CN113888775A - Vehicle early warning method, server, storage medium, vehicle early warning system and vehicle - Google Patents
Vehicle early warning method, server, storage medium, vehicle early warning system and vehicle Download PDFInfo
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- G—PHYSICS
- G07—CHECKING-DEVICES
- 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
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0808—Diagnosing performance data
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G07—CHECKING-DEVICES
- 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
- G07C5/008—Registering or indicating the working of vehicles communicating information to a remotely located station
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- G—PHYSICS
- G07—CHECKING-DEVICES
- 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
- G07C5/08—Registering or indicating performance data other than driving, working, idle, or waiting time, with or without registering driving, working, idle or waiting time
- G07C5/0816—Indicating performance data, e.g. occurrence of a malfunction
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
- H04L12/40006—Architecture of a communication node
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
- H04L2012/40208—Bus networks characterized by the use of a particular bus standard
- H04L2012/40215—Controller Area Network CAN
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/28—Data switching networks characterised by path configuration, e.g. LAN [Local Area Networks] or WAN [Wide Area Networks]
- H04L12/40—Bus networks
- H04L2012/40267—Bus for use in transportation systems
Abstract
The invention provides a vehicle early warning method, a server, a computer storage medium, a vehicle early warning system and a vehicle, wherein the method comprises the following steps: acquiring real-time vehicle-mounted data; calculating the matching degree of the vehicle-mounted data and an early warning working condition model; and predicting that the vehicle enters an early warning working condition according to the matching degree, and sending warning information. The vehicle early warning method of the embodiment of the invention predicts the working condition of the vehicle by monitoring the running state of the vehicle in real time; the early warning purpose is achieved in advance, and the driving safety of a user is protected.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a vehicle early warning method, a server, a non-temporary computer storage medium, a vehicle early warning system and a vehicle.
Background
With the development of vehicle technology, in some schemes, a cluster-type distributed parallel algorithm is used to mine association rules from a massive fault alarm information base, as shown in fig. 1, which is a schematic diagram of conventional fault early warning in the related art. The method comprises the steps of utilizing a knowledge base to predict and process alarm information, placing a fault alarm information base into a cluster to process, reducing cost caused by mass data information mining, utilizing a cluster mode to reduce performance requirements of a server, mining relevant rules from the fault alarm information base and combining the relevant rules with the relevant information to form the knowledge base, predicting the probability of fault occurrence according to relevant faults in the knowledge base and the support degree and the confidence degree of the faults, and rapidly guiding fault processing.
The traditional fault early warning mainly focuses on that when equipment in a network system breaks down, a large amount of meaningless fault alarm information interferes with fault diagnosis, the fault alarm information is input, relevant rules among fault alarm information are mined through a data mining technology, a fault is determined and positioned, and relevant fault probability is predicted.
However, the traditional fault early warning focuses on diagnosis positioning and fault troubleshooting after a fault occurs, early warning cannot be performed, and the fault cannot be avoided, and because the input is fault warning information and is not equipment operation information, the daily monitoring and early warning effect is poor, and the traditional fault early warning belongs to post-troubleshooting.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. Therefore, an object of the present invention is to provide a vehicle early warning method, which can early warn, avoid failure, and improve driving safety of users.
A second object of the present invention is to provide a server.
It is a third object of the invention to propose a non-transitory computer storage medium.
A fourth object of the present invention is to provide a vehicle warning system.
A fifth object of the invention is to propose a vehicle.
In order to achieve the above object, a first aspect of the present invention provides a vehicle warning method, including: acquiring real-time vehicle-mounted data; calculating the matching degree of the vehicle-mounted data and an early warning working condition model; and predicting that the vehicle enters an early warning working condition according to the matching degree, and sending warning information.
According to the vehicle early warning method provided by the embodiment of the invention, based on the pre-established early warning condition model, when the real-time vehicle-mounted data of the vehicle is obtained, the matching degree of the real-time vehicle-mounted data and the corresponding early warning condition model is calculated, the vehicle is predicted to enter the early warning condition model, and the warning information is sent, namely the real-time vehicle-mounted data is dynamically processed, so that the fault monitoring of the vehicle is realized, the purpose of early warning is achieved, the driving safety of a user is improved, compared with the situation that the fault warning information appears, the fault type is determined, the vehicle early warning method in the embodiment predicts that the vehicle enters the early warning condition according to the matching degree, and the warning information is sent, so that the vehicle early warning method belongs to early warning and is safer.
In some embodiments, calculating the matching degree of the vehicle-mounted data and the early warning condition model comprises: carrying out streaming data processing on the vehicle-mounted data; respectively inputting the vehicle-mounted data processed by the stream data into various trained early warning working condition models; obtaining the confidence coefficient, corresponding to the vehicle-mounted data, output by each early warning working condition model; obtaining the confidence coefficient, corresponding to the vehicle-mounted data, output by each early warning working condition model; obtaining a target early warning working condition model with the highest confidence coefficient output; and the highest confidence degree corresponds to the forward classification of the target early warning condition model, and the highest confidence degree is higher than a preset threshold value of the target early warning condition model, and whether the vehicle-mounted data are matched with the early warning condition model or not is judged.
In some embodiments, the early warning condition model includes a vehicle behavior model, and the warning information is sent if the vehicle is predicted to enter the early warning condition according to the matching degree, including: if the vehicle-mounted data is matched with the vehicle using behavior model, determining that the vehicle is not operated properly by the user; generating improper operation information according to the vehicle behavior model matched with the vehicle-mounted data; and sending the improper operation information to warn.
In some embodiments, the vehicle warning method further includes: predicting vehicle faults according to the improper operation information and generating fault early warning information; and sending the vehicle early warning information to carry out fault early warning.
In some embodiments, the early warning condition model includes a fault model, and the warning information is sent if the vehicle is predicted to enter the early warning condition according to the matching degree, including: matching the vehicle-mounted data with the fault model, and predicting that the vehicle will have a fault corresponding to the fault model; generating fault early warning information according to the fault model matched with the vehicle-mounted data; and sending the fault early warning information to carry out fault early warning.
In some embodiments, the vehicle warning method further includes: and if the confidence coefficient is smaller than the preset threshold value, the vehicle-mounted data is stored to be used for updating the early warning working condition model, and the early warning working condition model is perfected by continuously storing the vehicle-mounted data of which the confidence coefficient is smaller than the preset threshold value, so that the early warning accuracy is improved.
In order to achieve the above object, an embodiment of a second aspect of the present invention provides a server, including: at least one processor; a memory communicatively coupled to the at least one processor; wherein the memory stores an early warning condition model and instructions executable by the at least one processor, the instructions when executed by the at least one processor implementing the vehicle early warning method of the above embodiment.
According to the server provided by the embodiment of the invention, the processor executes the vehicle early warning method, so that real-time vehicle-mounted data can be monitored, the running state of a vehicle can be predicted, the purpose of early warning is achieved, and the driving safety of a user is ensured.
In order to achieve the above object, a non-transitory computer storage medium is provided in an embodiment of a third aspect of the present invention, on which a computer program is stored, wherein the computer program is configured to implement the vehicle warning method according to the above embodiment when executed.
In order to achieve the above object, a fourth aspect of the present invention provides a vehicle warning system, including: the acquisition device is used for acquiring vehicle-mounted data; the communication device is connected with the data acquisition device and is used for sending the vehicle-mounted data and receiving warning information; the control device is connected with the communication device and used for generating a warning control signal according to the warning information; and the warning device is connected with the control device and used for warning according to the warning control signal.
According to the vehicle early warning system provided by the embodiment of the invention, the vehicle-mounted data is sent and the warning information is received through the communication device, the vehicle is predicted to enter the early warning working condition model according to the matching degree of the vehicle-mounted data and the early warning working condition model, the control device generates the control signal according to the received warning information to early warn the vehicle, the user is reminded to standardize the locomotive through early warning in time, fault investigation and maintenance are carried out, accidents caused by misoperation are avoided, and the driving safety of the user is ensured.
In order to achieve the above object, an embodiment of a fifth aspect of the invention proposes a vehicle including: the vehicle early warning system comprises a vehicle body, a CAN bus and the vehicle early warning system, wherein the vehicle early warning system is communicated with the CAN bus.
According to the vehicle provided by the embodiment of the invention, when the vehicle early warning system is communicated with the CAN bus, the real-time vehicle-mounted data is monitored by calculating the matching degree of the real-time vehicle-mounted data and the early warning working condition model, so that accidents caused by improper operation of users are avoided, and the driving safety of the users is ensured.
Drawings
The above and/or additional aspects and advantages of the present invention 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 view of a conventional fault early warning according to the related art;
FIG. 2 is a flow diagram of a big data processing procedure according to one embodiment of the invention;
FIG. 3 is a flow chart of early warning condition model building according to one embodiment of the present invention;
FIG. 4 is a flow chart of a vehicle warning method according to one embodiment of the invention;
FIG. 5 is a schematic diagram of a vehicle warning system architecture according to one embodiment of the present invention;
FIG. 6 is a flow chart of a vehicle warning method according to one embodiment of the invention;
FIG. 7 is a block diagram of a server according to one embodiment of the invention;
FIG. 8 is a block diagram of a vehicle warning system according to one embodiment of the present invention;
FIG. 9 is a block diagram of a vehicle according to one embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below, the embodiments described with reference to the drawings being illustrative, and the embodiments of the present invention will be described in detail below.
In the running process of the vehicle, because the early warning and fault troubleshooting cannot be carried out on improper operation, vehicle fault information, maintenance information and the like of a user in time, accidents caused by the improper operation cannot be avoided, the running state of the vehicle is monitored in real time through the vehicle early warning method provided by the embodiment of the invention, and the early warning of the vehicle is necessary.
The vehicle early warning method of the embodiment of the invention realizes the monitoring and prediction functions of vehicle-mounted data based on the pre-established early warning working condition model, wherein the early warning working condition model can comprise a fault model or a vehicle behavior model established based on improper operation or other models which can be used for predicting the bad operation of the vehicle, and the establishment of the early warning working condition model is firstly introduced below.
The early warning working condition model is established based on a big data technology and a cloud computing technology, mass data are stored firstly, data are imported and cleaned, then a machine learning algorithm is used for classifying according to the normal working condition, the abnormal working condition and the abnormal working condition of the vehicle, classification characteristic values are extracted to establish each working condition model, and the running state of the vehicle is simulated. The working condition models comprise working condition behaviors such as idle working conditions, acceleration working conditions, deceleration working conditions, constant speed working conditions, fault recording working conditions, improper operation behaviors, maintenance and the like, wherein the machine learning algorithm comprises a classification tree, a regression tree, K neighbors, a support vector machine, a random forest and the like.
The following describes the data processing and the machine learning algorithm model construction in detail with reference to the drawings.
FIG. 2 is a flow chart of big data processing according to an embodiment of the present invention.
And step S21, data acquisition, wherein the acquired data is imported into the server.
And step S22, data import, correction, duplication removal, completion and other operations are carried out on the collected data.
And step S23, classifying the data, and associating the vehicle operation data with information such as improper operation behaviors, fault records and maintenance, wherein the improper operation behaviors, the fault records and the maintenance information are represented by a series of time sequence data, and the data are classified according to the classification working condition.
And step S24, counting the data, and performing preliminary statistical analysis and arrangement on the classified data.
And step S25, storing the data, and storing the sorted and counted data in the server cluster.
Classifying the data processed by the big data according to working conditions, and carrying out model construction on each working condition by using a machine learning algorithm to establish an early warning working condition model.
The establishment of the early warning condition model is described with reference to fig. 3, which is a flowchart of the establishment of the early warning condition model according to an embodiment of the present invention, as shown in fig. 3.
And step S31, dividing the storage data after being processed and classified into training data and test data.
And step S32, learning the training data by using one or more machine learning algorithms and improved algorithms, generating an early warning working condition model, forming a knowledge base and simulating the running state of the vehicle.
And step S33, measuring the accuracy of the early warning condition model by using the test data in the early warning condition model.
And step S34, selecting the most suitable algorithm for each working condition according to the accuracy, establishing an early warning working condition model, and updating a knowledge base.
The early warning operating mode model can comprise a vehicle using behavior model, a fault model and the like, early warning can be performed on improper operation, fault information, maintenance and the like in the vehicle running process through the early warning operating mode model, a user is reminded of driving according to the standard, troubleshooting and maintenance are performed in time, accidents caused by improper operation of the user are avoided, and driving safety is guaranteed.
Based on the early warning condition model of the above embodiment, a vehicle early warning method according to an embodiment of the first aspect of the present invention is described below with reference to fig. 4 to 6.
As shown in fig. 4, the vehicle warning method according to the embodiment of the present invention at least includes step S1, step S2, and step S3, which are described in detail below.
And step S1, acquiring real-time vehicle-mounted data.
In an embodiment, the real-time on-board data includes vehicle operational data, mishandling behavior, vehicle fault records, maintenance information, and the like. Real-time vehicle-mounted data are collected through a Controller Area Network (CAN) technology of a vehicle-mounted terminal, and data such as improper operation behaviors, vehicle fault records and maintenance are collected through other ways such as background collection of historical data, fault data collected after maintenance and sale, maintenance records and the like.
The vehicle running data comprise running parameters such as vehicle speed, voltage, current, residual capacity, temperature, gear, accelerator depth, brake depth, battery fault state and the like, and alarm state; vehicle fault records such as fault records occurring in the whole vehicle test, fault records reflected when a customer reports and faults discussed on the network and the like; the maintenance information includes, for example, the time of maintenance of the vehicle, the name and number of parts to be replaced during maintenance, a description of a failure, the time of purchase of the vehicle, the mileage, and the time of the last maintenance.
And step S2, calculating the matching degree of the vehicle-mounted data and the early warning working condition model.
In the embodiment, the real-time vehicle-mounted data are uploaded to the server, the early warning working condition model and the real-time monitoring data are compared and matched, the matching degree of the real-time vehicle-mounted data such as the battery loss and the battery fault working condition model is calculated, whether the vehicle-mounted data are matched with the early warning working condition model or not is determined through the matching degree calculation, and therefore whether the vehicle needs to be early warned in advance or not is judged.
And step S3, predicting the vehicle to enter the early warning working condition according to the matching degree, and sending warning information.
In an embodiment, after the matching degree of the vehicle data and the early warning condition model is calculated in step S2, whether the vehicle enters the corresponding early warning condition model such as a battery fault condition model is determined according to the calculation result of the matching degree, and when the vehicle is predicted to enter the battery condition model, a battery fault is about to occur, and an alarm message is sent to the vehicle, and the alarm message can be sent through a vehicle-mounted voice system and is used for reminding a user to perform fault troubleshooting, avoiding an accident, and ensuring driving safety. When the alarm reminding is carried out, different strategies are adopted for different reminding in a grading way, the higher the grade is, the higher the reminding frequency is, and when the reminding frequency is too high, the vehicle control strategy can be properly and automatically changed, so that the driving safety of a user is ensured; the lower the grade, the lower the reminding frequency is, the driving of the user is not influenced, all reminding is finished after the user performs early warning operation or feedback, and the reminding is performed again when new warning information appears again.
As shown in fig. 5, which is a schematic diagram of a vehicle early warning system architecture according to an embodiment of the present invention, a pre-warning operating condition model is pre-established in a server, the establishment of the pre-warning operating condition model is classified according to different operating conditions based on results obtained after big data processing, then a machine learning algorithm is used to perform model establishment on each operating condition, an early warning operating condition model is generated, real-time vehicle-mounted data is monitored, the vehicle-mounted data is matched with the early warning operating condition model, a current operating state of the vehicle is determined, after data analysis is completed, the vehicle-mounted data is fed back to a user through a vehicle-mounted voice system, early warning processing is performed on a vehicle which has improper operation, is about to malfunction, and needs maintenance, the improper operation is identified, an alarm is performed until the operation is standard, and the alarm is stopped.
According to the vehicle early warning method provided by the embodiment of the invention, based on the pre-established early warning condition model, when the real-time vehicle-mounted data of the vehicle is obtained, the matching degree of the real-time vehicle-mounted data and the corresponding early warning condition model is calculated, namely the vehicle is predicted to enter the early warning condition model by performing dynamic processing on the real-time vehicle-mounted data, and alarm information is sent, so that the fault monitoring of the vehicle is realized, the purpose of early warning is achieved, the driving safety of a user is improved, compared with the situation that the fault alarm information appears, the fault type is determined.
In some embodiments, calculating the matching degree of the vehicle-mounted data and the early warning condition model comprises: carrying out stream data processing on the vehicle-mounted data; respectively inputting the vehicle-mounted data processed by the stream data into various trained early warning working condition models; obtaining the confidence coefficient of corresponding vehicle-mounted data output by each early warning condition model; obtaining the confidence coefficient of corresponding vehicle-mounted data output by each early warning condition model; obtaining a target early warning condition model with the highest confidence coefficient output; and determining that the vehicle-mounted data is matched with the pre-warning working condition model if the highest confidence degree corresponds to the forward classification of the target pre-warning working condition model and is higher than a preset threshold value of the target pre-warning working condition model. In the embodiment of the present invention, the early warning condition model is a model capable of predicting a possible bad operation condition of the vehicle, so the forward classification of the target early warning condition model may be a classification determined to belong to the model, for example, for the battery early warning model, the confidence level output by the battery early warning model may include confidence levels corresponding to a high risk, a medium risk and a low risk, where the high risk and the medium risk may correspond to the forward classification of the battery early warning model, that is, a classification determining a positive nature of a risk existing in the battery.
In the embodiment, when the vehicle runs, data stream processing is performed on real-time vehicle-mounted data, that is, vehicle-mounted data uploaded to a server in real time is intercepted at a certain frame frequency, the data subjected to stream processing and an early warning condition model are subjected to cluster classification matching, the matching degree of real-time monitoring data and a corresponding early warning model is calculated, for example, the confidence degrees of the real-time vehicle braking data and the braking early warning condition model are calculated, when the confidence degree of the vehicle braking data is higher than a preset threshold value of the braking early warning condition, the vehicle-mounted data is determined to be matched with the braking early warning condition model, warning information is sent, it is determined that the vehicle is in a braking early warning condition, and when the vehicle is about to fail, real-time running data inside the vehicle can change. The confidence interval is the degree of plausibility of the measured value of the measured variable.
In some embodiments, the early warning condition model includes a vehicle behavior model, and the vehicle is predicted to enter the early warning condition according to the matching degree, and then sending the warning information includes: if the vehicle-mounted data is matched with the vehicle using behavior model, determining that the vehicle is not operated by the user; generating improper operation information according to the vehicle behavior model matched with the vehicle-mounted data; and sending improper operation information to warn.
In an embodiment, the vehicle behavior model CAN be used for standardizing driving, identifying improper operation and the like, acquiring vehicle-mounted data such as that a driver steps on an accelerator or brakes suddenly, matching the acquired vehicle-mounted data with the vehicle behavior model, generating improper operation information according to a matching calculation result, sending the improper operation information by the CAN bus, giving an alarm to the vehicle for reminding, sending the prompt information to a vehicle-mounted voice system of the vehicle, and prompting the vehicle owner of the improper operation.
In some embodiments, the vehicle warning method further comprises: predicting vehicle faults according to the improper operation information and generating fault early warning information; the vehicle early warning information is sent to carry out fault early warning, namely the vehicle behavior model can assist in predicting vehicle faults, for example, if the vehicle owner has bad long-term driving habits and likes to open a violent refueling door and violently step on a brake, the brake system can be more easily damaged due to faults under the bad brake habits, and by reminding a user, troubleshooting and maintenance are carried out in time, and unnecessary accidents are avoided.
In some embodiments, the early warning condition model includes a fault model, and the vehicle is predicted to enter the early warning condition according to the matching degree, and then sending the warning information includes: matching the vehicle-mounted data with the fault model, and predicting that the vehicle will have faults corresponding to the fault model; generating fault early warning information according to the fault model matched with the vehicle-mounted data; and sending fault early warning information to carry out fault early warning.
In the embodiment, vehicle-mounted data such as brake depth data is used for calculating the matching degree of the brake depth data and a brake fault model, if the matching degree is greater than a preset threshold value of a brake fault mode, the brake depth data is determined to be matched with the brake fault, the brake fault of the vehicle is predicted, brake early warning information is generated according to the brake fault model and is sent to the vehicle, brake early warning is carried out in advance, a user is reminded of troubleshooting, and unnecessary accidents are avoided.
In some embodiments, the vehicle early warning method further includes storing the vehicle-mounted data for updating the early warning condition model if the confidence is smaller than the preset threshold, inputting the vehicle-mounted data into the trained early warning condition model, and then obtaining that the confidence of the vehicle-mounted data output by the corresponding condition model is smaller than the preset threshold, for example, if the confidence of the output vehicle-mounted data is 70% and the corresponding preset threshold is 90%, the confidence is lower than the preset threshold, which indicates that the vehicle-mounted data is not matched with the early warning condition model, and then storing the vehicle-mounted data.
The vehicle warning method according to the embodiment of the present invention will be described in detail with reference to fig. 6. Fig. 6 is a flowchart illustrating a vehicle warning method according to an embodiment of the present invention.
In step S41, the processed data is classified into the data subjected to the big data processing.
In step S42, if the training data is obtained, step S44 is performed.
Step S43, the data is tested.
And step S44, utilizing a machine learning algorithm to extract and learn the rules of the training data.
Step S45, a knowledge base is generated, a test is performed using the test data, and the knowledge base is updated.
And step S46, establishing an early warning working condition model.
And step S47, matching the vehicle-mounted data with the early warning working condition model in the real-time vehicle-mounted data transmission process to determine the vehicle running state.
And step S48, alarming and reminding improper operation, fault and maintenance information.
In summary, according to the vehicle early warning method in the embodiment of the present invention, based on the pre-established early warning condition model, when the real-time vehicle-mounted data of the vehicle is obtained, the matching degree between the real-time vehicle-mounted data and the corresponding early warning condition model is calculated, that is, the real-time vehicle-mounted data is dynamically processed to predict that the vehicle enters the early warning condition model, and the warning information is sent, so as to monitor the fault of the vehicle, achieve the purpose of early warning, improve the driving safety of the user, and determine the fault type after the fault warning information appears.
A server according to an embodiment of the second aspect of the present invention is described below with reference to the drawings.
Fig. 7 is a block diagram of a server according to an embodiment of the invention, and as shown in fig. 7, a server 10 according to an embodiment of the invention includes at least one processor 11 and a memory 12 communicatively coupled to the at least one processor.
The memory 12 stores therein an early warning condition model and instructions executable by the at least one processor, and the instructions are executed by the at least one processor 11 to implement the vehicle early warning method according to the above embodiment.
According to the server 10 of the embodiment of the invention, the processor 11 executes the vehicle early warning method mentioned in the above embodiment, so that real-time vehicle-mounted data can be monitored, the running state of the vehicle can be predicted, the purpose of early warning is achieved, and the driving safety of a user is ensured.
A computer-readable storage medium of an embodiment of the third aspect of the present invention has a computer program stored thereon, and the computer program, when executed, implements the vehicle warning method mentioned in the above embodiment.
A vehicle warning system according to a fourth aspect of the present invention will be described with reference to the accompanying drawings.
Fig. 8 is a block diagram of a vehicle early warning system according to an embodiment of the present invention, and as shown in fig. 8, a vehicle early warning system 20 according to an embodiment of the present invention includes a collecting device 21, a communication device 22, a control device 23, and a warning device 24.
The acquisition device 21 is used for acquiring vehicle-mounted data; the communication device 22 is connected with the data acquisition device 21 and is used for sending vehicle-mounted data and receiving warning information; the control device 23 is connected with the communication device 22 and is used for generating an alarm control signal according to the alarm information; the warning device 24 is connected to the control device 23 for warning according to the warning control signal.
According to the vehicle early warning system 20 provided by the embodiment of the invention, the vehicle-mounted data is sent and the warning information is received through the communication device 22, the vehicle is predicted to enter the early warning working condition model according to the matching degree of the vehicle-mounted data and the early warning working condition model, the control device 23 generates a control signal according to the received warning information to carry out vehicle early warning, a user is reminded to standardize the locomotive through early warning in time, troubleshooting and maintenance are carried out, accidents caused by misoperation are avoided, and the driving safety of the user is ensured.
A vehicle according to an embodiment of the fifth aspect of the invention is described below with reference to the drawings.
Fig. 9 is a block diagram of a vehicle according to an embodiment of the present invention, and as shown in fig. 9, a vehicle 30 according to an embodiment of the present invention includes a vehicle body 31, a CAN bus 32, and the vehicle warning system 20 mentioned in the above embodiment, and the vehicle warning system 20 communicates with the CAN bus 32.
According to the vehicle 30 of the embodiment of the invention, when the vehicle early warning system 20 is in communication with the CAN bus 32, the real-time vehicle-mounted data is monitored by calculating the matching degree of the real-time vehicle-mounted data and the early warning condition model, so that accidents caused by improper operation of users are avoided, and the driving safety of the users is ensured.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (10)
1. A vehicle early warning method, comprising:
acquiring real-time vehicle-mounted data;
calculating the matching degree of the vehicle-mounted data and an early warning working condition model;
and predicting that the vehicle enters an early warning working condition according to the matching degree, and sending warning information.
2. The vehicle early warning method according to claim 1, wherein calculating the matching degree of the vehicle-mounted data and the early warning condition model comprises:
carrying out streaming data processing on the vehicle-mounted data;
respectively inputting the vehicle-mounted data processed by the stream data into various trained early warning working condition models;
obtaining the confidence coefficient, corresponding to the vehicle-mounted data, output by each early warning working condition model;
obtaining a target early warning condition model with the highest confidence coefficient output;
and if the highest confidence degree corresponds to the forward classification of the target early warning condition model and the highest confidence degree is higher than a preset threshold value of the target early warning condition model, determining that the vehicle-mounted data is matched with the target early warning condition model.
3. The vehicle early warning method according to claim 1, wherein the early warning condition model comprises a vehicle behavior model, and the warning information is sent when the vehicle is predicted to enter the early warning condition according to the matching degree, and the warning information comprises:
if the vehicle-mounted data is matched with the vehicle using behavior model, determining that the vehicle is not operated by the user;
generating improper operation information according to the vehicle behavior model matched with the vehicle-mounted data;
and sending the improper operation information to warn.
4. The vehicle warning method according to claim 3, further comprising:
predicting vehicle faults according to the improper operation information and generating fault early warning information;
and sending the vehicle early warning information to carry out fault early warning.
5. The vehicle early warning method according to claim 1, wherein the early warning condition model comprises a fault model, and if the vehicle is predicted to enter the early warning condition according to the matching degree, warning information is sent, and the warning information comprises:
matching the vehicle-mounted data with the fault model, and predicting that the vehicle will have a fault corresponding to the fault model;
generating fault early warning information according to the fault model matched with the vehicle-mounted data;
and sending the fault early warning information to carry out fault early warning.
6. The vehicle warning method according to claim 2, further comprising:
and if the confidence coefficient is smaller than the preset threshold value, storing the vehicle-mounted data for updating the early warning working condition model.
7. A server, comprising:
at least one processor;
a memory communicatively coupled to the at least one processor;
wherein the memory has stored therein an early warning condition model and instructions executable by the at least one processor, the instructions when executed by the at least one processor implementing the vehicle early warning method of any one of claims 1-6.
8. A non-transitory computer storage medium having a computer program stored thereon, wherein the computer program when executed implements the vehicle warning method of any one of claims 1-6.
9. A vehicle early warning system, in communication with the server of claim 7, comprising:
the acquisition device is used for acquiring vehicle-mounted data;
the communication device is connected with the data acquisition device and used for sending the vehicle-mounted data and receiving warning information;
the control device is connected with the communication device and used for generating a warning control signal according to the warning information;
and the warning device is connected with the control device and used for warning according to the warning control signal.
10. A vehicle comprising a body, a CAN bus, and the vehicle warning system of claim 9 in communication with the CAN bus.
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