CN116703250B - Second-hand vehicle business supervision and prediction system - Google Patents
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Abstract
The application provides a second-hand vehicle service supervision and prediction system, which comprises a client information module, a vehicle information module, a service analysis and prediction module and a database module; the client information module is used for identifying and managing client information; the vehicle information module is used for acquiring and managing vehicle information; the business analysis prediction module is used for analyzing business related indexes; the system provides a method for quantitatively evaluating the operation condition and the business potential, can comprehensively and clearly display the business condition, and is beneficial to improving the business management efficiency; and meanwhile, sales state prediction is performed based on the operation index, the client preference data and the service potential representation index, so that a prediction result is more accurate, and a more effective analysis decision basis can be provided for a manager.
Description
Technical Field
The application relates to the field of digitalization, in particular to a second-hand car business supervision and prediction system.
Background
The second-hand car service is an important service block of the whole automobile market, and a plurality of second-hand car companies exist in the market. However, the current second-hand car companies lack systematic datamation means in second-hand car business management, and only rely on subjective cognition and experience to conduct second-hand car business objective planning and business management. Therefore, the problems of incomplete information record, data deviation errors and low processing speed easily occur in daily management planning, so that the efficiency is low, the resources are wasted, and even the decision is wrong.
Meanwhile, the existing general management system also lacks a systematic evaluation function for the service scene of the second hand vehicle, and cannot comprehensively and objectively evaluate the service. Therefore, in order to solve these problems, a second-hand vehicle service supervision and prediction system needs to be developed to improve management efficiency and accuracy, and to have a systematic evaluation function for the second-hand vehicle service, so as to promote development of the second-hand vehicle service.
Disclosure of Invention
In view of the above limitations, the present application provides a second-hand vehicle service supervision and prediction system, which can comprehensively and clearly display service conditions by providing a method for quantitatively evaluating the service conditions, the service potential and predicting sales.
In order to achieve the above purpose, the present application adopts the following technical scheme:
the system comprises a client information module, a vehicle information module, a service analysis prediction module and a database module;
the client information module is used for identifying and managing client information and consists of a client information identifying unit and a client information managing unit;
the customer information identification unit analyzes the customer characteristics through the graphic acquisition device to obtain user characteristic data; the client information management unit is used for managing client information;
after the customer information identification unit obtains the user characteristic data, the customer information management unit stores the user characteristic data into the database module; when the user characteristic data is updated, the updated user characteristic data is synchronized to the database module by the client information management unit;
the vehicle information module is used for acquiring and managing vehicle information and consists of a vehicle information acquisition unit and a vehicle information management unit;
the vehicle information acquisition unit is used for acquiring the vehicle information input by the vehicle information input terminal and storing the vehicle information into the database module by the vehicle information management unit; when the vehicle information is changed, the vehicle information management unit synchronizes the changed vehicle information to the database module;
the business analysis prediction module is used for analyzing business related indexes; the business analysis and prediction module consists of an operation index analysis unit, a customer feedback analysis unit, a business potential analysis unit and a business prediction analysis unit;
the operation index analysis unit is used for analyzing and calculating operation indexes according to the sales data; the client feedback analysis unit is used for obtaining client preference data based on the client feedback data; the business potential analysis unit is used for evaluating business potential index calculation of the second-hand vehicle manufacturer according to sales data; the business prediction analysis unit is used for predicting future sales conditions of the second-hand vehicle manufacturer.
Compared with the prior art, the application has the following advantages:
(1) The method for quantitatively evaluating the operation condition and the business potential can comprehensively and clearly display the business condition, and is beneficial to improving the business management efficiency;
(2) Sales state prediction is carried out based on the operation index, the client preference data and the service potential representation index, the prediction result is more accurate, and more effective analysis decision basis can be provided for a manager.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application, as well as the preferred embodiments thereof, together with the following detailed description of the application, given by way of illustration only, together with the accompanying drawings.
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Fig. 1 is a block diagram of a second-hand vehicle service supervision and prediction system according to an embodiment of the present application.
Fig. 2 is a block diagram of a service analysis prediction module according to an embodiment of the present application.
Description of the embodiments
Other advantages and advantages of the present application will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application. For a further understanding of the present application, the present application will be described in further detail with reference to the following preferred embodiments.
The application provides a second-hand vehicle business supervision and prediction system; referring to fig. 1, the second-hand car service supervision and prediction system of the present application has the following constitution in order to realize the functions thereof:
the system comprises a client information module, a vehicle information module, a business analysis and prediction module, a visual interface module, a data interface module and a database module.
The client information module is used for identifying and managing client information and consists of a client information identifying unit and a client information managing unit.
The customer information identification unit analyzes the customer characteristics through the graphic acquisition device to obtain user characteristic data; the client information management unit is used for managing client information;
after the customer information identification unit obtains the user characteristic data, the customer information management unit stores the user characteristic data into the database module; when the user characteristic data is updated, the updated user characteristic data is synchronized to the database module by the client information management unit.
The vehicle information module is used for acquiring and managing vehicle information and consists of a vehicle information acquisition unit and a vehicle information management unit;
the vehicle information acquisition unit is used for acquiring the vehicle information input by the vehicle information input terminal and storing the vehicle information into the database module by the vehicle information management unit; when the vehicle information is changed, the changed vehicle information is synchronized to the database module by the vehicle information management unit.
The business analysis prediction module is used for analyzing business related indexes; referring to fig. 2, the business analysis prediction module is composed of a business index analysis unit, a customer feedback analysis unit, a business potential analysis unit, and a business prediction analysis unit.
The operation index analysis unit is used for analyzing and calculating operation indexes according to the sales data; the client feedback analysis unit is used for obtaining client preference data based on the client feedback data; the business potential analysis unit is used for evaluating business potential index calculation of the second-hand vehicle manufacturer according to sales data; the business prediction analysis unit is used for predicting future sales conditions of the second-hand vehicle manufacturer.
The visual interface module is used for representing the analysis result of the business analysis prediction module in the form of a visual chart and an operation interface.
The data interface module is a data input and output functional module provided by the system.
The database module is used for storing relevant data depending on the system.
As an embodiment, the operation index analysis unit calculates the operation index including:
s11, obtaining a target analysis range; the target analysis range is composed of a target time period, a target sales area, a target vehicle type and a target sales store;
s12, calling a vehicle information module, and obtaining vehicle data which is sold in a sales state and belongs to a target analysis range from a database to obtain first vehicle data;
s13, calculating an operation index in the target analysis range based on the first vehicle data.
Further, the operation index includes: sales volume, sales amount, gross profit margin, inventory turnover, customer satisfaction.
The sales volume represents the number of second hand carts sold in a certain time, calculated by:
and counting the number of data pieces of the first vehicle data to obtain the sales volume in the target time period.
The sales represent the total sales amount over time, calculated by:
and summing all sales in the first vehicle data to obtain sales in a target time period.
The gross profit represents the total profit of the seller during the sales process, calculated by:
and summing all purchase amounts in the first vehicle data to obtain a cost amount, and subtracting the cost amount from the sales amount to obtain gross profit.
The gross profit is the percentage of gross profit to sales.
The inventory turnover rate represents an inventory turnover speed, calculated by:
obtaining the data quantity of which the sales state is 'unsold' when the target analysis time period starts and ends, and obtaining a first stock number and a second stock number;
calculating the average stock number of the first stock number and the second stock number;
the ratio of sales to average stock number is the stock turnover rate.
The customer satisfaction represents the customer's satisfaction with the vendor's service, calculated by:
and calculating the average value, the maximum value and the minimum value of the satisfaction degree scores in the first vehicle data.
As an embodiment, the customer feedback analysis unit obtains customer preference data by:
s21, collecting customer feedback data, and preprocessing a text to obtain first evaluation data;
the customer feedback data consists of evaluation data of a plurality of customers; the evaluation data comprise a plurality of service evaluation indexes and corresponding customer evaluation texts;
s22, carrying out emotion preference analysis on the client evaluation text of each client to obtain second evaluation data;
the second evaluation data consists of index preference of a plurality of clients, wherein the index preference refers to emotion preference degree of a plurality of business evaluation indexes;
s23, calculating a preference characteristic value of each service evaluation index according to the second evaluation data to obtain client preference data; the client preference data consists of a plurality of service evaluation indexes and corresponding preference characteristic values.
Further, the service evaluation index consists of a vehicle evaluation index and a sales evaluation index; the vehicle evaluation index is an evaluation index of the pointer to the vehicle condition experience; the sales evaluation index is an evaluation index of a pointer to the sales process of the second hand vehicle.
Further, emotion preference analysis in S22 is realized by a text emotion analysis method based on deep learning, namely, a client evaluation text is converted into a text vector, and emotion preference is obtained by reasoning through an emotion preference model; the emotion preference model is a classification model trained based on a deep learning algorithm.
The emotion preference degree comprises five categories of positive strong, positive general, neutral, negative general and negative strong, and corresponding numerical labels are respectively 10, 8, 5, 3 and 0.
Further, the calculating mode of the preference characteristic value of the service evaluation index in S23 is as follows:
wherein the method comprises the steps ofβ i Is numbered asiA preference characteristic value of the business evaluation index,α j is numbered asjIs used to determine the influence degree weight of the client,w i,j is numbered asjCustomer pair number ofiEmotion preference degree value tags of the business evaluation index.
As an embodiment, the calculating the business potential index by the business potential analysis unit includes:
s31, calling a client information module, and screening client information with a new client mark from the client information module to obtain first client data;
s32, calculating a service potential representation index according to the first customer data.
Further, the service potential characterization index comprises a recommended evaluation rate, an evaluation conversion rate, a comprehensive replacement rate and a replacement rate of the product.
The recommendation evaluation rate refers to the proportion of the number of clients evaluated by the price of the recommended second-hand vehicle to the total number of clients; the total number of clients is derived from the number of data pieces of the first client data, and the number of clients to be recommended for the second-hand car price estimation uses the total number of tag data having "recommended price estimation" in the first client data.
The evaluation conversion rate refers to the proportion of the number of the clients completing replacement acquisition to the number of recommended second-hand car price evaluation clients; the number of customers who complete the replacement acquisition uses the total number of label data having both "recommended price assessment" and "complete replacement acquisition" in the first customer data.
The comprehensive replacement rate refers to the proportion of the number of vehicles completing replacement acquisition to the sales number of new vehicles; the number of vehicles completing the replacement acquisition is consistent with the number of clients completing the replacement acquisition, and the sales number of the new vehicles is obtained from the sales data.
The replacement rate of the product refers to the ratio of the number of the brand vehicles authorized by the dealer to replace the brand vehicles with the same brand vehicles to the sales number of the new vehicles.
As an embodiment, the calculating the business potential index by the business potential analysis unit further includes:
s33, obtaining a business potential grade result through a business potential evaluation model by means of business potential representation indexes, operation indexes and dealer basic characteristic data;
the business potential evaluation model is a classification model trained based on a machine learning algorithm.
Further, the operation index is obtained through the operation index analysis unit.
Further, the dealer fundamental characteristic data includes: dealer rank, dealer brand rank, dealer area rank, dealer personnel number, dealer personnel ratio, number of sales vehicle types, number of inventory vehicles, exhibition hall area rank, number of after-sales service items.
Further, the business potential evaluation model is obtained by:
collecting business potential evaluation data and cleaning the data, wherein the business potential evaluation data consists of business potential characterization index data, corresponding dealer basic feature data and business potential grade evaluation results;
dividing business potential evaluation data into a potential evaluation model training set and a potential evaluation model test set; model training is carried out by means of a potential evaluation model training set by means of a machine learning algorithm;
and performing model performance evaluation through the potential evaluation model test set, and performing parameter optimization according to the performance evaluation result to obtain a business potential evaluation model.
Further, the machine learning algorithm can adopt any one algorithm of logistic regression, naive Bayes, nearest neighbor, decision tree and support vector machine.
The machine learning algorithm is a mature technical means in the field, and can be implemented smoothly according to the description of the foregoing embodiments by a person skilled in the art, and is not repeated here.
As an embodiment, the business prediction analysis unit predicts future sales of the dealer by:
s41, calling an operation index analysis unit to obtain an operation index in a preset time period, and obtaining first characteristic data;
s42, calling a customer feedback analysis unit to obtain customer preference data in a preset time period, and obtaining second characteristic data;
s43, calling a service potential analysis unit to obtain a service potential representation index in a preset time period, and obtaining third characteristic data;
s44, combining the first characteristic data, the second characteristic data and the third characteristic data, and carrying out data standardization processing to obtain fourth characteristic data;
s45, inputting the fourth characteristic data into a sales prediction model to obtain a sales prediction result.
The sales prediction model is a regression model obtained by training a deep neural network; the sales prediction result is estimated sales and sales quantity.
Further, the deep neural network consists of an input layer, a first hidden layer, a second hidden layer, an LSTM layer, an attention layer, a full connection layer and an output layer;
the input layer is used for receiving fourth characteristic data;
the first hidden layer is a 1D convolution layer and is used for extracting local features;
the second hidden layer is a large convolution layer and is used for capturing global characteristics;
a pooling layer is also arranged between the first hidden layer and the second hidden layer, and is used for reducing the size of the feature map;
the LSTM layer is used for processing time series data and capturing trend characteristics;
a batch normalization layer is also arranged between the second hidden layer and the LSTM layer, and is used for accelerating training and improving the stability of the model;
the attention layer is used for weighting different features so as to improve the attention degree of key information;
the full connection layer is used for processing the features learned by the previous layer;
the output layer is used for outputting sales conditions.
As an embodiment, the system further comprises an intelligent decision module for matching business optimization suggestions according to the business index, the business potential characterization index and the sales prediction result.
The matching service optimization proposal is specifically realized by the following modes:
setting index standard thresholds of all indexes, and setting corresponding service optimization results according to the combination of the index standard thresholds to obtain a service optimization template;
and matching corresponding business optimization results according to the business optimization templates, the business indexes, the business potential representation indexes and the sales prediction results.
As one example, the methods of the present application may be implemented in software and/or a combination of software and hardware, e.g., using an Application Specific Integrated Circuit (ASIC), a general purpose computer, or any other similar hardware device.
The method of the present application may be implemented in the form of a software program that is executable by a processor to perform the steps or functions described above. Likewise, the software programs (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like.
In addition, some steps or functions of the methods described herein may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
Furthermore, parts of the methods of the present application may be applied as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide the methods and/or solutions according to the present application by way of operation of the computer. Program instructions for invoking the methods of the application may be stored in fixed or removable recording media and/or transmitted via a data stream in a broadcast or other signal bearing medium and/or stored within a working memory of a computer device operating according to the program instructions.
As an embodiment, the present application also provides an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to run a method and/or a solution according to the previous embodiments.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present application.
Finally, it is pointed out that in the present document relational terms such as first and second, and the like, are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal device comprising the element.
In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be regarded as not exist and not within the scope of the application claimed.
The present application is not limited to the above-mentioned embodiments, but is intended to be limited to the following embodiments, and any modifications, equivalents and modifications can be made to the above-mentioned embodiments without departing from the scope of the application.
Claims (8)
1. A second-hand vehicle business supervision and prediction system is characterized in that,
the system comprises a client information module, a vehicle information module, a business analysis and prediction module and a database module;
the client information module is used for identifying and managing client information and consists of a client information identifying unit and a client information managing unit;
the customer information identification unit analyzes the customer characteristics through the graphic acquisition device to obtain user characteristic data; the client information management unit is used for managing client information;
after the customer information identification unit obtains the user characteristic data, the customer information management unit stores the user characteristic data into the database module; when the user characteristic data is updated, the updated user characteristic data is synchronized to the database module by the client information management unit;
the vehicle information module is used for acquiring and managing vehicle information and consists of a vehicle information acquisition unit and a vehicle information management unit;
the vehicle information acquisition unit is used for acquiring the vehicle information input by the vehicle information input terminal and storing the vehicle information into the database module by the vehicle information management unit; when the vehicle information is changed, the vehicle information management unit synchronizes the changed vehicle information to the database module;
the business analysis prediction module is used for analyzing business related indexes; the business analysis and prediction module consists of an operation index analysis unit, a customer feedback analysis unit, a business potential analysis unit and a business prediction analysis unit;
the operation index analysis unit is used for analyzing and calculating operation indexes according to sales data; the client feedback analysis unit is used for obtaining client preference data based on the client feedback data; the business potential analysis unit is used for evaluating business potential index calculation of the second-hand vehicle manufacturer according to sales data; the business prediction analysis unit is used for predicting future sales conditions of a second-hand vehicle manufacturer;
the business prediction analysis unit predicts future sales conditions of the dealer and is realized by the following steps:
s41, calling an operation index analysis unit to obtain an operation index in a preset time period, and obtaining first characteristic data;
s42, calling a customer feedback analysis unit to obtain customer preference data in a preset time period, and obtaining second characteristic data;
s43, calling a service potential analysis unit to obtain a service potential representation index in a preset time period, and obtaining third characteristic data;
s44, combining the first characteristic data, the second characteristic data and the third characteristic data, and carrying out data standardization processing to obtain fourth characteristic data;
s45, inputting the fourth characteristic data into a sales prediction model to obtain a sales prediction result;
the sales prediction model is a regression model obtained by training a deep neural network;
the deep neural network consists of an input layer, a first hidden layer, a second hidden layer, an LSTM layer, an attention layer, a full connection layer and an output layer;
the input layer is used for receiving fourth characteristic data;
the first hidden layer is a 1D convolution layer and is used for extracting local features;
the second hidden layer is a large convolution layer and is used for capturing global characteristics;
a pooling layer is also arranged between the first hidden layer and the second hidden layer, and is used for reducing the size of the feature map;
the LSTM layer is used for processing time series data and capturing trend characteristics;
a batch normalization layer is also arranged between the second hidden layer and the LSTM layer, and is used for accelerating training and improving the stability of the model;
the attention layer is used for weighting different features so as to improve the attention degree of key information;
the full connection layer is used for processing the features learned by the previous layer;
the output layer is used for outputting sales conditions.
2. The second-hand truck business supervision and prediction system according to claim 1,
the operation index analysis unit calculates an operation index including:
s11, obtaining a target analysis range; the target analysis range is composed of a target time period, a target sales area, a target vehicle type and a target sales store;
s12, calling a vehicle information module, and obtaining vehicle data which is sold in a sales state and belongs to a target analysis range from a database to obtain first vehicle data;
s13, calculating an operation index in the target analysis range based on the first vehicle data.
3. The second-hand truck business supervision and prediction system according to claim 2, wherein,
the operation index comprises: sales volume, sales amount, gross profit margin, inventory turnover, customer satisfaction.
4. The second-hand truck business supervision and prediction system according to claim 1,
the customer feedback analysis unit obtains customer preference data by:
s21, collecting customer feedback data, and preprocessing a text to obtain first evaluation data;
the customer feedback data consists of evaluation data of a plurality of customers; the evaluation data comprise a plurality of service evaluation indexes and corresponding customer evaluation texts;
s22, carrying out emotion preference analysis on the client evaluation text of each client to obtain second evaluation data;
the second evaluation data consists of index preference of a plurality of clients, wherein the index preference refers to emotion preference degree of a plurality of business evaluation indexes;
s23, calculating a preference characteristic value of each service evaluation index according to the second evaluation data to obtain client preference data; the client preference data consists of a plurality of service evaluation indexes and corresponding preference characteristic values.
5. The second-hand truck business supervision and prediction system according to claim 4,
the emotion preference analysis in S22 is realized by a text emotion analysis method based on deep learning, and the specific mode is as follows:
converting the customer evaluation text into a text vector, and reasoning by means of the emotion preference model to obtain emotion preference degree; the emotion preference model is a classification model obtained based on deep learning algorithm training;
the emotion preference degree comprises five categories of positive strong, positive general, neutral, negative general and negative strong, and corresponding numerical labels are respectively 10, 8, 5, 3 and 0.
6. The second-hand truck business supervision and prediction system according to claim 4,
the calculation mode of the preference characteristic value of the service evaluation index in S23 is as follows:；
wherein the method comprises the steps ofβ i Is numbered asiIs of (1)The preference characteristic value of the evaluation index,α j is numbered asjIs used to determine the influence degree weight of the client,w i,j is numbered asjCustomer pair number ofiEmotion preference degree value tags of the business evaluation index.
7. The second-hand truck business supervision and prediction system according to claim 1,
the business potential analysis unit performs business potential index calculation including:
s31, calling a client information module, and screening client information with a new client mark from the client information module to obtain first client data;
s32, calculating a service potential representation index according to the first customer data;
the service potential characterization indexes comprise recommended evaluation rate, evaluation conversion rate, comprehensive replacement rate and replacement rate of the product;
the recommendation evaluation rate refers to the proportion of the number of clients evaluated by the price of the recommended second-hand vehicle to the total number of clients;
the evaluation conversion rate refers to the proportion of the number of the clients completing replacement acquisition to the number of recommended second-hand car price evaluation clients;
the comprehensive replacement rate refers to the proportion of the number of vehicles completing replacement acquisition to the sales number of new vehicles;
the replacement rate of the product refers to the ratio of the number of the brand vehicles authorized by the dealer to replace the brand vehicles with the same brand vehicles to the sales number of the new vehicles.
8. The second-hand truck business supervision and prediction system according to claim 1,
the business potential analysis unit performs business potential index calculation further comprises:
s33, obtaining a business potential grade result through a business potential evaluation model by means of business potential representation indexes, operation indexes and dealer basic characteristic data; the business potential evaluation model is a classification model trained based on a machine learning algorithm.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108876085A (en) * | 2018-04-12 | 2018-11-23 | 合肥天源迪科信息技术有限公司 | A kind of enterprise marketing support platform Internet-based |
| CN110599256A (en) * | 2019-09-18 | 2019-12-20 | 深圳前海微众银行股份有限公司 | Automobile sales prediction method and device, terminal equipment and storage medium |
| CN114565399A (en) * | 2022-01-20 | 2022-05-31 | 黄华波 | Product sales prediction method applied to electronic commerce |
| CN114912948A (en) * | 2022-04-24 | 2022-08-16 | 深圳船奇科技有限公司 | Cloud service-based cross-border e-commerce big data intelligent processing method, device and equipment |
| CN115409039A (en) * | 2022-08-25 | 2022-11-29 | 中国第一汽车股份有限公司 | Standard vehicle type data analysis method and device, electronic equipment and medium |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN108876085A (en) * | 2018-04-12 | 2018-11-23 | 合肥天源迪科信息技术有限公司 | A kind of enterprise marketing support platform Internet-based |
| CN110599256A (en) * | 2019-09-18 | 2019-12-20 | 深圳前海微众银行股份有限公司 | Automobile sales prediction method and device, terminal equipment and storage medium |
| CN114565399A (en) * | 2022-01-20 | 2022-05-31 | 黄华波 | Product sales prediction method applied to electronic commerce |
| CN114912948A (en) * | 2022-04-24 | 2022-08-16 | 深圳船奇科技有限公司 | Cloud service-based cross-border e-commerce big data intelligent processing method, device and equipment |
| CN115409039A (en) * | 2022-08-25 | 2022-11-29 | 中国第一汽车股份有限公司 | Standard vehicle type data analysis method and device, electronic equipment and medium |
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