CN113221457A - Method, device, equipment and medium for determining vehicle maintenance information - Google Patents
Method, device, equipment and medium for determining vehicle maintenance information Download PDFInfo
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Abstract
The utility model relates to a method, a device and a medium for determining vehicle maintenance information, which are characterized in that a plurality of working condition characteristic parameter data corresponding to vehicle data are obtained by obtaining the vehicle data according to the vehicle data and a plurality of preset working condition characteristic parameters, the plurality of working condition characteristic parameter data are respectively processed by a state prediction model corresponding to a part to obtain a plurality of state parameter data corresponding to the part, and the maintenance information of the vehicle is determined according to the plurality of state parameter data corresponding to the part, wherein the maintenance information comprises the part to be maintained and corresponding maintenance time; by acquiring the vehicle data in real time and determining the current state of the vehicle part according to the vehicle data, the maintenance information of the vehicle can be determined quickly and accurately in real time, the determination method is more intelligent, the waste of resources can be reduced to the maximum extent, and meanwhile, the safety of the vehicle is enhanced.
Description
Technical Field
The present disclosure relates to the field of vehicle maintenance technologies, and in particular, to a method, an apparatus, a device, and a medium for determining vehicle maintenance information.
Background
With the development of automobile technology, the application of automobiles is increasingly wide, and the automobile provides convenience for users, and meanwhile, the users also need to maintain the parts of the automobile.
At present, most of automobile maintenance adopts a mileage or period fixing mode, but the mileage or period fixing maintenance mode is adopted for users who have better automobile worker conditions at ordinary times, the automobile parts have better states after the maintenance period, maintenance is not needed, forced maintenance wastes resources and time of customers, and for users who have poor automobile conditions, when the maintenance period is not reached, the automobile may have problems and cannot remind the users of maintaining the automobile in time; and remind the user to maintain according to the car state (whether spare part appears unusually promptly), only can point out when the car appears unusually, for the user inconvenience while, the danger coefficient is also bigger.
Disclosure of Invention
In order to solve the technical problems described above or at least partially solve the technical problems, the present disclosure provides a method, an apparatus, a device, and a medium for determining vehicle maintenance information.
In a first aspect, an embodiment of the present disclosure provides a method for determining vehicle maintenance information, including:
acquiring vehicle data;
obtaining a plurality of working condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and a plurality of preset working condition characteristic parameters;
respectively processing the plurality of working condition characteristic parameter data through a state prediction model corresponding to the part to obtain a plurality of state parameter data corresponding to the part;
and determining maintenance information of the vehicle according to a plurality of state parameter data corresponding to the parts, wherein the maintenance information comprises the parts to be maintained and corresponding maintenance time.
Optionally, the method further comprises:
determining a plurality of working condition parameters, wherein the working condition parameters are parameters which can be obtained through calculation based on vehicle data;
and screening a plurality of preset working condition characteristic parameters for predicting the state of the part from the plurality of working condition parameters based on the preset contribution degrees of the plurality of working condition parameters to the state of the part.
Optionally, the plurality of operating condition characteristic parameters include one or more of vehicle environment, vehicle speed, vehicle load, driver driving characteristics, vehicle total mileage, brake duration and brake distance.
Optionally, determining maintenance information of the vehicle according to a plurality of state parameter data corresponding to the component includes:
acquiring a corresponding relation between a plurality of state parameters corresponding to the parts and maintenance time;
and searching the corresponding relation according to a plurality of state parameter data corresponding to the parts, and determining the maintenance information of the vehicle.
Optionally, the plurality of condition parameters includes one or more of a component condition, a component failure condition, a component wear level, performance cracking data, and a component remaining amount.
Optionally, the state prediction model is obtained by training in the following manner:
acquiring sample data of a plurality of working condition characteristic parameters, wherein the sample data is obtained by calculation based on vehicle data generated by the vehicle running in a first preset time period;
acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance based on the vehicle after the first preset time period is finished;
and training a neural network model according to the sample data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts to obtain a state prediction model corresponding to the parts.
Optionally, after training the neural network model, the method further includes:
acquiring verification data of a plurality of working condition characteristic parameters, wherein the verification data is obtained by calculation based on vehicle data generated by the vehicle running in a second preset time period;
acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance based on the vehicle after the second preset time period is finished;
respectively processing the verification data of the plurality of working condition characteristic parameters through the state prediction model corresponding to each part to obtain the prediction data of the plurality of state parameters corresponding to each part;
judging whether the prediction data are matched with the feedback data or not, if so, saving a corresponding state prediction model; and if not, correcting the corresponding state prediction model according to the verification data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts.
Optionally, the method further comprises:
and sending maintenance information to the vehicle so that the vehicle reminds the user of the parts to be maintained and the corresponding maintenance time according to the maintenance information.
In a second aspect, an embodiment of the present disclosure provides a device for determining vehicle maintenance information, including:
the acquisition module is used for acquiring vehicle data;
the extraction module is used for obtaining a plurality of working condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and a plurality of preset working condition characteristic parameters;
the processing module is used for respectively processing the plurality of working condition characteristic parameter data through the state prediction model corresponding to the part to obtain a plurality of state parameter data corresponding to the part;
the determining module is used for determining maintenance information of the vehicle according to a plurality of state parameter data corresponding to the parts, and the maintenance information comprises the parts to be maintained and corresponding maintenance time.
In a third aspect, an embodiment of the present disclosure provides a vehicle maintenance information determination apparatus, including:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method as described above.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method as described above.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the method for determining the vehicle maintenance information, the vehicle data are obtained, the working condition characteristic parameter data corresponding to the vehicle data are obtained according to the vehicle data and the preset working condition characteristic parameters, the working condition characteristic parameter data are respectively processed through the state prediction model corresponding to the part, the state parameter data corresponding to the part are obtained, and the vehicle maintenance information is determined according to the state parameter data corresponding to the part; by acquiring vehicle data in the vehicle running process in real time and determining the state of the current vehicle part according to the vehicle data, the maintenance information of the vehicle for maintenance can be determined quickly and accurately in real time, the determination method is more intelligent, the state of the vehicle can be known comprehensively in time according to the maintenance information, the waste of resources is reduced to the maximum extent, the safety of the vehicle is enhanced, and the user experience is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present disclosure, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic diagram of an application scenario provided in an embodiment of the present disclosure;
FIG. 2 is a flowchart of a method for training a state prediction model according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of a method for training a state prediction model according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of a method for determining vehicle maintenance information according to an embodiment of the present disclosure;
FIG. 5 is a flowchart of a method for determining vehicle maintenance information according to an embodiment of the present disclosure;
FIG. 6 is a flowchart of a method for determining vehicle maintenance information according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram illustrating a structure of a device for determining vehicle maintenance information according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of a vehicle maintenance information determination device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, aspects of the present disclosure will be further described below. It should be noted that the embodiments and features of the embodiments of the present disclosure may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced in other ways than those described herein; it is to be understood that the embodiments disclosed in the specification are only a few embodiments of the present disclosure, and not all embodiments.
In the prior art, maintenance time is mostly set through a fixed mileage interval or a fixed time interval, for example, engine oil, a machine filter, a spark plug, an air filter, an air conditioner filter element, brake fluid, a brake disc, speed reducer oil and the like used on a vehicle, and the maintenance time of the vehicle is set according to a certain mileage interval (for example, 20000km) or a certain time interval (for example, 1 year); and reminding a user of maintaining the abnormal parts according to the vehicle state, namely whether the parts are abnormal or not. However, the maintenance time is set by determining the mileage or time, so that for a user who uses the vehicle under a better working condition at ordinary times, the state of vehicle parts is intact without maintenance after the maintenance time, and resources are wasted if forced maintenance is performed; and the user is reminded of maintenance according to the vehicle state, namely the abnormity of the parts, and the safety of the vehicle and the user is lower. Accordingly, in view of the above problems, the present disclosure provides a method for determining vehicle maintenance information, which is described in detail below with reference to specific embodiments.
Specifically, a method of determining vehicle maintenance information may be performed by a vehicle or a server. Specifically, the vehicle end or the server may determine a plurality of state parameter data corresponding to the component through the state prediction model. The main body of execution of the training method of the state prediction model and the main body of execution of the determination method of the vehicle maintenance information may be the same or different.
For example, in one application scenario, as shown in FIG. 1, the server 12 trains a state prediction model. The vehicle 11 obtains a trained state prediction model from the server 12, the vehicle 11 obtains vehicle data, the vehicle data is processed through the trained state prediction model, and states of parts in the vehicle 11 are determined, in the application scenario, the state prediction model is trained through the server 12 and transmitted to the vehicle end 11, the vehicle end 11 obtains the vehicle data, states of the parts are obtained through the state prediction model, and maintenance information of the vehicle is determined according to the states of the parts.
In another application scenario, the server 12 predicts the model for the state. Further, the server 12 determines the states of the components in the vehicle 11 through the trained state prediction model. The vehicle data received by the server 12 can be acquired by the vehicle 11 and transmitted to the server 12 for processing, the server 12 transmits maintenance information to the vehicle 11, the vehicle 11 reminds a user of maintaining the vehicle through the maintenance information, in this application scenario, the vehicle 11 acquires the vehicle data and transmits the vehicle data to the server 12 for processing, the server 12 transmits the output result, namely, the maintenance information to the vehicle 11, and the vehicle 11 reminds the user of maintaining according to the maintenance information.
In yet another application scenario, the vehicle 11 trains a state prediction model. Further, the vehicle 11 determines the states of the components in the vehicle 11 through the trained state prediction model.
It is understood that the method for determining vehicle maintenance information provided by the embodiments of the present disclosure is not limited to the above-mentioned several possible scenarios.
The following embodiments are further described by taking as an example that the server 12 trains the state prediction model, the vehicle end 11 transmits the vehicle data to the server 12, the state prediction model of the server 12 processes the vehicle data to obtain state parameters corresponding to the components, determines maintenance information of the vehicle 11 according to the state parameters of the components, and sends the maintenance information to an application scenario of the vehicle 11.
Fig. 2 is a flowchart of a method for training a state prediction model according to an embodiment of the present disclosure, where the method for training a state prediction model by a server 12 includes the following steps as shown in fig. 2:
s210, sample data of the working condition characteristic parameters are obtained, and the sample data are obtained through calculation based on vehicle data generated by the vehicle running in a first preset time period.
Optionally, the plurality of operating condition characteristic parameters include one or more of vehicle environment, vehicle speed, vehicle load, driver driving characteristics, vehicle total mileage, brake duration and brake distance.
It can be understood that vehicle data generated by the vehicle in the driving process in the first preset time period is counted, wherein the vehicle data may include but is not limited to vehicle speed distribution, engine speed distribution, generator speed distribution, speed distribution of a speed reducer, engine oil temperature distribution, oil temperature distribution of the speed reducer, air conditioner use frequency and data in the working condition characteristic parameters, and the vehicle data acquired in the first preset time period may be subjected to statistical analysis to obtain sample data of a plurality of working condition characteristic parameters.
Optionally, based on the preset contribution degree of the multiple working condition parameters to the state of the component, multiple working condition characteristic parameters for reflecting the state of the component are screened from the multiple working condition parameters.
Understandably, according to the above-mentioned contribution degree of the plurality of working condition parameters to the performance in the component state, sorting is performed, the vehicle working condition characteristic parameters which have a large influence on the performance in the component state are screened out, that is, the working condition characteristic parameters of the component state can be reflected most, wherein each component has one or more corresponding working condition characteristic parameters, for example, the working condition characteristic parameters which have a large influence on the lubricating oil include: total vehicle range, driver driving characteristics, vehicle speed, and vehicle load.
Understandably, the vehicle data acquired in the first preset time period may be vehicle data generated in the vehicle driving process, such as the vehicle speed, the total vehicle mileage and the like in table 1, wherein the driving characteristics of the driver of the vehicle may refer to the condition that the driver drives the vehicle, and may be divided into two options of violent driving and smooth driving, which represent the condition that the driver drives the vehicle in the first preset time period; the vehicle load can refer to the acting force generated on a building when a vehicle is static or moves on the building, and preferably, the state of the vehicle load can be defined, including the conditions of full load, half load and no load; the braking duration may refer to the time taken by a running vehicle from the start of braking to the complete stop of the vehicle; the braking distance may refer to the distance the vehicle travels from the start of braking to the time of full standstill at a certain speed per hour.
For example, sample data for a plurality of operating condition characteristic parameters may be as shown in Table 1. The sample data calculation mode takes the working condition characteristic parameter vehicle environment of serial number 1 in table 1 as an example, the first preset time period is 10 days, the server receives 10 pieces of vehicle data in total, and statistical analysis is performed on the 10 pieces of vehicle data to obtain a high temperature percentage of 60%, a low temperature percentage of 20% and a normal temperature percentage of 20%.
TABLE 1
S220, obtaining feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance based on the vehicle after the first preset time period is finished;
optionally, the feedback data of the plurality of state parameters corresponding to each component may include one or more of a component state, a component failure state, a component wear degree, a performance cracking data, and a component remaining amount.
It can be understood that after the vehicle is delivered, the user maintains the vehicle in an after-sales department, the after-sales personnel disassemble parts in the vehicle, check and detect the states of the parts, obtain a plurality of state parameters corresponding to the parts after the vehicle runs for a first preset time period, count and analyze the plurality of state parameters corresponding to the detected parts, generate feedback data, upload the feedback data to the after-sales system, and the server 12 obtains the feedback data of the plurality of state parameters corresponding to the parts in the first preset time period from the after-sales system, wherein the feedback data correspond to sample data in the first preset time period, that is, the sample data can indirectly obtain the feedback data, that is, the sample data and the feedback data have a corresponding relationship.
Understandably, the states of the components in the feedback data can represent the conditions of the components in the vehicle, including whether the components are damaged or not; the component fault state can be whether the component has a fault or not, and can comprise two options of yes or no; performance cracking data may indicate that the performance of the component has changed; the remaining amount of the part may refer to the remaining amount of the part having a certain storage amount, such as lubricating oil or the like.
For example, the feedback data of a plurality of state parameters corresponding to each component may be as shown in table 2. Taking number 1 in table 2 as an example, the current states of the components can be represented by good, good and bad, taking the performance cracking data of number 2 as an example, the viscosity of the lubricating oil is reduced by 30%, taking the residual amount in number 5 as an example, the residual amount of the lubricating oil is 90%, the brake disc is not residual amount, and the brake disc is empty.
TABLE 2
| Serial number | Component state parameter | Lubricating oil | Brake disc |
| 1 | State of component | Good effect | Difference (D) |
| 2 | Data on cracking of properties | 30％ | 50％ |
| 3 | Whether or not a part is faulty | Whether or not | Whether or not |
| 4 | Degree of wear of parts | 30％ | 60％ |
| 5 | Residual amount of | 90％ | NA |
| 6 | Whether it needs to be replaced | Whether or not | Is that |
And S230, training a neural network model according to the sample data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts to obtain a state prediction model corresponding to the parts.
Understandably, on the basis of the above S210 and S220, the server 12 trains the neural network model according to sample data of a plurality of working condition characteristic parameters and feedback data of a plurality of state parameters corresponding to each component, wherein the neural network model can be constructed by a convolutional neural network to obtain a state prediction model corresponding to each component, that is, the sample data is used as an input of the neural network model, and the feedback data is used as a label of the neural network model, that is, an output result.
The training method for the state prediction model provided by the embodiment of the disclosure obtains the feedback data of the plurality of state parameters corresponding to each part by obtaining sample data of the plurality of working condition characteristic parameters, wherein the sample data is obtained by calculation based on vehicle data generated by a vehicle running in a first preset time period, the feedback data is obtained by maintaining the vehicle after the first preset time period of the vehicle ends, and the neural network model is trained according to the sample data of the plurality of working condition characteristic parameters and the feedback data of the plurality of state parameters corresponding to each part to obtain the state prediction model corresponding to each part. The method has the advantages that the incidence relation between the state parameters, namely the feedback data, corresponding to each part and the working condition characteristic parameters, namely the sample data is established through training the neural network model, the corresponding part state parameters can be directly obtained through the working condition characteristic parameters, the processing speed is high, and the accuracy is high.
On the basis of the above embodiment, optionally, after training the neural network model and generating the state prediction model, the following steps are further included as shown in fig. 3:
s310, obtaining verification data of the working condition characteristic parameters, wherein the verification data is obtained through calculation based on vehicle data generated by the vehicle running in a second preset time period.
It can be understood that vehicle data generated by the vehicle in the driving process in the second preset time period are counted, wherein the vehicle data may include, but are not limited to, vehicle speed distribution, engine speed distribution, generator speed distribution, speed distribution of a speed reducer, engine oil temperature distribution, oil temperature distribution of the speed reducer, air conditioner use frequency, and data in the working condition characteristic parameters, and the vehicle data acquired in the second preset time period may be subjected to statistical analysis to obtain verification data of a plurality of working condition characteristic parameters.
Optionally, based on the preset contribution degree of the multiple working condition parameters to the state of the component, multiple working condition characteristic parameters for reflecting the state of the component are screened from the multiple working condition parameters.
Understandably, the vehicle working condition characteristic parameters which have great influence on the performance in the state of the parts are screened out according to the classification and the sequencing of the contribution degrees of the working condition parameters to the performance in the state of the parts, namely the working condition characteristic parameters which can reflect the state of the parts most, wherein each part has one or more corresponding working condition characteristic parameters.
It can be understood that the verification data and the sample data may be obtained in the same manner and calculated in different manners, and based on the vehicle data obtained in different time periods, the plurality of working condition characteristic parameters determined according to the vehicle data screening may be different.
And S320, obtaining feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance based on the vehicle after the second preset time period is finished.
It can be understood that after the vehicle is delivered, the user maintains the vehicle in an after-sales department, the after-sales personnel disassemble each part in the vehicle, check and detect the state of the part, obtain a plurality of state parameters corresponding to each part after the vehicle is loaded for a second preset time period, count and analyze the plurality of state parameters corresponding to each detected part, generate feedback data, upload the feedback data to the after-sales system, and the server 12 acquires the feedback data of the plurality of state parameters corresponding to each part from the after-sales system, wherein the feedback data corresponds to sample data in the second preset time period, that is, the feedback data can be indirectly obtained from the sample data, and the sample data and the feedback data have a corresponding relationship.
And S330, respectively processing the verification data of the plurality of working condition characteristic parameters through the state prediction models corresponding to the parts to obtain prediction data of the plurality of state parameters corresponding to the parts.
It can be understood that, on the basis of the above S310, the trained state prediction model identifies the verification data of the plurality of operating condition characteristic parameters, and outputs the prediction data of the plurality of state parameters corresponding to each component.
S340, judging whether the prediction data are matched with the feedback data, and if so, saving a corresponding state prediction model; and if not, correcting the corresponding state prediction model according to the verification data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts.
Understandably, on the basis of the above S320 and S330, it is determined whether the prediction data obtained by the trained state prediction model is matched with the feedback data, that is, whether the prediction data is the same as the verification data within a certain range, if so, the currently tested state prediction model is stored, and the matching of the prediction data with the feedback data also indicates that the accuracy of the currently trained state prediction model is higher; if not, training the currently tested state prediction model according to the verification data and the feedback data obtained in the second preset time period, and correcting the state prediction model for testing.
Understandably, the current state prediction model is corrected in time according to the acquired verification data and feedback data of different preset time periods, so that the accuracy of the state prediction model is improved.
The training method of the state prediction model provided by the embodiment of the disclosure obtains verification data of a plurality of working condition characteristic parameters by obtaining the verification data calculated based on vehicle data generated by a vehicle running in a second preset time period, obtains feedback data of the plurality of state parameters corresponding to each part, obtains the feedback data by maintaining the vehicle after the second preset time period, respectively processes the verification data of the plurality of working condition characteristic parameters through the state prediction model corresponding to each part, obtains prediction data of the plurality of state parameters corresponding to each part, judges whether the prediction data is matched with the feedback data, and if so, saves the corresponding state prediction model; and if not, correcting the corresponding state prediction model according to the verification data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts. By correcting the trained state prediction model, the accuracy of the state prediction model is improved, the universality of the state prediction model is enhanced, and the processing efficiency is improved.
On the basis of the foregoing embodiment, fig. 4 is a flowchart of a method for determining vehicle maintenance information according to an embodiment of the present disclosure, and processing vehicle data by using a trained state prediction model in the foregoing embodiment includes the following steps shown in fig. 4:
and S410, acquiring vehicle data.
Understandably, the server 12 receives vehicle data transmitted by the vehicle 11 in real time. During use, the vehicle may generate a large amount of vehicle data, wherein the vehicle data may include one or more of the operating condition parameters described above.
It is appreciated that the operating condition parameters may include, but are not limited to, one or more of vehicle speed profile, engine speed profile, generator speed profile, retarder speed profile, oil temperature profile, retarder oil temperature profile, air conditioner usage frequency.
And S420, obtaining a plurality of working condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and the plurality of preset working condition characteristic parameters.
Understandably, on the basis of the above S410, the server 12 performs statistical analysis on the acquired vehicle data to obtain a plurality of operating condition characteristic parameter data corresponding to the vehicle data, where the operating condition characteristic parameter data is included in the vehicle data, that is, the operating condition parameters, and the number of the preset operating condition characteristic parameters corresponds to the number of the operating condition characteristic parameter data.
And S430, respectively processing the plurality of working condition characteristic parameter data through the state prediction models corresponding to the parts to obtain a plurality of state parameter data corresponding to the parts.
Understandably, on the basis of the above S420, the trained state prediction model is used to process and identify the plurality of condition characteristic parameter data to obtain a plurality of state parameter data corresponding to the component.
And S440, determining maintenance information of the vehicle according to the plurality of state parameter data corresponding to the parts, wherein the maintenance information comprises the parts to be maintained and corresponding maintenance time.
It is understood that, based on the above S430, maintenance information of the vehicle that transmits the vehicle data to the server is determined based on the plurality of state parameter data corresponding to the component obtained by the state prediction model.
The method for determining vehicle maintenance information includes obtaining vehicle data, obtaining a plurality of working condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and a plurality of preset working condition characteristic parameters, respectively processing the plurality of working condition characteristic parameter data through a state prediction model corresponding to a part to obtain a plurality of state parameter data corresponding to the part, and determining maintenance information of a vehicle according to the plurality of state parameter data corresponding to the part, wherein the maintenance information includes one or more parts to be maintained and corresponding maintenance time; by acquiring the vehicle data in the vehicle running process in real time and determining the states of all parts of the current vehicle according to the vehicle data, the maintenance information for maintaining the vehicle can be determined quickly and accurately in real time, the determination method is more intelligent, the states of the vehicle can be known timely and comprehensively according to the maintenance information, the waste of resources is reduced to the maximum extent, the safety of the vehicle is enhanced, and the user experience is improved.
On the basis of the foregoing embodiment, optionally, the processing of the vehicle data acquired in S410 further includes the following steps as shown in fig. 5:
s510, determining a plurality of working condition parameters, wherein the working condition parameters are parameters which can be obtained through calculation based on vehicle data.
Understandably, the vehicle data transmitted to the server by the vehicle can be acquired, the vehicle data in the first preset time can be counted, the acquired vehicle data can be processed in the modes of accumulative summation calculation, proportion calculation, difference value or mean value taking and the like, and a plurality of working condition parameters can be acquired by counting and calculating data of a plurality of working conditions in the vehicle data.
S520, screening a plurality of preset working condition characteristic parameters for predicting the state of the part from the plurality of working condition parameters based on the preset contribution degrees of the plurality of working condition parameters to the state of the part.
Understandably, on the basis of the above S510, the vehicle operating condition characteristic parameters that greatly affect the performance in the component state are sorted according to the contribution degrees of the plurality of operating condition parameters to the performance in the component state, that is, the operating condition characteristic parameters that can reflect the component state most are selected as the preset operating condition characteristic parameters, wherein each component has one or more corresponding operating condition characteristic parameters, for example, by taking serial number 1 lubricating oil in table 1 as an example, the operating condition characteristic parameters that greatly affect the lubricating oil in the vehicle driving process include: the total vehicle mileage, the driving characteristics of the driver, the vehicle speed and the vehicle load, and the condition characteristic parameters having a large influence on the lubricating oil may cause corresponding changes in the state parameters of the lubricating oil, for example, the duty ratio of the driving characteristics of the driver in the condition parameters during heavy driving is high, which may cause performance cracking data of the lubricating oil, i.e., viscosity reduction of the lubricating oil, and increase of the wear degree of the brake disc, etc., that is, according to the determined condition characteristic parameters having a large influence on the lubricating oil, the current state parameter data of the lubricating oil may be obtained through the state prediction model.
According to the method for determining the vehicle maintenance information, provided by the embodiment of the disclosure, a plurality of working condition parameters are determined, the working condition parameters are parameters which can be obtained through calculation based on vehicle data, and a plurality of preset working condition characteristic parameters used for predicting the state of the part are screened out from the working condition parameters based on the preset contribution degree of the working condition parameters to the state of the part. The acquired vehicle data are subjected to preliminary statistics and processing, a plurality of preset working condition characteristic parameter data which have large influence on the contribution degree of the parts are obtained through screening, the data volume can be reduced, the characteristics of the vehicle data are saved to the maximum extent, and the accurate prediction of the states of the parts can be ensured while the processing speed of a state prediction model is improved.
On the basis of the above embodiment, optionally, determining the maintenance information of the vehicle according to the plurality of state parameter data corresponding to the component, further includes the following steps as shown in fig. 6:
s610, acquiring the corresponding relation between a plurality of state parameters corresponding to the parts and the maintenance time.
It can be understood that the corresponding relationship between the plurality of state parameters and the maintenance time corresponding to each component is established according to the feedback data corresponding to the component obtained in the first preset time period and the second preset time period, that is, the historical vehicle data obtained during the vehicle running process and the component state parameters obtained by detecting the component state after each preset time period, that is, the maintenance time of the component is determined according to the state of each component, wherein the maintenance time may be a reasonable time range or an accurate time period, and the determined corresponding relationship is stored.
For example, taking the state parameters of the components in table 1 as an example, if the performance cracking data of the lubricating oil is reduced by 30%, the maintenance time of the lubricating oil can be determined to be between 30 days and 40 days, and if the remaining amount of the lubricating oil is 90%, the corresponding maintenance time is correspondingly increased; if the wear degree of the parts of the brake disc reaches 60%, the corresponding maintenance time of the brake disc may be between 20 days and 30 days, and the corresponding relationship may be determined according to the historical operating condition data of the vehicle actually used by the user and the historical states of the parts, which is not limited herein.
S620, searching a corresponding relation according to the plurality of state parameter data corresponding to the parts, and determining the maintenance information of the vehicle.
Understandably, on the basis of the above S610, a corresponding relationship is searched according to a plurality of state parameter data corresponding to each component, different state parameters of each component correspond to different maintenance times respectively, and maintenance information for the current vehicle is determined according to the corresponding relationship, where the maintenance information includes one or more components to be maintained and the maintenance time corresponding to the components.
Optionally, the method for determining vehicle maintenance information further includes: and sending maintenance information to the vehicle so that the vehicle reminds the user of the parts to be maintained and the corresponding maintenance time according to the maintenance information.
It can be understood that, on the basis of the above S620, the server 12 sends the determined maintenance information to the control device of the vehicle 11, the vehicle 11 is a vehicle that sends vehicle data to the server 12, and the control device of the vehicle 11 can directly remind the user of the state and maintenance time of each component according to the maintenance information.
According to the method for determining the vehicle maintenance information, the corresponding relation between the plurality of state parameters corresponding to each part and the maintenance time is obtained, the corresponding relation is searched according to the plurality of state parameter data corresponding to each part, and the vehicle maintenance information is determined, wherein the maintenance information comprises one or more parts to be maintained and the corresponding maintenance time. The use relationship is determined according to the historical part state of the vehicle and the maintenance time, the corresponding relationship is searched according to a plurality of state parameter data corresponding to all parts in the current vehicle, the maintenance time corresponding to all parts can be determined quickly, the processing speed is high, and the accuracy of the determined maintenance information is high.
Fig. 7 is a schematic structural diagram of a device for determining vehicle maintenance information according to an embodiment of the present disclosure. A vehicle maintenance information determination device according to an embodiment of the present disclosure may execute a process flow according to an embodiment of a vehicle maintenance information determination method, and as shown in fig. 7, a vehicle maintenance information determination device 700 includes:
an acquisition module 710 for acquiring vehicle data;
the extracting module 720 is configured to obtain a plurality of operating condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and the plurality of preset operating condition characteristic parameters;
the processing module 730 is configured to process the multiple pieces of operating condition characteristic parameter data through the state prediction models corresponding to the components, respectively, to obtain multiple pieces of state parameter data corresponding to the components;
the determining module 740 is configured to determine maintenance information of the vehicle according to a plurality of state parameter data corresponding to the component, where the maintenance information includes the component to be maintained and a corresponding maintenance time;
the sending module 750 is configured to send maintenance information to the vehicle, so that the vehicle reminds a user of parts to be maintained and maintenance time according to the maintenance information.
Optionally, the device 700 for determining vehicle maintenance information further includes: the characteristic module is used for determining a plurality of working condition parameters, and the working condition parameters are parameters which can be obtained through calculation based on vehicle data; and screening a plurality of preset working condition characteristic parameters for predicting the state of the part from the plurality of working condition parameters based on the preset contribution degrees of the plurality of working condition parameters to the state of the part.
Optionally, the device 700 for determining vehicle maintenance information further includes: the training module is used for acquiring sample data of a plurality of working condition characteristic parameters, and the sample data is obtained by calculation based on vehicle data generated by the vehicle running in a first preset time period; acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance based on the vehicle after the first preset time period is finished; and training a neural network model according to the sample data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts to obtain a state prediction model corresponding to the parts.
Optionally, the plurality of operating condition characteristic parameters in the extracting module 720 include one or more of vehicle environment, vehicle speed, vehicle load, driver driving characteristics, total vehicle mileage, braking duration, and braking distance.
Optionally, the determining module 740 determines the maintenance information of the vehicle according to a plurality of state parameter data corresponding to the component, and is specifically configured to: acquiring a corresponding relation between a plurality of state parameters corresponding to the parts and maintenance time; and searching the corresponding relation according to a plurality of state parameter data corresponding to the parts, and determining the maintenance information of the vehicle.
Optionally, the plurality of condition parameters in the determination module 740 includes one or more of a component condition, a component failure condition, a component wear level, performance cracking data, and a component remaining amount.
Optionally, after the neural network model is trained in the training module, the method is specifically configured to: acquiring verification data of a plurality of working condition characteristic parameters, wherein the verification data is obtained by calculation based on vehicle data generated by the vehicle running in a second preset time period; acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance based on the vehicle after the second preset time period is finished; respectively processing the verification data of the plurality of working condition characteristic parameters through the state prediction model corresponding to each part to obtain the prediction data of the plurality of state parameters corresponding to each part; judging whether the prediction data are matched with the feedback data or not, if so, saving a corresponding state prediction model; and if not, correcting the corresponding state prediction model according to the verification data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts.
The vehicle maintenance information determination apparatus according to the embodiment shown in fig. 7 may be used to implement the technical solutions of the above method embodiments, and the implementation principles and technical effects are similar, and are not described herein again.
Fig. 8 is a schematic structural diagram of a vehicle maintenance information determination apparatus according to an embodiment of the present disclosure. The vehicle maintenance information determination device may be a server or a vehicle as described above. The vehicle maintenance information determination apparatus provided by the embodiment of the present disclosure may execute the processing flow provided by the embodiment of the vehicle maintenance information determination method, and as shown in fig. 8, the vehicle maintenance information determination apparatus 800 includes: a processor 810, a communication interface 820, and a memory 830; wherein the computer program is stored in the memory 830 and configured to be executed by the processor 810 for the determination of vehicle maintenance information as described above.
In addition, the disclosed embodiment also provides a computer readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method for determining vehicle maintenance information of the above embodiment.
Furthermore, the disclosed embodiments also provide a computer program product comprising a computer program or instructions which, when executed by a processor, implement the method of determining vehicle maintenance information as above.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present disclosure, which enable those skilled in the art to understand or practice the present disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (15)
1. A method for determining vehicle maintenance information, characterized in that a state prediction model corresponding to a component of a vehicle is established in advance for the component, the method comprising:
acquiring vehicle data;
obtaining a plurality of working condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and a plurality of preset working condition characteristic parameters;
respectively processing the plurality of working condition characteristic parameter data through a state prediction model corresponding to the part to obtain a plurality of state parameter data corresponding to the part;
and determining maintenance information of the vehicle according to a plurality of state parameter data corresponding to the parts, wherein the maintenance information comprises the parts to be maintained and corresponding maintenance time.
2. The method according to claim 1, wherein the plurality of preset operating condition characteristic parameters are determined by:
determining a plurality of working condition parameters, wherein the working condition parameters can be calculated based on vehicle data;
and screening a plurality of preset working condition characteristic parameters for predicting the state of the part from the plurality of working condition parameters based on the preset contribution degrees of the plurality of working condition parameters to the state of the part.
3. The method of claim 2, wherein the plurality of operating condition characteristic parameters include one or more of vehicle environment, vehicle speed, vehicle load, driver driving characteristics, vehicle mileage, brake duration, brake distance.
4. The method of claim 1, wherein determining the vehicle maintenance information based on the plurality of state parameter data corresponding to the component comprises:
acquiring a corresponding relation between a plurality of state parameters corresponding to the parts and maintenance time;
and searching the corresponding relation according to the plurality of state parameter data corresponding to the parts, and determining the maintenance information of the vehicle.
5. The method of claim 4, wherein the plurality of condition parameters include one or more of a component condition, a component failure condition, a component wear level, performance cracking data, a component remaining amount.
6. The method of claim 1, wherein the state prediction model is trained by:
obtaining sample data of a plurality of working condition characteristic parameters, wherein the sample data is obtained by calculation based on vehicle data generated by the vehicle running in a first preset time period;
acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance after the first preset time period of the vehicle is ended;
and training a neural network model according to the sample data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts to obtain a state prediction model corresponding to the parts.
7. The method of claim 6, wherein after the training of the neural network model, the method further comprises:
acquiring verification data of a plurality of working condition characteristic parameters, wherein the verification data is obtained by calculation based on vehicle data generated by the vehicle running in a second preset time period;
acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained based on vehicle maintenance after the second preset time period of the vehicle is ended;
respectively processing the verification data of the plurality of working condition characteristic parameters through the state prediction model corresponding to each part to obtain prediction data of a plurality of state parameters corresponding to each part;
judging whether the prediction data are matched with the feedback data or not, if so, saving a corresponding state prediction model; and if not, correcting the corresponding state prediction model according to the verification data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts.
8. The method according to any one of claims 1-7, further comprising:
and sending the maintenance information to the vehicle so that the vehicle reminds a user of parts to be maintained and corresponding maintenance time according to the maintenance information.
9. A vehicle maintenance information determination device, comprising:
the acquisition module is used for acquiring vehicle data;
the extraction module is used for obtaining a plurality of working condition characteristic parameter data corresponding to the vehicle data according to the vehicle data and a plurality of preset working condition characteristic parameters, wherein the number of the preset working condition characteristic parameters corresponds to the number of the working condition characteristic parameter data;
the processing module is used for respectively processing the plurality of working condition characteristic parameter data through the state prediction models corresponding to the parts to obtain a plurality of state parameter data corresponding to the parts;
the determining module is used for determining maintenance information of the vehicle according to a plurality of state parameter data corresponding to the parts, and the maintenance information comprises the parts to be maintained and corresponding maintenance time.
10. The apparatus of claim 9, further comprising:
and the sending module is used for sending the maintenance information to the vehicle so that the vehicle reminds a user of parts to be maintained and corresponding maintenance time according to the maintenance information.
11. The apparatus of claim 9, further comprising:
the characteristic module is used for determining a plurality of working condition parameters, and the working condition parameters are parameters which can be obtained through calculation based on vehicle data; and screening a plurality of preset working condition characteristic parameters for predicting the state of the part from the plurality of working condition parameters based on the preset contribution degrees of the plurality of working condition parameters to the state of the part.
12. The apparatus of claim 9, further comprising:
the training module is used for acquiring sample data of a plurality of working condition characteristic parameters, and the sample data is obtained by calculation based on vehicle data generated by the vehicle running in a first preset time period; acquiring feedback data of a plurality of state parameters corresponding to each part, wherein the feedback data is obtained by vehicle maintenance after the first preset time period of the vehicle is ended; and training a neural network model according to the sample data of the working condition characteristic parameters and the feedback data of the state parameters corresponding to the parts to obtain a state prediction model corresponding to the parts.
13. The apparatus of claim 9, wherein the determining module is specifically configured to: acquiring a corresponding relation between a plurality of state parameters corresponding to the parts and maintenance time; and searching the corresponding relation according to the plurality of state parameter data corresponding to the parts, and determining the maintenance information of the vehicle.
14. A vehicle maintenance information determination apparatus, characterized by comprising:
a memory;
a processor; and
a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-8.
15. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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| CN202110520811.4A CN113221457B (en) | 2021-05-13 | 2021-05-13 | Method, device, equipment and medium for determining vehicle maintenance information |
| PCT/CN2022/076917 WO2022237266A1 (en) | 2021-05-13 | 2022-02-18 | Method, apparatus and device for determining vehicle maintenance information, and medium |
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