CN115049444B - Data processing method, device, equipment and medium - Google Patents
Data processing method, device, equipment and medium Download PDFInfo
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Abstract
The application discloses a data processing method, a device, equipment and a medium, which are applied to the technical field of computers and are used for solving the problems of higher maintenance cost and use cost and lower query efficiency of vehicle data in the prior art. The method comprises the following steps: classifying the vehicle data of different vehicle brands by using the data classification model based on different coroutines respectively to obtain the vehicle data of different vehicle types of different vehicle brands; for each vehicle brand, utilizing a coroutine corresponding to the vehicle brand to obtain a brand vehicle database of the vehicle brand based on vehicle data of different vehicle types of the vehicle brand; for each equipment type, the product vehicle database corresponding to the equipment type is obtained based on the brand vehicle database of each vehicle brand supported by the vehicle diagnosis equipment of the equipment type, so that reasonable classification of vehicle data can be realized, the maintenance cost and the use cost of the vehicle data can be reduced, and the query efficiency of the vehicle data is improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a data processing method, apparatus, device, and medium.
Background
At present, vehicle diagnosis mainly depends on vehicle maintenance engineers to inquire vehicle data such as year, model, displacement and the like of a vehicle in vehicle diagnosis equipment, and then, the vehicle diagnosis is carried out according to the inquired vehicle data.
Along with the continuous acceleration of the update speed of the vehicle, the vehicle data is gradually huge and complicated, which not only causes the maintenance difficulty and the use cost of the vehicle data to be increased, but also can influence the query efficiency of the vehicle data.
Disclosure of Invention
The embodiment of the application provides a data processing method, a data processing device and a data processing medium, which are used for solving the problems that in the prior art, the maintenance difficulty and the use cost of vehicle data are high, and the query efficiency is low.
The technical scheme provided by the embodiment of the application is as follows:
in one aspect, an embodiment of the present application provides a data processing method, including:
for each vehicle brand, classifying the vehicle data of the vehicle brand based on a data classification model corresponding to the vehicle brand by utilizing a cooperative distance corresponding to the vehicle brand to obtain the vehicle data of different vehicle types corresponding to the vehicle brand; the data classification model corresponding to the vehicle brand is a machine learning model which is obtained by training based on historical vehicle data of the vehicle brand and standard vehicle types of the historical vehicle data and is used for classifying the vehicle data of the vehicle brand according to the vehicle types;
For each vehicle brand, utilizing a coroutine corresponding to the vehicle brand to obtain a brand vehicle database corresponding to the vehicle brand based on vehicle data of different vehicle types corresponding to the vehicle brand;
for each equipment type, a product vehicle database corresponding to the equipment type is obtained based on a brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnostic equipment of the equipment type in brand vehicle databases corresponding to different vehicle brands.
In one possible implementation manner, before classifying the vehicle data of each vehicle brand based on the data classification model corresponding to the vehicle brand by using the corollary corresponding to the vehicle brand, the method further includes:
for each vehicle brand, circularly executing batch searching operation on the original vehicle database by utilizing the coroutine corresponding to the vehicle brand until all vehicle data searching of the vehicle brand in the original vehicle database is determined to be completed; wherein the batch retrieval operation comprises: and generating SQL sentences based on the original database identification, the current query starting identification and the current query target quantity, and querying the vehicle data of the vehicle brand from the original vehicle database through the SQL sentences.
In one possible embodiment, when obtaining the product vehicle database corresponding to the device type based on the brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnostic device of the device type in the brand vehicle databases corresponding to different vehicle brands, the method further includes:
based on the equipment type, each vehicle brand supported by the equipment type vehicle diagnosis equipment and different vehicle types corresponding to each vehicle brand, respectively adding index data for the vehicle data of different vehicle types corresponding to each vehicle brand in a product vehicle database corresponding to the equipment type; the data structure of each index data in the product vehicle database corresponding to the equipment type is a tree structure.
In a possible implementation manner, the data processing method provided in the embodiment of the present application further includes:
receiving a data query request initiated by vehicle diagnostic equipment aiming at a target vehicle; the data query request comprises the equipment type of the vehicle diagnosis equipment, the vehicle brand of the target vehicle and the vehicle type;
determining a product vehicle database corresponding to the vehicle diagnosis equipment based on the equipment type of the vehicle diagnosis equipment, and inquiring vehicle data of the target vehicle from the product vehicle database corresponding to the vehicle diagnosis equipment based on the vehicle brand and the vehicle type of the target vehicle;
And packaging the vehicle data of the target vehicle into a data query response, and returning the data query response to the vehicle diagnostic equipment for display.
In one possible embodiment, querying vehicle data of the target vehicle from a product vehicle database corresponding to the vehicle diagnostic device based on a vehicle brand and a vehicle type of the target vehicle includes:
determining index data of a vehicle brand based on the vehicle brand of the target vehicle, and inquiring a brand vehicle database corresponding to the vehicle brand from product vehicle databases corresponding to the vehicle diagnostic equipment based on the index data of the vehicle brand;
and determining index data of the vehicle type based on the vehicle type of the target vehicle, and inquiring the vehicle data of the target vehicle from a brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type.
In one possible embodiment, querying vehicle data of a target vehicle from a brand vehicle database corresponding to a vehicle brand based on index data of a vehicle type includes:
and based on the index data of the vehicle type, performing paging inquiry operation on the brand vehicle database corresponding to the vehicle brand in a circulating way until the vehicle data of the target vehicle is inquired.
In a possible implementation manner, the data processing method provided in the embodiment of the present application further includes:
receiving a vehicle diagnosis request initiated by vehicle diagnosis equipment aiming at a target vehicle; the vehicle diagnosis request comprises monitoring data of a target vehicle;
acquiring a vehicle diagnosis result of the target vehicle by using a vehicle diagnosis model based on the monitoring data of the target vehicle and the vehicle data of the target vehicle in a product vehicle database corresponding to the vehicle diagnosis equipment; the vehicle diagnosis model is a machine learning model which is obtained by training based on historical monitoring data, historical vehicle data and standard fault types of different vehicle types corresponding to different vehicle brands and is used for carrying out fault diagnosis on the target vehicle according to the fault types.
In another aspect, an embodiment of the present application provides a data processing apparatus, including:
the data classification unit is used for classifying the vehicle data of each vehicle brand by utilizing the cooperative distance corresponding to the vehicle brand based on the data classification model corresponding to the vehicle brand to obtain the vehicle data of different vehicle types corresponding to the vehicle brand; the data classification model corresponding to the vehicle brand is a machine learning model which is obtained by training based on historical vehicle data of the vehicle brand and standard vehicle types of the historical vehicle data and is used for classifying the vehicle data corresponding to the vehicle brand according to the vehicle types;
The brand establishment unit is used for obtaining a brand vehicle database corresponding to each vehicle brand based on vehicle data of different vehicle types corresponding to the vehicle brand by utilizing a coroutine corresponding to the vehicle brand;
the product establishing unit is used for obtaining a product vehicle database corresponding to each equipment type based on the brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnosis equipment of the equipment type in the brand vehicle databases corresponding to different vehicle brands.
In one possible implementation manner, for each vehicle brand, before classifying the vehicle data of the vehicle brand based on the data classification model corresponding to the vehicle brand by using the co-range corresponding to the vehicle brand, the data classification unit is further configured to:
for each vehicle brand, circularly executing batch searching operation on the original vehicle database by utilizing the coroutine corresponding to the vehicle brand until all vehicle data searching of the vehicle brand in the original vehicle database is determined to be completed; wherein the batch retrieval operation comprises: and generating SQL sentences based on the original database identification, the current query starting identification and the current query target quantity, and querying the vehicle data of the vehicle brand from the original vehicle database through the SQL sentences.
In a possible implementation manner, the data processing apparatus provided in the embodiment of the present application further includes:
an index adding unit, configured to add index data to vehicle data of different vehicle types corresponding to each vehicle brand in a product vehicle database corresponding to the equipment type, based on the equipment type, each vehicle brand supported by the equipment type vehicle diagnostic equipment, and different vehicle types corresponding to each vehicle brand; the data structure of each index data in the product vehicle database corresponding to the equipment type is a tree structure.
In a possible implementation manner, the data processing apparatus provided in the embodiment of the present application further includes:
the data query unit is used for receiving a data query request initiated by the vehicle diagnosis equipment aiming at the target vehicle; the data query request comprises the equipment type of the vehicle diagnosis equipment, the vehicle brand of the target vehicle and the vehicle type; determining a product vehicle database corresponding to the vehicle diagnosis equipment based on the equipment type of the vehicle diagnosis equipment, and inquiring vehicle data of the target vehicle from the product vehicle database corresponding to the vehicle diagnosis equipment based on the vehicle brand and the vehicle type of the target vehicle; and packaging the vehicle data of the target vehicle into a data query response, and returning the data query response to the vehicle diagnostic equipment for display.
In one possible implementation manner, when the vehicle data of the target vehicle is queried from the product vehicle database corresponding to the vehicle diagnosis device based on the vehicle brand and the vehicle type of the target vehicle, the data query unit is specifically configured to:
determining index data of a vehicle brand based on the vehicle brand of the target vehicle, and inquiring a brand vehicle database corresponding to the vehicle brand from product vehicle databases corresponding to the vehicle diagnostic equipment based on the index data of the vehicle brand;
and determining index data of the vehicle type based on the vehicle type of the target vehicle, and inquiring the vehicle data of the target vehicle from a brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type.
In one possible embodiment, when the vehicle data of the target vehicle is queried from the brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type, the data query unit is specifically configured to:
and based on the index data of the vehicle type, performing paging inquiry operation on the brand vehicle database corresponding to the vehicle brand in a circulating way until the vehicle data of the target vehicle is inquired.
In a possible implementation manner, the data processing apparatus provided in the embodiment of the present application further includes:
A vehicle diagnosis unit for receiving a vehicle diagnosis request initiated by a vehicle diagnosis device for a target vehicle; the vehicle diagnosis request comprises monitoring data of a target vehicle; acquiring a vehicle diagnosis result of the target vehicle by using a vehicle diagnosis model based on the monitoring data of the target vehicle and the vehicle data of the target vehicle in a product vehicle database corresponding to the vehicle diagnosis equipment; the vehicle diagnosis model is a machine learning model which is obtained by training based on historical monitoring data, historical vehicle data and standard fault types of different vehicle types corresponding to different vehicle brands and is used for carrying out fault diagnosis on the target vehicle according to the fault types.
In another aspect, an embodiment of the present application provides an electronic device, including: the data processing method provided by the embodiment of the application is implemented by the memory, the processor and the computer program stored on the memory and capable of running on the processor when the processor executes the computer program.
On the other hand, the embodiment of the application also provides a computer readable storage medium, and the computer readable storage medium stores computer instructions which when executed by a processor realize the data processing method provided by the embodiment of the application.
The beneficial effects of the embodiment of the application are as follows:
in the embodiment of the application, a large amount of vehicle data is classified based on the data classification model by utilizing different cooperative distance, so that the data classification efficiency can be improved, and the data classification accuracy can be improved. In addition, by classifying a large amount of vehicle data according to the vehicle brands and the vehicle types and generating brand vehicle databases corresponding to different vehicle brands, reasonable classification of the vehicle data can be realized, so that maintenance cost and use cost of the vehicle data can be reduced, query efficiency of the vehicle data is improved, and further classification of the vehicle data can be realized by associating the equipment type with each vehicle brand supported by the equipment type vehicle diagnosis equipment and generating a product vehicle database corresponding to the equipment type, so that query efficiency of the vehicle data can be further improved, and good data support can be provided for analysis and improvement of the equipment type vehicle diagnosis equipment.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a schematic flow chart of an overview of a data processing method in an embodiment of the present application;
FIG. 2a is a schematic diagram of parallel processing of vehicle data in different coroutines according to an embodiment of the present application;
FIG. 2b is a schematic diagram of the association of a vehicle diagnostic device with a product vehicle database and a brand vehicle database in an embodiment of the present application;
FIG. 3 is a schematic flow chart of a vehicle data query and vehicle diagnostic operation in an embodiment of the present application;
FIG. 4 is a schematic functional structure of a data processing apparatus according to an embodiment of the present application;
fig. 5 is a schematic diagram of a hardware structure of an electronic device in an embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantageous effects of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only some embodiments, but not all embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In order to facilitate a better understanding of the present application, technical terms related to the embodiments of the present application will be briefly described below.
The brand vehicle database is a database for storing vehicle data of different vehicle types corresponding to a certain vehicle brand. In the embodiment of the application, one vehicle brand corresponds to one brand vehicle database, and the brand vehicle database may be a brand vehicle data table.
The product vehicle database is a database for storing vehicle data of different vehicle types corresponding to various vehicle brands supported by a certain vehicle diagnosis device. In embodiments of the present application, a vehicle diagnostic product vehicle database may be, but is not limited to, a product vehicle data sheet.
The vehicle diagnosis device is a device which is connected with the target vehicle in a wired or wireless communication way and is used for collecting monitoring data such as the frame number, the current driving mileage, the current rotating speed of a transmitter and the like of the target vehicle.
The remote diagnosis device is a device which is directly or indirectly connected with the vehicle diagnosis device and is used for carrying out fault diagnosis on the target vehicle based on the monitoring data of the target vehicle collected by the vehicle diagnosis device and the vehicle data of the target vehicle inquired from the product vehicle database. In the embodiment of the application, the remote diagnosis device may be a server, a computer, or the like.
The vehicle diagnosis model is a machine learning model which is obtained by training based on historical monitoring data, historical vehicle data and standard fault types of different vehicle types corresponding to different vehicle brands and is used for carrying out fault diagnosis on the target vehicle according to the fault types.
The data classification model is a machine learning model which is obtained by training based on historical vehicle data of one vehicle brand and standard vehicle types of the historical vehicle data and is used for classifying the vehicle data of the one vehicle brand according to the vehicle types. In this embodiment of the present application, each vehicle brand corresponds to a data classification model, and the data classification model corresponding to each vehicle brand may include a machine learning model (for example, a convolutional neural network model), or may include a plurality of classification models, where one classification model is used to detect one vehicle type; in addition, in the embodiment of the application, different vehicle types can flexibly adjust the classification mode according to actual demands, for example, when the vehicle types are classified according to power sources, the different vehicle types can comprise fuel vehicles, hybrid electric vehicles, pure electric vehicles and the like; different vehicle types when classified by vehicle type may include small, mini, compact, medium, advanced, luxury, three-compartment, car platform based Van (CDV), multi-Purpose Vehicles (MPV), sport utility Vehicles (Sport Utility Vehicle, SUV), etc.; different vehicle types when classified according to functions can include recreational vehicles, travel cars, sedans, sports cars, convertible vehicles and the like; different vehicle types when classified according to the carriage can include single carriage, two carriage half carriage, three carriage and the like.
After technical terms related to the application are introduced, application scenes and design ideas of the embodiment of the application are briefly introduced.
At present, vehicle data is usually stored in a form in a magnetic disk of a remote diagnosis device, the form data is about 10G, the data volume is large, and as the speed of updating vehicles is continuously increased, the vehicle data is exponentially increased, and millions of vehicle data of one vehicle brand are usually stored in one form, so that the maintenance difficulty and the use cost of the vehicle data are increased, and the query efficiency of the vehicle data is affected.
Therefore, in the embodiment of the application, the remote diagnosis device classifies a large amount of vehicle data based on the data classification model by using different cooperative distance respectively to obtain vehicle data of different vehicle types corresponding to different vehicle brands, establishes brand vehicle databases for the different vehicle brands based on the vehicle data of the different vehicle types corresponding to the different vehicle brands respectively, and establishes product vehicle databases for the vehicle diagnosis devices of the different device types based on the vehicle data of the different vehicle types corresponding to the vehicle brands supported by the vehicle diagnosis devices of the different device types respectively. Therefore, by utilizing different coroutines to classify a large amount of vehicle data based on the data classification model, not only can the data classification efficiency be improved, but also the data classification accuracy can be improved. In addition, by classifying a large amount of vehicle data according to the vehicle brands and the vehicle types, generating brand vehicle databases corresponding to different vehicle brands, associating the device types with the vehicle brands supported by the vehicle diagnosis device of the device types, and generating product vehicle databases corresponding to different device types, reasonable classification of the vehicle data can be realized, so that maintenance cost and use cost of the vehicle data can be reduced, query efficiency of the vehicle data is improved, and good data support is provided for analysis and improvement of the vehicle diagnosis device.
After the application scenario and the design idea of the embodiment of the present application are introduced, the technical solutions provided by the embodiment of the present application are described in detail below.
The embodiment of the application provides a data processing method, which can be applied to a remote diagnosis device, and is shown in fig. 1, and the outline flow of the data processing method provided by the embodiment of the application is as follows:
step 101: the remote diagnosis device classifies the vehicle data of each vehicle brand by utilizing the cooperative distance corresponding to the vehicle brand based on the data classification model corresponding to the vehicle brand, and obtains the vehicle data of different vehicle types corresponding to the vehicle brand.
In practical applications, the remote diagnosis device may train the initial data classification model in advance based on the historical vehicle data of each vehicle brand and the standard vehicle type of the historical vehicle data, so as to obtain the data classification model. Specifically, for each vehicle brand, the remote diagnosis device may take the historical vehicle data of the vehicle brand and the standard vehicle type of the historical vehicle data as a training sample set, and perform iterative training on the initial data classification model based on the training sample set until it is determined that an iteration termination condition is met (for example, the number of iterative training reaches a set number of times, and if the current loss value is not greater than a set threshold value, etc.), and obtain a data classification model based on each model parameter of the initial data classification model obtained by the last iterative training; wherein each iterative training comprises: selecting a target training sample from the training sample set, inputting historical vehicle data in the target training sample into an initial data classification model to obtain a predicted vehicle type, obtaining a current loss value based on the predicted vehicle type and a standard vehicle type in the target training sample, and updating each model parameter of the initial data classification model based on the current loss value.
Further, after the remote diagnosis device trains the data classification model of each vehicle brand, the vehicle data of each vehicle brand can be obtained, and the vehicle data of each vehicle brand is classified based on the data classification model of each vehicle brand. Specifically, the remote diagnostic device may create a coroutine for each vehicle brand separately; performing batch search operation on the original vehicle database circularly by utilizing a coroutine corresponding to the vehicle brand for each vehicle brand until all vehicle data of the vehicle brand in the original vehicle database are determined to be searched, wherein the batch search operation comprises the steps of generating SQL sentences based on original database identifications (such as name information, index data, storage addresses and the like of the original vehicle database), current inquiry starting identifications (such as page 1, line 100 and the like) and current inquiry target numbers (such as 500000) and inquiring vehicle data of the vehicle brand from the original vehicle database through the SQL sentences; and then, aiming at each vehicle brand, utilizing a cooperative distance corresponding to the vehicle brand to respectively input all vehicle data of the vehicle brand into a data classification model corresponding to the vehicle brand for classification processing, and obtaining the vehicle data of different vehicle types corresponding to the vehicle brand.
Step 102: the remote diagnosis device obtains a brand vehicle database corresponding to each vehicle brand based on vehicle data of different vehicle types corresponding to the vehicle brand by utilizing a coroutine corresponding to the vehicle brand.
In practical application, the remote diagnosis device can utilize the corresponding protocol of each vehicle brand to perform format conversion on the vehicle data of different vehicle types corresponding to the vehicle brand according to the set data format, and judge whether the vehicle brand has a brand vehicle database; if the vehicle data of different vehicle types corresponding to the vehicle brand after the format conversion exist, the vehicle data of different vehicle types corresponding to the vehicle brand can be saved in a brand vehicle database of the vehicle brand in batches; if the vehicle type data does not exist, a brand vehicle database can be created for the vehicle brand, and the vehicle data of different vehicle types corresponding to the vehicle brand after format conversion is saved in batches to the brand vehicle database of the vehicle brand.
Step 103: the remote diagnosis device obtains a product vehicle database corresponding to each device type based on a brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnosis device of the device type in brand vehicle databases corresponding to different vehicle brands.
In practical application, the remote diagnosis device can convert the format of vehicle data of different vehicle types corresponding to each vehicle brand supported by the vehicle diagnosis device of the device type according to the set data format, and judge whether the device type has a product vehicle database; if the vehicle data of different vehicle types corresponding to the vehicle brands supported by the vehicle diagnosis equipment of the equipment type after the format conversion exist, the vehicle data of different vehicle types are stored in a product vehicle database of the equipment type in batches; if the vehicle type is not present, a product vehicle database can be created for the equipment type, and vehicle data of different vehicle types corresponding to various vehicle brands supported by the vehicle diagnosis equipment of the equipment type after format conversion are stored in the product vehicle database of the equipment type in batches.
It is worth mentioning that, in the embodiment of the present application, the brand vehicle databases corresponding to different vehicle brands and the product vehicle databases corresponding to different device types may also be updated continuously in real time or periodically according to the increase or decrease of the vehicle data, so that the comprehensiveness and accuracy of the brand vehicle databases and the product vehicle databases may be ensured. In addition, in the embodiment of the application, a large amount of vehicle data is classified according to the vehicle brands and the vehicle types, and brand vehicle databases corresponding to different vehicle brands are generated, so that reasonable classification of the vehicle data can be realized, the maintenance cost and the use cost of the vehicle data can be reduced, the query efficiency of the vehicle data is improved, and further classification of the vehicle data can be realized by associating the equipment type with each vehicle brand supported by the equipment type vehicle diagnosis equipment and generating the product vehicle database corresponding to the equipment type, so that the query efficiency of the vehicle data can be further improved, and good data support can be provided for analysis and improvement of the vehicle diagnosis equipment.
The following describes in further detail the data processing method according to steps 101 to 103 in the embodiment of the present application, specifically, when the remote diagnosis device processes, for each vehicle brand, the vehicle data corresponding to the vehicle brand by using the cooperative distance corresponding to the vehicle brand, for example, as shown in fig. 2a, the following manner may be adopted, but not limited to:
first, the remote diagnosis apparatus creates n covitines (), which represent a process of waiting for vehicle data of n vehicle brands, using waitgroup. Add (n); wherein n vehicle brands make up a constant set.
Then, the remote diagnosis device asynchronously and parallelly processes the vehicle data of each vehicle brand through n covariates by using the program language go key, and determines waitgroup.wait () through a loop traversal constant set to end when the processing of the vehicle data of the n vehicle brands is finished; in the process of asynchronously and parallelly processing the vehicle data of each vehicle brand through n procedures, the remote diagnosis device can circularly execute batch search operation on the original vehicle database through SQL sentences by utilizing the procedure corresponding to each vehicle brand until all the vehicle data of the vehicle brand in the original vehicle database are determined to be searched, so that all the vehicle data of the vehicle brand are obtained. The method for acquiring the brand vehicle database corresponding to each vehicle brand is the same as the above description method, and the repetition is omitted.
Finally, the remote diagnosis device obtains a product vehicle database corresponding to the device type based on the brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnosis device of the device type in the brand vehicle databases corresponding to different vehicle brands aiming at each device type. The method for acquiring the product vehicle database corresponding to each equipment type is the same as the above description method, and the repetition is omitted.
It is worth mentioning that, in the embodiment of the present application, for each equipment type, when the remote diagnosis device obtains the product vehicle database corresponding to the equipment type based on the brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnosis device of the equipment type in the brand vehicle databases corresponding to different vehicle brands, the remote diagnosis device may also respectively add index data for the vehicle data of different vehicle types corresponding to each vehicle brand in the product vehicle database corresponding to the equipment type based on the equipment type, each vehicle brand supported by the vehicle diagnosis device of the equipment type and different vehicle types corresponding to each vehicle brand; the data structure of each index data in the product vehicle database corresponding to the equipment type is a tree structure (e.g. a b+ tree structure). Thus, referring to fig. 2b, when the vehicle diagnostic device initiates a data query request, a data query is performed based on the brand vehicle database corresponding to each vehicle brand in the product vehicle database corresponding to the device type of the vehicle diagnostic device, so that quick and accurate query of vehicle data can be realized, index data is respectively established for the vehicle data in the product vehicle database corresponding to the different device type, and query is performed in the product vehicle database and the brand vehicle database by using the index data, so that the query efficiency of the vehicle data can be further improved, and further the vehicle diagnostic efficiency can be improved. Specifically, referring to fig. 3, in the embodiment of the present application, the vehicle data query and vehicle diagnosis operations include:
Step 301: the remote diagnosis device receives a data query request initiated by the vehicle diagnosis device aiming at a target vehicle; the data query request comprises the equipment type of the vehicle diagnosis equipment, the vehicle brand of the target vehicle and the vehicle type.
In practical application, a vehicle maintenance engineer may perform a data query operation on a vehicle diagnostic device for a target vehicle, the vehicle diagnostic device generates a data query request based on a device type of the vehicle diagnostic device, a vehicle brand of the target vehicle, and a vehicle type in response to the data query operation for the target vehicle, and sends the data query request to a remote diagnostic device through a remote relay server, and the remote diagnostic device may receive the data query request initiated by the vehicle diagnostic device for the target vehicle.
Step 302: the remote diagnosis device determines a product vehicle database corresponding to the vehicle diagnosis device based on the device type of the vehicle diagnosis device, and queries vehicle data of the target vehicle from the product vehicle database corresponding to the vehicle diagnosis device based on the vehicle brand and the vehicle type of the target vehicle.
In practical application, the remote diagnosis device may determine index data of a vehicle brand based on the vehicle brand of the target vehicle, and query a brand vehicle database corresponding to the vehicle brand from product vehicle databases corresponding to the vehicle diagnosis device based on the index data of the vehicle brand; and determining index data of the vehicle type based on the vehicle type of the target vehicle, and inquiring vehicle data of the target vehicle from a brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type. Specifically, when the remote diagnosis device queries the vehicle data of the target vehicle from the brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type, in order to reduce the difficulty of data query and improve the data query efficiency, the remote diagnosis device may perform the paging query operation on the brand vehicle database corresponding to the vehicle brand in a circulating manner based on the index data of the vehicle type until the vehicle data of the target vehicle is queried.
Step 303: the remote diagnostic device packages the vehicle data of the target vehicle into a data query response and returns the data query response to the vehicle diagnostic device for display.
In practical applications, the remote diagnosis device may send the data query response to the vehicle diagnosis device through the remote transit server, the vehicle diagnosis device may perform a pagination display on the vehicle data of the target vehicle in the data query response when receiving the data query response, for example, 20-30 pieces of vehicle data per page, and the vehicle diagnosis device may further provide a screening function during pagination display, so that a vehicle maintenance engineer may view and screen the vehicle data of the target vehicle.
Step 304: the remote diagnosis device receives a vehicle diagnosis request initiated by the vehicle diagnosis device aiming at a target vehicle; wherein the vehicle diagnostic request includes monitoring data of the target vehicle.
In practical application, the vehicle diagnosis device may perform vehicle diagnosis on the target vehicle based on the vehicle data of the target vehicle returned by the remote diagnosis device, or may collect the monitoring data of the target vehicle when it is determined that the remote diagnosis device is needed to assist in diagnosis, and after generating a vehicle diagnosis request based on the monitoring data of the target vehicle, send the vehicle diagnosis request to the remote diagnosis device through the remote transfer server, where the remote diagnosis device may receive the vehicle diagnosis request initiated by the vehicle diagnosis device for the target vehicle.
Step 305: the remote diagnosis device obtains a vehicle diagnosis result of the target vehicle by using the vehicle diagnosis model based on the monitoring data of the target vehicle and the vehicle data of the target vehicle in the product vehicle database corresponding to the vehicle diagnosis device.
In practical application, the remote diagnosis device may train the initial vehicle diagnosis model in advance based on the historical monitoring data, the historical vehicle data and the standard fault types of different vehicle types corresponding to different vehicle brands, so as to obtain the vehicle diagnosis model. Specifically, the remote diagnosis device may take the historical monitoring data, the historical vehicle data and the standard fault types of different vehicle types corresponding to different vehicle brands as a training sample set, and perform iterative training on the initial vehicle diagnosis model based on the training sample set until determining that the iteration termination condition is met (for example, the number of iterative training times reaches the set number of times), and determine the vehicle diagnosis model based on each model parameter of the initial vehicle diagnosis model obtained by the last iterative training; wherein each iterative training comprises: selecting a target training sample from the training sample set, inputting historical monitoring data and historical vehicle data in the target training sample into an initial vehicle diagnosis model to obtain a predicted fault type, obtaining a current loss value based on the predicted fault type and a standard fault type in the target training sample, and updating each model parameter of the initial vehicle diagnosis model based on the current loss value. Further, after the training of the vehicle diagnosis model is completed, the remote diagnosis device may perform vehicle diagnosis on the target vehicle based on the vehicle diagnosis model, and specifically, may input the monitoring data of the target vehicle and the vehicle data of the target vehicle in the product vehicle database corresponding to the vehicle diagnosis device into the vehicle diagnosis model, so as to obtain a vehicle diagnosis result of the target vehicle.
Step 306: the remote diagnosis device packages the vehicle diagnosis result of the target vehicle into a vehicle diagnosis response, and returns the vehicle diagnosis response to the vehicle diagnosis device for display.
In practical application, the remote diagnosis device may send the vehicle diagnosis response to the vehicle diagnosis device through the remote transit server, and the vehicle diagnosis device may display the vehicle diagnosis result of the target vehicle in the vehicle diagnosis response after receiving the vehicle diagnosis response, so that a vehicle maintenance engineer may check the vehicle diagnosis result of the target vehicle.
Based on the foregoing embodiments, the embodiments of the present application further provide a data processing apparatus, where the data processing apparatus may be applied to a remote diagnostic device, and referring to fig. 4, the data processing apparatus 400 provided in the embodiments of the present application includes at least:
the data classification unit 401 is configured to, for each vehicle brand, classify vehicle data of the vehicle brand based on a data classification model corresponding to the vehicle brand by using a co-range corresponding to the vehicle brand, so as to obtain vehicle data of different vehicle types corresponding to the vehicle brand; the data classification model corresponding to the vehicle brand is a machine learning model which is obtained by training based on historical vehicle data of the vehicle brand and standard vehicle types of the historical vehicle data and is used for classifying the vehicle data corresponding to the vehicle brand according to the vehicle types;
A brand establishment unit 402, configured to obtain, for each vehicle brand, a brand vehicle database corresponding to the vehicle brand based on vehicle data of different vehicle types corresponding to the vehicle brand by using a co-range corresponding to the vehicle brand;
the product creation unit 403 is configured to obtain, for each device type, a product vehicle database corresponding to the device type based on brand vehicle databases corresponding to respective vehicle brands supported by vehicle diagnostic devices of the device type in brand vehicle databases corresponding to different vehicle brands.
In one possible implementation, before classifying the vehicle data of each vehicle brand based on the data classification model corresponding to the vehicle brand by using the co-ordinates corresponding to the vehicle brand, the data classification unit 401 is further configured to:
for each vehicle brand, circularly executing batch searching operation on the original vehicle database by utilizing the coroutine corresponding to the vehicle brand until all vehicle data searching of the vehicle brand in the original vehicle database is determined to be completed; wherein the batch retrieval operation comprises: and generating SQL sentences based on the original database identification, the current query starting identification and the current query target quantity, and querying the vehicle data of the vehicle brand from the original vehicle database through the SQL sentences.
In one possible implementation manner, the data processing apparatus 400 provided in the embodiment of the present application further includes:
an index adding unit 404, configured to add index data to vehicle data of different vehicle types corresponding to respective vehicle brands in a product vehicle database corresponding to the device type, based on the device type, respective vehicle brands supported by the device type and different vehicle types corresponding to the respective vehicle brands; the data structure of each index data in the product vehicle database corresponding to the equipment type is a tree structure.
In one possible implementation manner, the data processing apparatus 400 provided in the embodiment of the present application further includes:
a data query unit 405, configured to receive a data query request initiated by a vehicle diagnostic device for a target vehicle; the data query request comprises the equipment type of the vehicle diagnosis equipment, the vehicle brand of the target vehicle and the vehicle type; determining a product vehicle database corresponding to the vehicle diagnosis equipment based on the equipment type of the vehicle diagnosis equipment, and inquiring vehicle data of the target vehicle from the product vehicle database corresponding to the vehicle diagnosis equipment based on the vehicle brand and the vehicle type of the target vehicle; and packaging the vehicle data of the target vehicle into a data query response, and returning the data query response to the vehicle diagnostic equipment for display.
In one possible implementation manner, when the vehicle data of the target vehicle is queried from the product vehicle database corresponding to the vehicle diagnostic device based on the vehicle brand and the vehicle type of the target vehicle, the data query unit 405 is specifically configured to:
determining index data of a vehicle brand based on the vehicle brand of the target vehicle, and inquiring a brand vehicle database corresponding to the vehicle brand from product vehicle databases corresponding to the vehicle diagnostic equipment based on the index data of the vehicle brand;
and determining index data of the vehicle type based on the vehicle type of the target vehicle, and inquiring the vehicle data of the target vehicle from a brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type.
In one possible implementation, when the vehicle data of the target vehicle is queried from the brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type, the data query unit 405 is specifically configured to:
and based on the index data of the vehicle type, performing paging inquiry operation on the brand vehicle database corresponding to the vehicle brand in a circulating way until the vehicle data of the target vehicle is inquired.
In one possible implementation manner, the data processing apparatus 400 provided in the embodiment of the present application further includes:
A vehicle diagnostic unit 406 for receiving a vehicle diagnostic request initiated by a vehicle diagnostic device for a target vehicle; the vehicle diagnosis request comprises monitoring data of a target vehicle; acquiring a vehicle diagnosis result of the target vehicle by using a vehicle diagnosis model based on the monitoring data of the target vehicle and the vehicle data of the target vehicle in a product vehicle database corresponding to the vehicle diagnosis equipment; the vehicle diagnosis model is a machine learning model which is obtained by training based on historical monitoring data, historical vehicle data and standard fault types of different vehicle types corresponding to different vehicle brands and is used for carrying out fault diagnosis on the target vehicle according to the fault types.
It should be noted that, the principle of the data processing apparatus 400 provided in the embodiment of the present application to solve the technical problem is similar to that of the data processing method provided in the embodiment of the present application, so that the implementation of the data processing apparatus 400 provided in the embodiment of the present application may refer to the implementation of the data processing method provided in the embodiment of the present application, and the repetition is omitted.
After the data processing method and device provided by the embodiment of the application are introduced, the electronic device provided by the embodiment of the application is briefly introduced.
Referring to fig. 5, an electronic device 500 provided in an embodiment of the present application at least includes: the data processing method provided in the embodiment of the present application is implemented by the processor 501, the memory 502, and a computer program stored in the memory 502 and executable on the processor 501 when the processor 501 executes the computer program.
The electronic device 500 provided by embodiments of the present application may also include a bus 503 that connects the different components, including the processor 501 and the memory 502. Where bus 503 represents one or more of several types of bus structures, including a memory bus, a peripheral bus, a local bus, and so forth.
The Memory 502 may include readable storage media in the form of volatile Memory, such as random access Memory (Random Access Memory, RAM) 5021 and/or cache Memory 5022, and may further include Read Only Memory (ROM) 5023. The memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, the program modules 5024 including, but not limited to, an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 501 may be one processing element or a collective term for a plurality of processing elements, for example, the processor 501 may be a central processing unit (Central Processing Unit, CPU) or one or more integrated circuits configured to implement the data processing method provided in the embodiments of the present application. In particular, the processor 501 may be a general purpose processor including, but not limited to, a CPU, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), an off-the-shelf programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
The electronic device 500 may communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), with one or more devices that enable a user to interact with the electronic device 500 (e.g., cell phone, computer, etc.), and/or with any device that enables the electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). Such communication may be through an Input/Output (I/O) interface 505. Also, electronic device 500 may communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network (Wide Area Network, WAN), and/or a public network such as the internet via network adapter 506. As shown in fig. 5, network adapter 506 communicates with other modules of electronic device 500 over bus 503. It should be appreciated that although not shown in fig. 5, other hardware and/or software modules may be used in connection with electronic device 500, including, but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) subsystems, tape drives, data backup storage subsystems, and the like.
It should be noted that the electronic device 500 shown in fig. 5 is only an example, and should not impose any limitation on the functions and application scope of the embodiments of the present application.
The following describes a computer-readable storage medium provided in an embodiment of the present application. The computer readable storage medium provided in the embodiments of the present application stores computer instructions that, when executed by a processor, implement the data processing method provided in the embodiments of the present application. In particular, the computer instructions may be embedded or installed in a processor, so that the processor may implement the data processing method provided in the embodiments of the present application by executing the embedded or installed computer instructions.
In addition, the data processing method provided in the embodiment of the present application may also be implemented as a computer program product, where the computer program product includes a program code, and the program code implements the data processing method provided in the embodiment of the present application when the program code is run on a processor.
The computer program product provided by the embodiments of the present application may employ one or more computer-readable storage media, which may be, but are not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing, and more specific examples (a non-exhaustive list) of the computer-readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a RAM, a ROM, an erasable programmable read-Only Memory (Erasable Programmable Read Only Memory, EPROM), an optical fiber, a portable compact disk read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.
The computer program product provided by the embodiment of the application can adopt a CD-ROM and comprise program codes, and can also run on electronic devices such as remote diagnosis devices and the like. However, the computer program product provided by the embodiments of the present application is not limited thereto, and the computer readable storage medium may be any tangible medium that can contain, or store the program code for use by or in connection with the instruction execution system, apparatus, or device.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the elements described above may be embodied in one element in accordance with embodiments of the present application. Conversely, the features and functions of one unit described above may be further divided into a plurality of units to be embodied.
Furthermore, although the operations of the methods of the present application are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments of the present application without departing from the spirit and scope of the embodiments of the present application. Thus, if such modifications and variations of the embodiments of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to encompass such modifications and variations.
Claims (8)
1. A method of data processing, comprising:
creating n coroutines with waitgroup. Add (n), representing a process of waiting for vehicle data of n vehicle brands; wherein n vehicle brands make up a constant set; the vehicle data of each vehicle brand does not comprise the monitoring data of each vehicle corresponding to the vehicle brand;
processing the vehicle data of each vehicle brand asynchronously and parallelly through the n coroutines by using a program language go key, and determining waitgroup.wait () through circulating through the constant set to finish when the processing of the vehicle data of the n vehicle brands is finished; in the process of asynchronously and parallelly processing the vehicle data of each vehicle brand through the n cooperative programs, performing batch search operation on an original vehicle database circularly through SQL sentences by utilizing the cooperative program corresponding to the vehicle brand until the vehicle data of the vehicle brand are obtained after all the vehicle data of the vehicle brand in the original vehicle database are determined to be searched, and classifying the vehicle data of the vehicle brand based on the data classification model corresponding to the vehicle brand by utilizing the cooperative program corresponding to the vehicle brand to obtain the vehicle data of different vehicle types corresponding to the vehicle brand, wherein the data classification model corresponding to the vehicle brand is a machine learning model which is obtained by training based on the historical vehicle data of the vehicle brand and the standard vehicle type of the historical vehicle data and is used for classifying the vehicle data corresponding to the vehicle brand according to the vehicle type;
For each vehicle brand, utilizing a coroutine corresponding to the vehicle brand to obtain a brand vehicle database corresponding to the vehicle brand based on vehicle data of different vehicle types corresponding to the vehicle brand;
for each equipment type, obtaining a product vehicle database corresponding to the equipment type based on brand vehicle databases corresponding to various vehicle brands supported by vehicle diagnostic equipment of the equipment type in brand vehicle databases corresponding to different vehicle brands;
receiving a data query request initiated by vehicle diagnostic equipment aiming at a target vehicle; the data query request comprises the equipment type of the vehicle diagnosis equipment, the vehicle brand and the vehicle type of the target vehicle;
determining a product vehicle database corresponding to the vehicle diagnosis equipment based on the equipment type of the vehicle diagnosis equipment, and inquiring vehicle data of the target vehicle from the product vehicle database corresponding to the vehicle diagnosis equipment based on the vehicle brand and the vehicle type of the target vehicle;
and packaging the vehicle data of the target vehicle into a data query response, and returning the data query response to the vehicle diagnosis equipment for display.
2. The data processing method according to claim 1, wherein when obtaining the product vehicle database corresponding to the device type based on the brand vehicle database corresponding to each vehicle brand supported by the vehicle diagnostic device of the device type in the brand vehicle databases corresponding to different vehicle brands, further comprising:
adding index data to the vehicle data of different vehicle types corresponding to each vehicle brand in a product vehicle database corresponding to the equipment type based on the equipment type, each vehicle brand supported by the equipment type vehicle diagnosis equipment and different vehicle types corresponding to each vehicle brand; the data structure of each index data in the product vehicle database corresponding to the equipment type is a tree structure.
3. The data processing method according to claim 1, wherein querying vehicle data of the target vehicle from a product vehicle database corresponding to the vehicle diagnostic apparatus based on a vehicle brand and a vehicle type of the target vehicle, comprises:
determining index data of a vehicle brand based on the vehicle brand of the target vehicle, and inquiring a brand vehicle database corresponding to the vehicle brand from product vehicle databases corresponding to the vehicle diagnostic equipment based on the index data of the vehicle brand;
And determining index data of the vehicle type based on the vehicle type of the target vehicle, and inquiring the vehicle data of the target vehicle from a brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type.
4. The data processing method according to claim 1, wherein querying vehicle data of the target vehicle from a brand vehicle database corresponding to the vehicle brand based on the index data of the vehicle type, includes:
and based on the index data of the vehicle type, performing paging query operation on the brand vehicle database corresponding to the vehicle brand in a circulating way until the vehicle data of the target vehicle is queried.
5. The data processing method of claim 1, further comprising:
receiving a vehicle diagnosis request initiated by the vehicle diagnosis device aiming at the target vehicle; wherein the vehicle diagnostic request includes monitoring data of the target vehicle;
acquiring a vehicle diagnosis result of the target vehicle by using a vehicle diagnosis model based on the monitoring data of the target vehicle and the vehicle data of the target vehicle in a product vehicle database corresponding to the vehicle diagnosis equipment; the vehicle diagnosis model is a machine learning model which is obtained by training based on historical monitoring data, historical vehicle data and standard fault types of different vehicle types corresponding to different vehicle brands and is used for diagnosing faults of the target vehicle according to the fault types.
6. A data processing apparatus, comprising:
the data classification unit is used for creating n coroutines by utilizing waitgroup. Add (n), and representing the processing of vehicle data waiting for n vehicle brands, wherein the n vehicle brands form a constant set, and the vehicle data of each vehicle brand does not comprise monitoring data of each vehicle corresponding to the vehicle brand; processing the vehicle data of each vehicle brand asynchronously and parallelly through the n coroutines by using a program language go key, and determining waitgroup.wait () through circulating through the constant set to finish when the processing of the vehicle data of the n vehicle brands is finished; in the process of asynchronously and parallelly processing the vehicle data of each vehicle brand through the n cooperative programs, performing batch search operation on an original vehicle database circularly through SQL sentences by utilizing the cooperative program corresponding to the vehicle brand until the vehicle data of the vehicle brand are obtained after all vehicle data of the vehicle brand in the original vehicle database are determined to be searched, and classifying the vehicle data of the vehicle brand based on the data classification model corresponding to the vehicle brand by utilizing the cooperative program corresponding to the vehicle brand to obtain the vehicle data of different vehicle types corresponding to the vehicle brand, wherein the data classification model corresponding to the vehicle brand is a machine learning model which is obtained by training based on historical vehicle data of the vehicle brand and standard vehicle types of the historical vehicle data and is used for classifying the vehicle data corresponding to the vehicle brand according to the vehicle type;
The brand establishment unit is used for obtaining a brand vehicle database corresponding to each vehicle brand based on vehicle data of different vehicle types corresponding to the vehicle brand by utilizing a cooperative distance corresponding to the vehicle brand for each vehicle brand;
the product establishing unit is used for obtaining a product vehicle database corresponding to each equipment type based on brand vehicle databases corresponding to all vehicle brands supported by vehicle diagnostic equipment of the equipment type in brand vehicle databases corresponding to different vehicle brands aiming at each equipment type;
the vehicle diagnosis device comprises a data query unit, a data query unit and a data processing unit, wherein the data query unit is used for receiving a data query request initiated by vehicle diagnosis equipment aiming at a target vehicle, and the data query request comprises the equipment type of the vehicle diagnosis equipment, the vehicle brand of the target vehicle and the vehicle type; determining a product vehicle database corresponding to the vehicle diagnosis equipment based on the equipment type of the vehicle diagnosis equipment, and inquiring vehicle data of the target vehicle from the product vehicle database corresponding to the vehicle diagnosis equipment based on the vehicle brand and the vehicle type of the target vehicle; and packaging the vehicle data of the target vehicle into a data query response, and returning the data query response to the vehicle diagnosis equipment for display.
7. An electronic device, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the data processing method according to any of claims 1-5 when the computer program is executed.
8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, which when executed by a processor, implement the data processing method according to any of claims 1-5.
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