormal log data is obtained after the printing function prints, and the abnormal log data records the change process of the vehicle running to the abnormal working condition or the abnormal state.
The control software code in this embodiment was developed under the automobile open system architecture (AUTomotive OpenSystem Architecture, AUTOSAR). The AUTOSAR architecture is an open automobile software architecture standard, and aims to solve the problems of complexity and fragmentation in the development of an automobile electronic system. It provides a unified software architecture and development method that allows hardware and software components of different vendors to interoperate on different automotive platforms. The AUTOSAR architecture includes an application software layer, a runtime environment, a base software layer, and a microcontroller. The application software layer includes a plurality of SWCs (Sotware Component, software components), each of which may be regarded as a functional module, and the different SWCs interact through the AUTOSAR interface. Each SWC may be composed of Runnable and ports. Port is the interface for interaction between SWCs. Runneable can be understood as a function, and can be classified into an Initial Runneable, a Server Runneable, and a Runneable according to the trigger type of the Runneable. Wherein the Initial Runneable is triggered by initialization, the Server Runneable is triggered by event, and the Runneable is triggered by timer.
In this embodiment, the control software code is generated using a simulink module in matlab software. In the process of generating a control software code of software, a server run Function is used for packaging a printing Function, then the packaged server run Function is connected with a Function call, the Function call sets a triggering condition for printing abnormal log data, namely if an abnormal working condition or an abnormal state occurs during the operation of the control software code, the server run Function can be called by the Function call, and then a printing Function packaged in the server run Function is operated to print a log.
And 104, sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle.
Specifically, after the abnormal log data is obtained by printing, the abnormal log data can be immediately sent to the cloud or regularly packaged and sent to the cloud. When the abnormality detection analysis is performed on the vehicle, it is necessary to combine and analyze abnormality log data and running data of the vehicle, and the running data of the vehicle can reflect the running condition of the whole vehicle when abnormality occurs. And checking the running condition when the vehicle runs to the abnormal working condition or abnormal state, and combining the abnormal log data can improve the accuracy of analysis on the abnormal state of the vehicle.
Based on the steps 102 to 104, the abnormality detection method for a vehicle provided in the present embodiment includes: and in response to determining that the log data of the vehicle has the preset abnormal data identifier, calling a printing function in the control software code to print the log data associated with the abnormal data identifier, so as to obtain the abnormal log data. By means of the abnormal data identification preset in the log data, the abnormal log data can be quickly identified, the printing function is timely called to print the abnormal log data, the abnormal log data is visualized, and the abnormal running state of the vehicle can be conveniently analyzed according to the abnormal log data. And sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle. After the cloud receives the abnormal log data, the vehicle is subjected to abnormal detection analysis by combining the running data of the vehicle, so that the accuracy of the abnormal detection analysis is improved, and better after-sales tracking service is provided for users.
In some embodiments, the method further comprises:
and in response to determining that abnormal content exists in the log data, adding a corresponding abnormal data identifier for the abnormal content.
Specifically, in order to be able to print the abnormal log data, it is necessary to set an abnormal data identification in the log data in advance. According to the abnormal working conditions and abnormal states possibly encountered in the running process of the vehicle, a calibration table is preset, and the calibration table gathers and records abnormal data content and abnormal data identification corresponding to each abnormal working condition or abnormal state. Generating log data after the vehicle runs, comparing the content in the log data with the content in the calibration table, and if abnormal content recorded in the calibration table appears in the log data, adding a corresponding abnormal data identifier for the abnormal content. The addition of the abnormal data identification can accurately locate abnormal data in the log data, provide a data basis for detecting and analyzing the abnormal condition of the vehicle for technicians, enable the technicians to acquire the abnormal information of the vehicle which needs to be concerned, and formulate a targeted vehicle optimization method so as to provide better after-sales tracking service for users.
In some embodiments, before sending the exception log data to the cloud, the method further comprises: and sending and storing the abnormal log data to a storage unit through an Ethernet port.
Specifically, before the abnormal log data is sent to the cloud end, the abnormal log data needs to be stored in a storage unit of the vehicle end, and after the sending period is reached, the abnormal log data in one period is packaged and sent to the cloud end, so that daily occupation of a communication channel between the vehicle end and the cloud end is reduced. After the abnormal log data is printed through the printing function, the abnormal log data is sent to a storage unit at the vehicle end through an Ethernet port for storage, the abnormal log data is stored and managed in a centralized mode, and a data base is provided for the abnormal detection analysis of the subsequent vehicle.
In some embodiments, the method further comprises:
and sequencing the abnormal log data according to the time stamp in the abnormal log data, and storing the sequenced abnormal log data into the storage unit.
Specifically, after the exception log data is sent and stored in the storage unit, all the exception log data in the storage unit are ordered according to the time stamp in each exception log data to obtain an ordered list, and the ordered list is stored in the storage unit. Alternatively, the identification of each of the abnormality log data is stored in the ordered list, and the identification is used as an index value of the abnormality log data in the storage unit. And the exception log data is convenient to query and manage. The exception log data with the same time stamp can be stored in an associated mode, so that a subsequent technician can perform associated analysis on the associated stored exception log data.
The application also provides an anomaly detection method of the vehicle, which is applied to the cloud, and referring to fig. 2, the anomaly detection method comprises the following steps:
step 202, receiving abnormal log data sent by a vehicle-end controller, wherein the abnormal log data is obtained by calling a printing function in a control software code by the vehicle-end controller to print log data associated with the abnormal data identifier.
Specifically, the abnormal log data sent by the vehicle-end controller in real time or periodically is received and used for carrying out abnormal detection analysis on the vehicle. The abnormal log data is obtained by calling a printing function by the vehicle-end controller to print the log data associated with the abnormal data identifier. The abnormal data identification is preset in the log data, and when the same data as the data in the pre-calibrated abnormal data table appears in the log data of the vehicle, the abnormal data identification is carried out on the part of data, and the abnormal data identification represents the abnormal working condition or abnormal state of the vehicle. And after the abnormal data identification is detected in the log data, calling a printing function in the control software code to print the log data associated with the abnormal data identification. The printing function can output the content to the file for storage, the abnormal log data is obtained after the printing function prints, and the abnormal log data records the change process of the vehicle running to the abnormal working condition or the abnormal state. By means of the abnormal data identification preset in the log data, the abnormal log data can be quickly identified, the printing function is timely called to print the abnormal log data, the abnormal log data is visualized, and the abnormal running state of the vehicle can be conveniently analyzed according to the abnormal log data.
And 204, determining corresponding vehicle running data according to the abnormal log data.
Specifically, the abnormal log data corresponds to abnormal vehicle running data at a certain moment, the abnormal data are analyzed to be combined with running data of the vehicle at the moment, the running data can reflect the running condition of the whole vehicle, and the running data comprise the speed of the vehicle, the speed of the engine and the like. Therefore, it is necessary to determine the vehicle running data at the corresponding time from the abnormality log data.
And 206, performing abnormality detection analysis on the vehicle based on the abnormality log data and the vehicle running data.
Specifically, the same anomaly log data may correspond to different vehicle operating states, that is, the causes of the same anomaly condition may be various. Therefore, the cause of the abnormality of the vehicle cannot be estimated accurately from the single abnormality log data. The reasons for abnormal conditions of the vehicle can be reasonably inferred and analyzed by combining the vehicle driving data. By combining the anomaly log data and the vehicle running data to perform anomaly detection analysis on the vehicle, the accuracy of detection analysis can be remarkably improved, an improvement direction is provided for the optimization of the subsequent vehicle software function, and the satisfaction degree of a user on using the vehicle is improved.
In some embodiments, the determining corresponding vehicle travel data from the anomaly log data includes:
extracting a timestamp in the exception log data;
and determining the vehicle driving data according to the time stamp.
Specifically, a timestamp exists in the printed anomaly log data. The timestamp refers to a manner of recording time, and is generally used for recording the occurrence time of an event or information such as creation and modification time of a file. By means of the time stamps, we can conveniently sort, query and compare the events, thus better managing and analyzing the data. The time stamp in the abnormality log data records time information of the generation of the abnormality log data. According to the time stamp, vehicle driving data at corresponding time can be accurately searched, and accuracy of vehicle abnormality detection and analysis is improved. In a specific embodiment, the timestamp may be sent to the vehicle end to request to query the driving data, and the vehicle end queries the driving data corresponding to the timestamp in the historical driving data stored in the vehicle end according to the timestamp and sends the driving data to the cloud. Or, the running data of the vehicle which is uploaded in advance is called in a database of the cloud or a database of a third party platform. In another embodiment, if there is no travel data corresponding to the time stamp in the historical travel data stored in the vehicle end, the travel data with the shortest time interval from the time stamp is selected as the travel data to be uploaded to the cloud.
In some embodiments, the method further comprises:
determining an abnormal event according to the abnormal log data and the vehicle running data;
determining the early warning level of the abnormal event according to a preset rule;
and sending early warning information according to the early warning level.
Specifically, the abnormal event is determined after the vehicle is subjected to abnormality detection analysis according to the abnormality log data and the driving data, and the abnormal event may include electronic equipment in the vehicle generating the abnormal data and/or a cause of the abnormality. Different abnormal events correspond to different early warning levels, if the abnormal events influence normal running of the vehicle and potential hazards exist in the vehicle and personal safety, the corresponding early warning levels are higher, for example, an application program in a controller exits, and the driving function of the vehicle is directly influenced. If the abnormal event has no influence on the normal running of the vehicle, the corresponding early warning level is lower. The early warning information corresponding to the abnormal event with higher early warning level can comprise sending the early warning information to the monitoring client in real time, reminding technicians of paying attention to the abnormal event in time, and meanwhile, the early warning information can be accompanied with a certain acousto-optic reminding. The early warning information corresponding to the abnormal event with the lower early warning level can be early warning information sent to the monitoring client at regular intervals. In addition, for the abnormal event with higher occurrence frequency, the reminding information can be additionally sent to the monitoring client, so that the technician is prevented from disregarding the abnormal event of the type. The method of the embodiment can provide timely and comprehensive early warning information for technicians, help the technicians track abnormal states in the running process of the vehicle, and provide better after-sales tracking service for users.
The application also provides an abnormality detection method of the vehicle, comprising the following steps:
in response to determining that a preset abnormal data identifier exists in the log data of the vehicle, calling a printing function in a control software code by a vehicle end controller to print the log data associated with the abnormal data identifier to obtain abnormal log data;
the vehicle-end controller sends the abnormal log data to a cloud;
the cloud receives abnormal log data sent by a vehicle-end controller;
the cloud end determines corresponding vehicle driving data according to the abnormal log data;
and the cloud performs anomaly detection analysis on the vehicle based on the anomaly log data and the vehicle running data.
According to the abnormality detection method for the vehicle, in response to determining that the preset abnormal data identifier exists in the log data of the vehicle, a printing function in a control software code is called to print the log data associated with the abnormal data identifier, and the abnormal log data is obtained. By means of the abnormal data identification preset in the log data, the abnormal log data can be quickly identified, the printing function is timely called to print the abnormal log data, the abnormal log data is visualized, and the abnormal running state of the vehicle can be conveniently analyzed according to the abnormal log data. And sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle. After the cloud receives the abnormal log data, the vehicle is subjected to abnormal detection analysis by combining the running data of the vehicle, so that the accuracy of the abnormal detection analysis is improved, and better after-sales tracking service is provided for users.
It should be noted that, the method of the embodiment of the present application may be performed by a single device, for example, a computer or a server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the method of an embodiment of the present application, the devices interacting with each other to accomplish the method.
It should be noted that the foregoing describes some embodiments of the present application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments described above and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Based on the same inventive concept, the application also provides an abnormality detection device of the vehicle, corresponding to the method of any embodiment.
Referring to fig. 3, the abnormality detection apparatus of a vehicle, applied to a vehicle-end controller, includes:
the printing module 302 is configured to call a printing function in a control software code to print the log data associated with the abnormal data identifier to obtain the abnormal log data in response to determining that the preset abnormal data identifier exists in the log data of the vehicle;
the sending module 304 is configured to send the anomaly log data to a cloud end, so that the cloud end performs anomaly detection analysis on the vehicle based on the anomaly log data and the driving data of the vehicle.
In some embodiments, an identification module is further included that is configured to, in response to determining that anomalous content is present in the log data, add a corresponding anomalous data identification for the anomalous content.
In some embodiments, before the sending the exception log data to the cloud, a storage module is further included, the storage module configured to send and store the exception log data to a storage unit through an ethernet port.
In some embodiments, the method further comprises ordering the exception log data according to the time stamp in the exception log data, and storing the ordered exception log data to the storage unit.
Based on the same inventive concept, the application also provides an abnormality detection device of the vehicle, corresponding to the method of any embodiment.
Referring to fig. 4, the abnormality detection device for a vehicle, applied to a cloud, includes:
the receiving module 402 is configured to receive abnormal log data sent by the vehicle-end controller, wherein the abnormal log data is obtained by the vehicle-end controller calling a printing function in a control software code to print log data associated with the abnormal data identifier;
a determining module 404 configured to determine corresponding vehicle travel data from the anomaly log data;
an analysis module 406 configured to perform abnormality detection analysis on the vehicle based on the abnormality log data and the vehicle travel data.
In some embodiments, the determining module 404 is further configured to extract a timestamp in the exception log data; and determining the vehicle driving data according to the time stamp.
In some embodiments, the system further comprises an early warning module configured to determine an abnormal event from the abnormal log data and the vehicle travel data; determining the early warning level of the abnormal event according to a preset rule; and sending early warning information according to the early warning level.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
The device of the foregoing embodiment is used to implement the abnormality detection method of the vehicle corresponding to any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the application also provides an electronic device corresponding to the method of any embodiment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the method for detecting the abnormality of the vehicle according to any embodiment applied to the vehicle end controller.
Fig. 5 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), microprocessor, application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, etc. for executing relevant programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage device, dynamic storage device, or the like. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The electronic device of the foregoing embodiment is configured to implement the abnormality detection method of the vehicle corresponding to any one of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the abnormality detection method of the vehicle according to any of the above embodiments, corresponding to the method of any of the above embodiments.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
The storage medium of the foregoing embodiment stores computer instructions for causing the computer to execute the abnormality detection method of the vehicle according to any one of the foregoing embodiments, and has the advantages of the corresponding method embodiments, which are not described herein.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the application (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the application, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the application as described above, which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure the embodiments of the present application. Furthermore, the devices may be shown in block diagram form in order to avoid obscuring the embodiments of the present application, and also in view of the fact that specifics with respect to implementation of such block diagram devices are highly dependent upon the platform within which the embodiments of the present application are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the application, it should be apparent to one skilled in the art that embodiments of the application can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the application has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present embodiments are intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, and the like, which are within the spirit and principles of the embodiments of the application, are intended to be included within the scope of the application.
Claims (10)
1. An abnormality detection method for a vehicle, applied to a vehicle-end controller, comprising:
in response to determining that a preset abnormal data identifier exists in the log data of the vehicle, calling a printing function in a control software code to print the log data associated with the abnormal data identifier to obtain abnormal log data;
and sending the abnormal log data to a cloud end so that the cloud end carries out abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle.
2. The method according to claim 1, wherein the method further comprises:
and in response to determining that abnormal content exists in the log data, adding a corresponding abnormal data identifier for the abnormal content.
3. The method of claim 1, wherein prior to sending the exception log data to the cloud, the method further comprises:
and sending and storing the abnormal log data to a storage unit through an Ethernet port.
4. The abnormality detection method for the vehicle is characterized by being applied to a cloud end and comprising the following steps of:
the method comprises the steps of receiving abnormal log data sent by a vehicle-end controller, wherein the abnormal log data are obtained by calling a printing function in a control software code by the vehicle-end controller to print log data associated with an abnormal data identifier;
determining corresponding vehicle driving data according to the abnormal log data;
and performing abnormality detection analysis on the vehicle based on the abnormality log data and the vehicle running data.
5. The method of claim 4, wherein the determining corresponding vehicle travel data from the anomaly log data comprises:
extracting a timestamp in the exception log data;
and determining the vehicle driving data according to the time stamp.
6. An abnormality detection device for a vehicle, which is applied to a vehicle-end controller, comprising:
the printing module is configured to respond to the fact that the preset abnormal data identification exists in the log data of the vehicle, and a printing function in a control software code is called to print the log data associated with the abnormal data identification, so that the abnormal log data are obtained;
the sending module is configured to send the abnormal log data to a cloud end so that the cloud end can conduct abnormal detection analysis on the vehicle based on the abnormal log data and the running data of the vehicle.
7. An anomaly detection device of a vehicle, applied to a cloud, comprising:
the receiving module is configured to receive abnormal log data sent by the vehicle-end controller, wherein the abnormal log data is obtained by calling a printing function in a control software code by the vehicle-end controller to print log data associated with the abnormal data identifier;
a determining module configured to determine corresponding vehicle running data from the abnormality log data;
an analysis module configured to perform abnormality detection analysis on the vehicle based on the abnormality log data and the vehicle running data.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 3 when the program is executed by the processor.
9. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1 to 3 or 4-5.
10. A vehicle comprising the electronic device of claim 8.
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|---|---|---|---|
| CN202311015166.6A CN117037328A (en) | 2023-08-11 | 2023-08-11 | Abnormality detection method for vehicle, related equipment and vehicle |
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| CN202311015166.6A CN117037328A (en) | 2023-08-11 | 2023-08-11 | Abnormality detection method for vehicle, related equipment and vehicle |
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