CN111191400A - Vehicle part service life prediction method and system based on user fault reporting data - Google Patents
Vehicle part service life prediction method and system based on user fault reporting data Download PDFInfo
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Abstract
A vehicle part service life prediction method and system based on user fault reporting data comprises the following steps: establishing a life estimation model; extracting vehicle information of a vehicle to be evaluated in operation from a server; the service life estimation model estimates the service life of each part of the current vehicle to be estimated according to the extracted vehicle information to obtain the estimated service life of each part, and then compares the estimated service life with a preset part service life threshold value to judge whether the current vehicle to be estimated contains the part of which the estimated service life reaches the service life threshold value; and if so, outputting the first result information to the server. The invention can predict the service life of the vehicle parts registered in the server on line, thereby forecasting the vehicle parts before the vehicle parts are formally failed and informing relevant operation and maintenance personnel to process, thereby reducing the workload of the operation and maintenance personnel, improving the working efficiency of the operation and maintenance personnel and avoiding the influence on the experience of users caused by the mixed use of the failed vehicles.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of Internet of things, in particular to a vehicle part service life prediction method and system based on user fault reporting data.
[ background of the invention ]
With the rise of shared bicycles, shared mopeds and shared electric vehicles, the travel of people becomes more convenient. However, as the usage of the shared vehicle increases and the time elapses, the parts of the vehicle may gradually age and deteriorate, which may affect the user experience and even may be discarded. For a vehicle with a fault, the vehicle can be processed only by actively reporting when the user uses the vehicle and then informing operation and maintenance personnel to confirm on site or by a method of street scanning of the operation and maintenance personnel. This type of processing is inefficient, failing to process in time failed vehicles, and failing to find in time vehicles that are about to fail. If the fault vehicle is not recovered or processed in time, the experience of the user is greatly influenced when the fault vehicle and the normal vehicle are jointly used.
In addition, when a user declares a vehicle fault, false fault reporting often exists, which can greatly increase the workload of operation and maintenance personnel and reduce the actual working efficiency of the operation and maintenance personnel.
Therefore, for the shared vehicle, if the vehicle fault can be found in time and the service life of the vehicle can be estimated, operation and maintenance personnel can conveniently and timely process the vehicle to be in fault, the problem that the fault vehicle is mixed in for use is reduced, and a series of problems caused by the fault vehicle are avoided.
[ summary of the invention ]
The invention aims to solve the problems and provides a vehicle part service life prediction method and system based on user fault reporting data.
In order to solve the problems, the invention provides a vehicle part service life prediction method based on user fault reporting data, which comprises the following steps:
establishing a life estimation model;
deploying the life estimation model in a server;
extracting vehicle information of a vehicle to be evaluated in operation from the server;
the service life estimation model estimates the service life of each part of the current vehicle to be estimated according to the extracted vehicle information to obtain the estimated service life of each part;
the service life prediction model compares the predicted service life with a preset component service life threshold value, and judges whether the current vehicle to be evaluated contains components of which the predicted service life reaches the service life threshold value;
if yes, the service life prediction model outputs first result information.
Further, the service life estimation model compares the estimated service life with a preset component service life threshold value, when whether the current vehicle to be estimated contains a component of which the estimated service life reaches the service life threshold value is judged, if not, vehicle information of other vehicles to be estimated in operation is extracted from the server, and the service life of each component of the current vehicle to be estimated is estimated again by the service life estimation model according to the extracted vehicle information.
Further, when the life estimation model is established, the method comprises the following steps:
extracting an error reporting picture which is submitted when a user reports an error and reports a vehicle fault from the server;
automatically detecting the fault position of the vehicle fault part in the fault reporting picture by using a vehicle fault detection model;
extracting vehicle life related information of a vehicle to which the fault part belongs from the server;
establishing a life estimation training set according to the fault part, the fault position and the vehicle life related information;
and performing model training on a machine learning model by using the life estimation training set, and establishing the life estimation model.
Further, the vehicle information includes one or more of a vehicle failure location, a failure location of the failure location, and the vehicle life related information; the vehicle life related information comprises vehicle operation input date information, reported fault date information and reported fault city information.
Further, when a vehicle fault detection model is used for automatically detecting a fault position of a vehicle fault part in the fault report picture, the method comprises the following steps:
inputting the fault reporting picture into the vehicle fault detection model;
the vehicle fault detection model automatically detects whether the vehicle in the fault report picture contains a fault part;
and if so, the vehicle fault detection model detects the fault position of the fault part.
If not, extracting the fault reporting picture which is submitted when the user reports the fault and reports the vehicle fault from the server again.
Further, the vehicle fault detection model is trained by the following steps:
extracting fault reporting pictures which relate to faults of all parts of the vehicle and are submitted when a user reports faults from the server;
manually marking the vehicle fault part in the fault reporting picture;
establishing a fault detection training set according to the manually marked fault reporting picture;
and performing model training on a target detection model based on deep learning by using the fault detection training set, and establishing the vehicle fault detection model.
Further, the vehicle fault detection model is deployed in the server; the fault reporting pictures comprise a guarantee picture of a real fault and a fault reporting picture in a false fault reporting process.
Further, after the life estimation model outputs first result information, the life estimation model informs a vehicle operation and maintenance department of the first result information; the first result information is result information reporting that the estimated life of the part of the vehicle to be evaluated reaches a life threshold.
Further, the first result information comprises vehicle information of the vehicle to be evaluated, the estimated life of the vehicle component to be evaluated and evaluation result information.
In addition, the invention also provides a vehicle part service life prediction system based on user fault reporting data, which comprises a server, a vehicle information acquisition module and a service life prediction model, wherein the server is provided with vehicle information of all vehicles in operation and not put into operation and can be used for receiving and storing fault reporting pictures which are submitted by users to report vehicle faults when the users report faults; the vehicle information acquisition module is used for extracting the vehicle information of the vehicle to be evaluated from the server; the service life prediction model is deployed in the server and used for predicting the service life of each part of the vehicle to be evaluated according to the vehicle information to obtain the predicted service life of each part; and the service life estimation module is used for comparing the estimated service life with a preset service life threshold of the part and judging whether the vehicle to be evaluated contains the part of which the estimated service life reaches the service life threshold.
Further, the service life prediction model is also used for outputting first result information to the server when the service life prediction model judges that the vehicle to be evaluated contains parts of which the predicted service life reaches a service life threshold value; the server is also used for receiving and storing the first result information.
The system further comprises an error reporting picture acquisition module and a vehicle fault detection model, wherein the error reporting picture acquisition module is used for extracting an error reporting picture which is submitted by a user when a fault is reported from the server and reports the vehicle fault; the vehicle fault detection model is deployed in the server and used for automatically detecting fault positions of vehicle fault parts in the fault report picture.
According to the vehicle part service life prediction method and system based on the user fault report data, the service life prediction model is deployed in the server, the service life of each part of the vehicle to be estimated is predicted by the service life prediction model, so that the service life of the part of the vehicle registered in the server can be automatically predicted on line, whether the part of the vehicle reaches the service life limit or not can be automatically judged, the vehicle part can be predicted before the part of the vehicle is formally in fault, and related operation and maintenance personnel are informed to process the part of the vehicle, so that the workload of the operation and maintenance personnel can be reduced, the working efficiency of the operation and maintenance personnel is improved, and the influence on the experience of the user due to the fact that the fault vehicle is mixed in for use is avoided.
[ description of the drawings ]
FIG. 1 is a flow chart of a method for predicting the life of a vehicle component based on user fault reporting data.
Fig. 2 is a schematic flow chart of establishing a life prediction model.
FIG. 3 is another schematic flow chart of the method for establishing a life prediction model.
Fig. 4 is a schematic flow chart illustrating the process of automatically detecting the fault location of the vehicle fault location in the fault-reporting picture by using the vehicle fault detection model.
FIG. 5 is a schematic flow chart of a method for establishing a vehicle fault detection model.
Fig. 6 is a diagram illustrating classification of failure reporting pictures.
Fig. 7 is a schematic diagram of vehicle information.
FIG. 8 is a schematic diagram of a part of the structure of a system for predicting the life of a vehicle component based on user failure data.
Fig. 9 is a schematic diagram illustrating a principle of establishing a life prediction model.
[ detailed description ] embodiments
The following examples are further illustrative and supplementary to the present invention and do not limit the present invention in any way.
Example 1
As shown in fig. 1, the present embodiment provides a method for predicting the life of a vehicle component based on user fault reporting data, which includes the following steps:
s10, establishing a life prediction model:
the purpose of establishing the service life prediction model is to predict the service life of each part of the vehicle, so as to judge whether the vehicle comprises the part of which the predicted service life reaches the service life threshold value, thereby conveniently notifying operation and maintenance personnel to process in time, improving the working efficiency of the operation and maintenance personnel, and avoiding the influence on user experience caused by the mixed use of a fault vehicle.
The life prediction model is formed by training according to the existing fault reporting picture and the related information of the vehicle. In this embodiment, as shown in fig. 2 and 3, the method for establishing the life estimation model is as follows:
s101, extracting an error reporting picture which is submitted when the user reports the error and reports the vehicle fault from the server.
The failure reporting picture is a picture which is submitted to a server to report vehicle failure when a user finds that the vehicle failure actively reports the failure in the actual use process. As shown in fig. 6, the failure reporting pictures include a picture of a vehicle failure with a real failure and a picture of a vehicle in a false failure reporting. In this step, the failure reporting picture extracted from the server may be a vehicle failure picture in real failure or a vehicle picture in false failure reporting.
S102, automatically detecting the fault position of the vehicle fault part in the fault report picture by using a vehicle fault detection model. When the life estimation model is trained, model training needs to be performed according to vehicle fault information of a real fault, and the fault reporting picture extracted in step S101 may be a false fault reporting vehicle picture, so that before model training, the real fault reporting picture needs to be selected, the false fault reporting picture needs to be removed, a fault position where a fault part occurs is detected from the true fault reporting picture, and useful data is selected to be used as a data set during subsequent model training. In the step, the fault reporting picture and the fault position are screened automatically through a vehicle fault detection model. As shown in fig. 4, in step S102, the automatic detection of the failure report picture by the vehicle failure detection model specifically includes the following steps:
and S1021, inputting the fault reporting picture into the vehicle fault detection model. In this step, the fault reporting pictures including the real fault and the false fault extracted in step S101 are input into the vehicle fault detection model.
S1022, the vehicle fault detection model automatically detects whether the vehicle in the fault report picture contains a fault part; the vehicle fault detection model is formed by training a target detection model based on deep learning and can carry out target detection on a fault part and a fault position of a vehicle, so that after an error reporting picture is input into the vehicle fault detection model, the vehicle fault detection model can automatically identify whether the fault part is contained in the error reporting picture.
If the fault part is not included, the current detected fault reporting picture is a false fault reporting vehicle picture which is an useless fault reporting picture for model training, so that the fault reporting picture does not need to be processed. When the vehicle fault detection model detects that the fault reporting picture does not contain a fault part, returning to the step S101: and extracting an error reporting picture which is submitted by the user when the user reports the error and reports the vehicle fault from the server so as to extract a new error reporting picture again to continue fault part and fault position detection.
If the fault part is included, the current detected fault reporting picture is the fault reporting picture of the real fault, the vehicle fault detection model automatically detects the specific fault position of the fault part in the fault reporting picture, and outputs the fault position information of the vehicle to which the fault reporting picture belongs. And after the vehicle fault detection model outputs fault position information, storing the fault position information and the fault position information in the server. Thereafter, the process may proceed to step S101 or step S103 as needed.
S103, extracting vehicle life related information of the vehicle to which the fault part belongs from the server; the vehicle life related information includes, but is not limited to, vehicle operation input date information, reporting fault date information, and reporting fault city information. The time interval between the reported failure date and the vehicle operation input date is directly related to the service life of the vehicle part and can be used as one of the parameters for estimating the service life of the vehicle part; different climates and different using habits of different cities also have certain influence on the service life of the vehicle, so that the city information of the vehicle can be used as one of the parameters for estimating the service life of the vehicle. In other embodiments, suitable data may be selected as the vehicle life related information according to actual needs.
The step S102 may obtain the failure location information of the failure location of the vehicle, and the step S103 may obtain the vehicle life related information of the vehicle to which the failure location belongs. When building the life estimation model, it is necessary to first obtain enough training data to build the life estimation training set, where the data includes the fault location information and/or fault position information obtained in step S102, and also includes the vehicle life related information extracted in step S103, so in different embodiments, different strategies may be applied to collect enough data to build the life estimation training set.
In some embodiments, as shown in fig. 2, a sufficient amount of vehicle failure location information and failure position information may be collected, and then vehicle life related information of a vehicle to which the failure location belongs may be extracted once through step S103; in this case, after step S102, if the data amount of the vehicle failure location information and the failure position information does not reach the predetermined number, for example, 300 pieces of data, the process returns to step S101, and the process loops through step S101 and step S102 until the failure location information and the failure position information of a sufficient data amount are obtained, and then the process proceeds to step S103, and then step S104 and step S105 are sequentially executed. Since step S102 includes step S1021 and step S1022, for this type of scenario, when step S1022 detects that the current failure report picture includes a failure location and outputs failure location information, if the data amount of the failure location information and the failure location information reaches a predetermined number, step S1022 is followed by step S103; if the number of pieces has not reached the predetermined number, the process proceeds to step S101 after step S1022. When it is detected in step S1022 that the current failure picture does not include a failure part, the process returns to step S101 after step S1022.
In some embodiments, as shown in fig. 3, a piece of vehicle failure location information and failure position information may be collected first, and then a piece of vehicle life related information of a vehicle to which the failure location belongs may be collected; the steps S101, S102 and S103 are executed in a loop until a sufficient number of the failure location information, the failure position information and the vehicle life related information are obtained, the process then proceeds to step S104, and then step S105 is executed again. Since step S102 includes step S1021 and step S1022, for this scenario, when step S1022 detects that the current failure report picture includes a failure location and outputs failure location information, step S1022 is followed by proceeding directly to step S103; after step S103, if the data amount does not reach a sufficient number, the process returns to step S101; if the data amount reaches a sufficient number, the process proceeds to step S104. When it is detected in step S1022 that the current safeguard picture does not include a faulty part, the process returns to step S101 after step S1022.
When sufficient fault location information, and corresponding vehicle life related information are obtained through steps S101, S102, and S103, step S104 may be entered.
And S104, establishing a life estimation training set according to the fault part, the fault position and the vehicle life related information. The life estimation training set is a data set of the fault location information and the fault location information acquired in step S102 and the vehicle life related information acquired in step S103, and includes a plurality of pieces of fault location information, and corresponding vehicle life related information. And the data of the life estimation training set is related to the accuracy of the model after training. When more data are obtained in steps S102 and S103, the more data of the life estimation training set are, the more abundant the data of the life estimation training set is, and thus the more accurate the trained life estimation model is.
As shown in fig. 2 and 3, after step S104, the process proceeds to step S105:
and S105, performing model training on the machine learning model by using the life estimation training set, and establishing the life estimation model. After the life estimation training set is established, the data of the life estimation training set can be used for model training. In the model training step, a known technique may be referred to, which is not repeated in this embodiment, and in a general case, the data of the life prediction training set is processed and corrected, such as feature extraction, feature dimension reduction, feature conversion, feature normalization, and the like, and then a training model is established using the processed data, and parameters of a model function are determined, so as to obtain the life prediction model. During training, an appropriate machine learning model can be selected for training according to actual needs, and for example, models such as a linear model, a neural network model, a fully-connected neural network model, a convolutional neural network model, and a cyclic neural network model can be used for model training.
As shown in fig. 2 and 3, after the steps S101, S102, S103, S104 and S105, a life estimation model can be established.
As shown in fig. 1, after the life estimation model is built in step S10, the process proceeds to step S20:
s20, deploying the life estimation model in a server;
as shown in fig. 1, after step S20, the flow proceeds to step S30:
and S30, extracting the vehicle information of the vehicle to be evaluated from the server. In general, all vehicles, including vehicles in operation and vehicles not in operation, such as new vehicles, vehicles to be transferred, old vehicles that have been repaired but not yet in use, and the like, are recorded on the server. The vehicle to be evaluated may be a vehicle already put into operation, or may be all vehicles put into operation and not put into operation, and may be specifically set according to actual needs. In some embodiments, to reduce the amount of server calculation, the service life evaluation may be performed only for the vehicles already put into operation, and in this case, the vehicle to be evaluated refers to only the vehicles already put into operation in this step. In some embodiments, the service life assessment may be performed for all vehicles registered by the server, wherein the assessed vehicles in this step refer to all vehicles that are already in operation and not in operation. In this embodiment, the vehicle to be evaluated generally refers to all vehicles registered in the server.
The vehicle information is information related to the vehicle stored in the server, as shown in fig. 7, and includes, but is not limited to, one or more of vehicle failure location information, vehicle life related information, and the like. The vehicle fault position information comprises historical fault position information and current fault position information. The fault location information includes historical fault location information and current fault location information. The vehicle life related information comprises vehicle operation input date information, reported fault city information and the like. The reported fault date information comprises historical reported fault date information and current reported fault date information. The reported fault city information comprises historical reported fault city information and current reported fault city information.
When the vehicle never has a fault, the vehicle information extracted in step S30 may only include vehicle commissioning date information and city information; when the vehicle has failed many times, the vehicle information extracted in step S30 may include a plurality of historical failure locations, failure locations of the historical failure locations, historical reported failure date information, historical reported failure city information, and vehicle commissioning date information. The vehicle information extracted in step S30 should be data that is analyzed by the life prediction model, and the specific setting thereof may be determined according to the function parameters of the life prediction model determined in step S10.
The policy for extracting the vehicle information of the vehicle to be evaluated from the server may be set according to actual conditions, for example, the vehicle information of one vehicle to be evaluated may be extracted at a time to perform subsequent life evaluation, or the vehicle information of a plurality of vehicles to be evaluated may be extracted at a time to perform subsequent life evaluation. For another example, the vehicle information of the vehicle can be extracted according to the sequence of the vehicle operation date so as to perform subsequent life evaluation, so that the vehicle which is earlier operated can be preferentially evaluated.
As shown in fig. 1, after step S30, the flow proceeds to step S40:
s40, the service life estimation model estimates the service life of each part of the current vehicle to be estimated according to the extracted vehicle information to obtain the estimated service life of each part; the service life prediction model compares the predicted service life with a preset component service life threshold value, and judges whether the current vehicle to be evaluated contains components of which the predicted service life reaches the service life threshold value;
the life threshold may be preset according to a situation, and may be a theoretical life of a vehicle component, an actual life threshold obtained by performing big data analysis according to the life estimation training set, or a certain life value smaller than the theoretical life or the actual life threshold. For example, after big data analysis is performed on the data of the life estimation training set, the actual life of the lock is usually 15 months, and in order to reduce the vehicle failure phenomenon encountered by the user, the life threshold value can be set to 14.5 months, so that operation and maintenance personnel can be notified in advance when the service life of the vehicle part is close to the limit, and the influence on user experience caused by the fact that the lock cannot be processed in time after the service life reaches the limit is avoided.
When the service life prediction model judges that the current vehicle to be evaluated comprises parts of which the predicted service life reaches the service life threshold, the service life prediction model outputs first result information to the server, so that the server can conveniently inform the first result information to a vehicle operation and maintenance part, and a vehicle operation and maintenance department can conveniently process the vehicle which is estimated to be about to break down in time.
The first result information is result information reporting that the estimated life of the part of the vehicle to be evaluated reaches the life threshold, and the specific format is not limited, and the first result information includes, but is not limited to, vehicle information of the vehicle to be evaluated currently and estimated life information of the part of the vehicle. For example, the format of the first result information may be: xxxxxxxx numbered vehicles, sitting in shanghai bao' an, the estimated life of the vehicle saddle remains for 3 days, please deal with it in time.
After the life estimation model outputs the first result information, go to step S30 (extracting the vehicle information of the vehicle to be evaluated from the server) to perform a new life estimation process: and re-extracting the vehicle information of other vehicles to be evaluated so as to estimate the service life of the vehicles.
When the life estimation model determines that the current vehicle to be estimated does not include any component whose estimated life reaches the life threshold, the process returns to step S30 (vehicle information of the vehicle to be estimated is extracted from the server) to execute a new life estimation process: and re-extracting the vehicle information of other vehicles to be evaluated so as to estimate the service life of the vehicles.
In the above steps S10, S20, S30, S40, S10 and S20 are one-time steps, and S30 and S40 are cyclic steps sequentially executed. In other words, after the life estimation model is built in step S10 and deployed in the server in step S20, the steps S30 and S40 are executed in a loop, and the life estimation of the components is performed on the vehicles in the server until the life estimation of all the vehicles is completed. Between S30 and S40, steps S30 and S30 are performed, and then the process proceeds to step S40. After the step S40, different steps are performed according to the evaluation result of S40: when the estimated life of the part of the vehicle to be currently evaluated reaches the life threshold value in the step S40, outputting first result information, and then returning to the step S30 to repeat the cycle; when the estimated life of the current vehicle to be evaluated without the parts reaches the life threshold value in the step S40, the process returns to the step S30 directly to repeat the cycle.
In the present embodiment, as shown in fig. 5, the vehicle failure detection model in steps S1021 and S1022 is trained by the following steps:
A. extracting fault reporting pictures which relate to faults of all parts of the vehicle and are submitted when a user reports faults from the server; the number of the fault reporting pictures is enough, and the fault reporting pictures relate to all parts of the vehicle, so that the initial data is rich and comprehensive enough. In this step, in some embodiments, only the failure report picture of the real fault may be extracted. In some embodiments, the failure reporting picture of the real fault and the failure reporting picture of the false failure can be extracted without being treated differently. In this embodiment, in order to facilitate the vehicle fault detection model to automatically determine whether the fault reporting image includes a fault portion, so as to improve the accuracy of distinguishing a real fault from a false fault, in this step, the fault reporting image extracted from the server should include a fault reporting image of a real fault and a fault reporting image of a false fault.
B. Manually marking the vehicle fault part in the fault reporting picture; in the step, when a fault part exists in the fault reporting picture, manually marking the fault part; when the fault part does not exist in the fault reporting picture, no identification is carried out on the fault reporting picture, or a special identification is used for marking. For example, a rectangular frame including the fault location may be drawn in the fault report picture, and the fault location in the rectangular frame is a detection target for training a target detection model in a subsequent step.
C. Establishing a fault detection training set according to the manually marked fault reporting picture; and the fault detection training set is a data set of fault reporting pictures acquired in the step B, and in the embodiment, the fault detection training set comprises a plurality of fault reporting pictures with fault parts and a plurality of fault reporting pictures without fault parts, which are manually marked. In other embodiments, the fault detection training set may also include only several manually labeled fault reporting pictures with fault locations. The quantity of data of the fault detection training set is related to the accuracy of the model after training. When the fault detection training set comprises more data and the types of fault parts are richer, the trained vehicle fault detection model is more accurate.
D. And performing model training on a target detection model based on deep learning by using the fault detection training set, and establishing the vehicle fault detection model. After the fault detection training set is established, the data of the fault detection training set can be used for model training. In this embodiment, model training is performed by using a deep learning-based target detection model, and in the step of model training, reference may be made to a known technology, which is not described in detail in this embodiment.
The vehicle part service life prediction method based on the user fault reporting data can automatically predict the service life of the vehicle parts registered in the server on line, so that whether the vehicle parts reach the service life limit or not is judged, prediction can be performed before the vehicle parts are formally in fault, and related operation and maintenance personnel are notified to perform processing, so that the workload of the operation and maintenance personnel can be reduced, the working efficiency of the operation and maintenance personnel is improved, and the influence on the experience of the user due to the fact that the fault vehicles are mixed in the service is avoided.
Example 2
The embodiment provides a vehicle part life prediction system based on user fault reporting data, as shown in fig. 8 and fig. 9, the system includes a server, a vehicle information acquisition module, a life prediction model, a fault reporting picture acquisition module, a vehicle fault detection model, and a vehicle life information acquisition module.
The server records vehicle information of all vehicles, including vehicle information of all vehicles in operation and all vehicles not in operation.
The vehicle information refers to information related to the vehicle, and includes, but is not limited to, one or more of historical fault positions of the vehicle, fault positions of the historical fault positions, current fault positions, fault positions of the current fault positions, vehicle operation-on date information, historical reported fault date information, current reported fault date information, historical reported fault city information, current reported fault city information, and city information.
When the vehicle never fails, the vehicle information recorded in the server may only include vehicle commissioning date information and belonging city information;
when a vehicle has multiple faults, the vehicle information recorded in the server may include multiple historical fault parts, fault positions of the historical fault parts, historical reported fault date information, historical reported fault city information and vehicle operation-on date information.
The server can be used for receiving and storing fault reporting pictures which are submitted by users when fault reporting is carried out and report vehicle faults.
And the fault reporting pictures received and stored by the server comprise a vehicle fault picture in real fault and a vehicle picture in false fault reporting.
As shown in fig. 9, the failure report picture acquiring module is configured to extract a failure report picture for reporting a vehicle failure, which is submitted when a user reports a failure, from the server.
As shown in fig. 9, the failure report picture acquiring module is further configured to input the extracted failure report picture into the vehicle failure detection model.
The fault reporting picture acquisition module extracts fault reporting pictures from the server, wherein the fault reporting pictures comprise a vehicle fault picture of a real fault and a vehicle picture during false fault reporting.
As shown in fig. 9, the vehicle fault detection model is deployed in the server, and is configured to automatically detect a fault location where a vehicle fault location occurs in the fault report picture input by the fault report picture acquisition module.
The fault reporting pictures extracted by the fault reporting picture acquisition module comprise a vehicle fault picture of a real fault and a vehicle picture during false fault reporting, so that the vehicle fault detection model automatically detects whether a vehicle in the fault reporting pictures contains a fault part to distinguish the fault reporting pictures of the real fault and the fault reporting pictures of the false fault, and then detects a specific fault position of the fault part.
As shown in fig. 9, when the vehicle fault detection model detects that the fault location is included in the vehicle in the fault report picture, the vehicle fault detection model continues to detect the fault location of the fault location in the fault report picture, and outputs the fault location information and the corresponding fault location information to the server.
The fault part is a part on the vehicle, such as a lock, a saddle, a chain, etc. The fault position is a relatively more specific position where a fault occurs on the fault position, for example, when a password device of the vehicle lock fails, the fault position is the vehicle lock, and the fault position is the password device on the vehicle lock. It should be understood that the range of the fault location is wider than the fault location, the fault location is only a certain location point where the fault location is faulty, and the component corresponding to the fault location is only a part of the component corresponding to the fault location. The vehicle fault detection model firstly judges a large-scale fault position on the fault report picture, and then further judges a fault position of a more specific point on the fault position.
When the vehicle fault detection model detects that the fault reporting picture does not contain a fault part, the vehicle fault detection model does not detect the fault reporting picture any more so as to complete the current automatic detection process of the fault reporting picture.
And the server is also used for receiving and storing fault position information and fault position information output by the vehicle fault detection model.
Through the fault reporting picture acquisition module and the vehicle fault detection model, the fault reporting picture uploaded to the server can be automatically detected on line, and whether the vehicle fault information currently reported to the server by a user is real or not is judged in time, so that the workload of operation and maintenance personnel can be reduced, and the working efficiency of the operation and maintenance personnel is improved.
In addition, after the fault position and the fault position information of the fault reporting picture are automatically detected through the fault reporting picture acquisition module and the vehicle fault detection model, the data can be further conveniently used for carrying out big data analysis and training the service life prediction model, the service life prediction model is continuously improved, the accuracy of the prediction result of the service life prediction model is improved, and corresponding data references are provided for a vehicle research and development department and a management department.
The vehicle fault detection model may be formed by performing model training according to the fault report picture in the server, and the specific steps thereof may refer to step A, B, C, D (shown in fig. 5) in embodiment 1, which is not described again in this embodiment.
As shown in fig. 9, the vehicle life information acquiring module is configured to extract vehicle life related information of a vehicle to which the fault location belongs from the server.
When the vehicle fault detection model detects a fault part in the fault report picture and outputs the fault part information to the server, the vehicle life information acquisition module can extract the vehicle life related information of the vehicle to which the fault part information belongs from the server according to the fault part information.
The vehicle life related information includes, but is not limited to, vehicle operation input date information, reporting fault date information, and reporting fault city information. The time interval between the reported failure date and the vehicle operation input date is directly related to the service life of the vehicle part, so that the time interval can be used as one of the parameters for estimating the service life of the vehicle part; different climates and different using habits of different cities also have certain influence on the service life of the vehicle, so that the city information of the vehicle can be used as one of the parameters for estimating the service life of the vehicle. In other embodiments, suitable data may be selected as the vehicle life related information according to actual needs.
As shown in fig. 9, the vehicle fault detection model can obtain the fault location and fault position information of the vehicle to which the fault report picture belongs, and the vehicle life related information of the vehicle to which the fault location belongs can be extracted from the vehicle life related information; and establishing a life estimation training set by using enough fault part information, fault position information and corresponding vehicle life related information to train the life estimation model.
As shown in fig. 1, the life estimation model may be formed by performing model training through steps S101, S102, S103, S104, and S105 in embodiment 1, and the establishing process is not described in detail in this embodiment. And after the life estimation model is established, the life estimation model is deployed in the server.
As shown in fig. 8, the vehicle information obtaining module is configured to extract vehicle information of a vehicle to be evaluated from the server, and to transmit the extracted vehicle information to the life estimation model.
As shown in fig. 8, the service life estimation model is used for estimating the service life of each component of the vehicle to be estimated according to the vehicle information, so as to obtain the estimated service life of each component.
The service life prediction model is further used for comparing the predicted service life with a preset service life threshold of the part to judge whether the vehicle to be evaluated contains the part of which the predicted service life reaches the service life threshold.
The life threshold may be preset according to a situation, and may be a theoretical life of a vehicle component, an actual life threshold obtained by performing big data analysis according to the life estimation training set in embodiment 1, or a certain life value smaller than the theoretical life or the actual life threshold.
As shown in fig. 8, the life estimation model is further configured to output the first result information to the server. And when the service life prediction model judges that the vehicle to be evaluated contains parts of which the predicted service life reaches a service life threshold value, the service life prediction model outputs first result information to the server.
As shown in fig. 8, the server is further configured to receive first result information output by the life estimation model, and send the first result information to a corresponding vehicle operation and maintenance department, so that the vehicle operation and maintenance department can timely process a vehicle that is estimated to be about to fail.
The first result information is result information reporting that the estimated life of the component of the estimated vehicle reaches a life threshold, and the specific format is not limited, and may be set according to actual needs. The first result information includes, but is not limited to, vehicle information of a vehicle to be currently evaluated, estimated life information of vehicle components, evaluation result information, and the like.
The server, the vehicle information acquisition module, the service life prediction model, the guarantee picture acquisition module, the vehicle fault detection model and the vehicle service life information acquisition module form the vehicle part service life prediction system based on the user fault reporting data, the system can perform service life prediction on parts of the vehicle registered in the server on line automatically, and therefore whether the parts of the vehicle reach the service life limit is judged, prediction can be performed before the parts of the vehicle are formally faulted, related operation and maintenance personnel are informed to process, the workload of the operation and maintenance personnel can be reduced, the working efficiency of the operation and maintenance personnel is improved, and the phenomenon that the experience of the user is influenced due to the fact that the faulted vehicle is mixed in for use is avoided.
While the invention has been described with reference to the above embodiments, the scope of the invention is not limited thereto, and the above components may be replaced with similar or equivalent elements known to those skilled in the art without departing from the spirit of the invention.
Claims (12)
1. A vehicle part service life prediction method based on user fault reporting data is characterized by comprising the following steps:
establishing a life estimation model;
deploying the life estimation model in a server;
extracting vehicle information of a vehicle to be evaluated in operation from the server;
the service life estimation model estimates the service life of each part of the current vehicle to be estimated according to the extracted vehicle information to obtain the estimated service life of each part;
the service life prediction model compares the predicted service life with a preset component service life threshold value, and judges whether the current vehicle to be evaluated contains components of which the predicted service life reaches the service life threshold value;
if yes, the service life prediction model outputs first result information.
2. The vehicle part life prediction method based on user fault reporting data as claimed in claim 1, wherein the life prediction model compares the predicted life with the preset part life threshold, when it is determined whether the current vehicle to be evaluated contains a part whose predicted life reaches the life threshold, if not, vehicle information of other vehicles to be evaluated in operation is extracted from the server, and the life prediction model predicts the service life of each part of the current vehicle to be evaluated again according to the extracted vehicle information.
3. The method for predicting the service life of the vehicle part based on the user fault-reporting data as claimed in claim 1, wherein the method for predicting the service life comprises the following steps:
extracting an error reporting picture which is submitted when a user reports an error and reports a vehicle fault from the server;
automatically detecting the fault position of the vehicle fault part in the fault reporting picture by using a vehicle fault detection model;
extracting vehicle life related information of a vehicle to which the fault part belongs from the server;
establishing a life estimation training set according to the fault part, the fault position and the vehicle life related information;
and performing model training on a machine learning model by using the life estimation training set, and establishing the life estimation model.
4. The vehicle part life prediction method based on user fault reporting data as claimed in claim 3, wherein the vehicle information comprises one or more of the vehicle fault location, fault location of the fault location, and the vehicle life related information; the vehicle life related information comprises vehicle operation input date information, reported fault date information and reported fault city information.
5. The vehicle part life prediction method based on user fault reporting data as claimed in claim 3, wherein when the vehicle fault detection model is used to automatically detect the fault position of the vehicle fault part in the fault reporting picture, the method comprises the following steps:
inputting the fault reporting picture into the vehicle fault detection model;
the vehicle fault detection model automatically detects whether the vehicle in the fault report picture contains a fault part;
if so, the vehicle fault detection model detects the fault position of the fault part;
if not, extracting the fault reporting picture which is submitted when the user reports the fault and reports the vehicle fault from the server again.
6. The method of claim 5, wherein the vehicle fault detection model is trained by the steps of:
extracting fault reporting pictures which relate to faults of all parts of the vehicle and are submitted when a user reports faults from the server;
manually marking the vehicle fault part in the fault reporting picture;
establishing a fault detection training set according to the manually marked fault reporting picture;
and performing model training on a target detection model based on deep learning by using the fault detection training set, and establishing the vehicle fault detection model.
7. The vehicle part life prediction method based on user fault reporting data as recited in claim 3, wherein the vehicle fault detection model is deployed in the server; the fault reporting pictures comprise a guarantee picture of a real fault and a fault reporting picture in a false fault reporting process.
8. The vehicle part life prediction method based on data for reporting faults as claimed in claim 1, wherein after the life prediction model outputs first result information, the life prediction model notifies the first result information to a vehicle operation and maintenance department; the first result information is result information reporting that the estimated life of the part of the vehicle to be evaluated reaches a life threshold.
9. The vehicle part life prediction method for reporting faults as claimed in claim 1, wherein the first result information comprises vehicle information of the vehicle to be evaluated, estimated life of the vehicle part to be evaluated and evaluation result information.
10. A vehicle component life prediction system based on user fault reporting data, comprising:
the server is provided with vehicle information of all vehicles in operation and not put into operation and can be used for receiving and storing fault reporting pictures which are submitted by users when fault reporting is carried out and report vehicle faults;
the vehicle information acquisition module is used for extracting the vehicle information of the vehicle to be evaluated from the server;
the service life prediction model is deployed in the server and used for predicting the service life of each part of the vehicle to be evaluated according to the vehicle information to obtain the predicted service life of each part; and the service life estimation module is used for comparing the estimated service life with a preset service life threshold of the part and judging whether the vehicle to be evaluated contains the part of which the estimated service life reaches the service life threshold.
11. The vehicle component life prediction system based on user-reported data of claim 10,
the service life prediction model is further used for outputting first result information to the server when the service life prediction model judges that the vehicle to be evaluated contains parts of which the predicted service life reaches a service life threshold;
the server is also used for receiving and storing the first result information.
12. The vehicle component life prediction system based on user-reported data as recited in claim 10, further comprising:
the fault reporting picture acquisition module is used for extracting a fault reporting picture which is submitted by a user when a fault is reported and reports a vehicle fault from the server;
and the vehicle fault detection model is deployed in the server and is used for automatically detecting the fault position of the vehicle fault part in the fault report picture.
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