A support vector regression-based new energy vehicle sales prediction method, system, device and readable storage medium

Abstract

The application discloses a new energy vehicle sales volume prediction method, system, device and readable storage medium based on support vector regression, comprising the following steps: acquiring new energy vehicle sales data and new energy vehicle data; performing data preprocessing on the acquired new energy vehicle sales data and new energy vehicle data; extracting feature data of the new energy vehicle data; dividing the data set into a training set, a verification set and a test set; introducing a support vector regression algorithm model, training the support vector regression algorithm model by using the training data set; evaluating the performance of the support vector regression algorithm model by using the verification data set, optimizing the model according to the evaluation result; and predicting the new energy vehicle sales volume by using the trained support vector regression algorithm model on the test data set or new data. By using the support vector regression algorithm, the application comprehensively considers vehicle data and geographical position information, and realizes accurate prediction and evaluation of the new energy vehicle sales volume.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of automobiles, and specifically to a new energy vehicle sales volume prediction method, system, device and readable storage medium based on support vector regression. BACKGROUND

[0002] Automobile sales volume prediction has a profound impact on automobile manufacturers, sellers, and investors. Accurate automobile sales volume prediction helps automobile manufacturers and sellers develop reasonable production plans and optimize inventory management. Manufacturers can adjust production lines based on prediction results to ensure that vehicle production matches market demand, avoiding overstock or shortage situations. This is crucial for improving production efficiency, reducing costs, and maintaining good supply chain relationships. Automobile sales volume prediction results can also help businesses identify potential market risks and develop appropriate risk response measures to reduce operational risks.

[0003] Existing automobile sales volume prediction methods use statistical analysis or regression analysis methods, but the accuracy of this method is not high, the application range is limited by the amount of data, and a large amount of data is required. When the data quality is not high or the market changes greatly, the prediction results will have a large deviation.

[0004] In summary, the existing automobile sales volume prediction method has the problems of low accuracy, limited application range by data volume, and large deviation in prediction results. SUMMARY

[0005] To solve the problems in the prior art, the application provides a new energy vehicle sales volume prediction method, system, device and readable storage medium based on support vector regression, which is used to solve the problems in the background art. By using the support vector regression algorithm, vehicle data and geographic location information are considered comprehensively to achieve accurate prediction and evaluation of new energy vehicle sales volume.

[0006] To achieve the above-mentioned purpose, the application provides the following technical solutions:

[0007] A new energy vehicle sales volume prediction method based on support vector regression includes the following steps:

[0008] Step 1, obtaining new energy vehicle sales data and new energy vehicle data;

[0009] Step 2, data preprocessing of the new energy vehicle sales data and new energy vehicle data obtained in step 1;

[0010] Step 3, extracting feature data of the new energy vehicle data in step 1;

[0011] Step 4, dividing the data set into a training set, a validation set and a test set;

[0012] Step 5, introduce the support vector regression algorithm model, use the training data set in step 4 to train the support vector regression algorithm model;

[0013] Step 6, use the validation data set in step 4 to evaluate the performance of the support vector regression algorithm model, and optimize the model according to the evaluation results;

[0014] Step 7, use the trained support vector regression algorithm model to predict the sales of new energy vehicles for the test data set or new data.

[0015] Preferably, in step 1, the new energy vehicle sales data includes sales volume and date; the new energy vehicle data includes vehicle mileage, battery capacity, charging times and geographic location information.

[0016] Preferably, in step 2, the data preprocessing includes cleaning the data obtained in step 1, removing outliers and duplicates; then filling in missing values, normalizing or normalizing the data to ensure that the magnitudes of different data are consistent.

[0017] Preferably, in step 3, the feature data includes vehicle driving characteristics, battery usage characteristics and geographic location characteristics;

[0018] The vehicle driving characteristics include average speed, driving time and driving distance; the battery usage characteristics include charging times and average charging time; the geographic location characteristics include city population density and charging device quantity.

[0019] Preferably, in step 4, the training set, validation set and test set are divided according to the proportions of 70%, 15% and 15%.

[0020] Preferably, the trained SVR model is deployed to actual application for real-time or batch prediction of new energy vehicle sales, and the model is adjusted and optimized according to the feedback data in actual application.

[0021] Preferably, the support vector regression algorithm model is updated regularly, and the support vector regression algorithm model is retrained using new data to maintain the effectiveness and accuracy of the prediction performance.

[0022] A new energy vehicle sales prediction system based on support vector regression includes a data collection module, a data preprocessing module, a feature engineering module, a data division module, a model training module, a model evaluation and optimization module, and a sales prediction module.

[0023] The data collection module is used to collect and prepare new energy vehicle sales data and new energy vehicle data;

[0024] The data preprocessing module is used for data preprocessing;

[0025] The feature engineering module is used to extract vehicle driving features, battery usage features, and geographic location features, etc.

[0026] The data division module is used to divide the data set into a training set, a validation set, and a test set.

[0027] The model training module is used to train the model using a support vector regression algorithm.

[0028] The model evaluation and optimization module is used to evaluate the model performance and make optimization adjustments.

[0029] The sales prediction module is used to use the trained model to make sales predictions.

[0030] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the support vector regression-based new energy vehicle sales prediction method according to any one of the above.

[0031] A computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the support vector regression-based new energy vehicle sales prediction method according to any one of the above.

[0032] Compared with the prior art, the present application has the following beneficial technical effects:

[0033] The present application provides a support vector regression-based new energy vehicle sales prediction method, which adds TBOX upload data and geographic location information to the data source of the prediction model and uses a support vector regression algorithm to accurately predict new energy vehicle sales. The present application uses a support vector regression algorithm to model, which can handle nonlinear relationships and better adapt to complex sales prediction problems. The present application method includes data collection and preparation, data preprocessing, feature engineering, data division, model training, model evaluation and optimization, sales prediction, model deployment, and continuous optimization. By using a support vector regression algorithm, vehicle data and geographic location information are considered comprehensively to accurately predict and evaluate new energy vehicle sales. The present application can better grasp market opportunities, optimize resource allocation, reduce operating risks, and achieve sustainable development through accurate new energy vehicle sales prediction. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 A support vector regression-based new energy vehicle sales prediction method is shown in the figure. DETAILED DESCRIPTION

[0035] In the following, certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and description are to be regarded as illustrative in nature rather than restrictive.

[0036] It should be understood that the terms "comprises" and "comprising," when used in this specification and the following claims, indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0037] It should also be understood that the terminology used in the description of the present application is for the purpose of describing certain embodiments only and is not intended to be limiting of the present application. As used in this description and the following claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0038] It should further be understood that the term "and / or" as used in the specification and in the claims, if any, means any one of the items, any combination of the items, and all possible combinations of the items, and includes the items themselves.

[0039] Various structural diagrams according to the disclosed embodiments of the present application are shown in the drawings. These diagrams are not drawn to scale, in which certain details are exaggerated for clarity of presentation and precision, and certain details can be omitted. The shapes of various regions, layers, and their relative sizes and positional relationships shown in the drawings are merely exemplary, and in actuality can be deviated due to manufacturing tolerances or technical limitations, and regions / layers with different shapes, sizes, and relative positions can be additionally designed by those skilled in the art according to actual needs.

[0040] Embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0041] The present application is a new energy vehicle sales prediction method based on support vector regression, comprising the following steps:

[0042] Step 1, obtaining new energy vehicle sales data and new energy vehicle data;

[0043] Step 2, data preprocessing of the new energy vehicle sales data and the new energy vehicle data obtained in step 1;

[0044] Step 3, extracting feature data of the new energy vehicle data in step 1;

[0045] Step 4, dividing the data set into a training set, a validation set, and a test set;

[0046] Step 5, introduce the support vector regression algorithm model, use the training data set in step 4 to train the support vector regression algorithm model;

[0047] Step 6, use the validation data set in step 4 to evaluate the performance of the support vector regression algorithm model, and optimize the model according to the evaluation results;

[0048] Step 7, use the trained support vector regression algorithm model to predict the sales of new energy vehicles for the test data set or new data.

[0049] The new energy vehicle sales prediction method based on support vector regression of the present application can be applied to the prediction of new energy vehicle sales. By adding TBOX uploaded data and geographic location information to the data source of the prediction model, accurate new energy vehicle sales prediction is achieved by using support vector regression algorithm. The present application can handle nonlinear relationships and better adapt to complex sales prediction problems by using support vector regression algorithm for modeling.

[0050] Example 1

[0051] The new energy vehicle sales prediction system based on support vector regression of the present application comprises a data collection module, a data preprocessing module, a data division module, a model training module, a model evaluation and optimization module, a sales prediction module, a model deployment module, a continuous optimization module, and a feature engineering module.

[0052] The data collection module is used to collect and prepare new energy vehicle sales data, TBOX uploaded data and geographic location information.

[0053] The data preprocessing module is used for data cleaning, missing value filling and standardization.

[0054] The feature engineering module is used to extract vehicle driving features, battery usage features and geographic location features.

[0055] The data division module is used to divide the data set into training set, validation set and test set.

[0056] The model training module is used to train the model using support vector regression algorithm.

[0057] The model evaluation and optimization module is used to evaluate the model performance and make optimization adjustment.

[0058] The sales prediction module is used to use the trained model to predict the sales.

[0059] The model deployment module is used to deploy the trained model to actual application.

[0060] The continuous optimization module is used to update the model periodically and make optimization adjustment.

[0061] Embodiment 2

[0062] A new energy vehicle sales prediction method based on support vector regression in an embodiment of the present application comprises the following steps:

[0063] Step 1, collect and prepare new energy vehicle sales data and new energy vehicle data; In this embodiment, the new energy vehicle sales data is collected, including sales volume, date, etc. TBOX uploaded data is collected, such as vehicle mileage, battery capacity, charging frequency, etc. Geographical location information is collected, such as latitude and longitude, city, etc.

[0064] Step 2, data preprocessing is performed on the data collected in step 1;

[0065] In this embodiment, the collected data is cleaned to remove outliers and duplicates. Missing values can be filled using interpolation, mean or median filling methods. Standardize or normalize the data to ensure that the magnitudes of different data are consistent.

[0066] Step 3, extract feature data of new energy vehicle data in step 1; In this embodiment, TBOX uploaded data and geographical location information are combined to extract relevant features. For example: vehicle driving characteristics: average speed, driving time, driving distance, etc. Battery usage characteristics: charging frequency, average charging duration, etc. Geographical location characteristics: city population density, number of charging facilities, etc.

[0067] Step 4, divide the data in step 3; In this embodiment, the data set is divided into training set, validation set and test set. Generally, it can be divided according to the proportions of 70%, 15% and 15%.

[0068] Step 5, introduce support vector regression algorithm model, and use the training data set in step 4 to train the support vector regression algorithm model;

[0069] In this embodiment, the library of support vector regression (SVR) algorithm is imported, such as Scikit-learn. The SVR model is trained using the training data set. Different kernel functions (such as linear kernel, polynomial kernel, Gaussian kernel, etc.) and hyperparameters can be set for training and optimization.

[0070] Step 6, evaluate and optimize the support vector regression algorithm model;

[0071] In this embodiment, the performance of the SVR model is evaluated using the validation data set, and common indicators such as root mean square error (RMSE) and mean absolute error (MAE) can be used. According to the evaluation results, the model is optimized, such as adjusting the kernel function, hyperparameters, etc.

[0072] Step 7, new energy vehicle sales prediction is performed; in this embodiment, the trained SVR model is used to predict the sales of the test data set or new data.

[0073] Step 8, deployment of the support vector regression algorithm model: the trained SVR model is deployed in actual application for real-time or batch prediction of new energy vehicle sales.

[0074] Step 9, continuous optimization: regularly update the support vector regression algorithm model, retrain the support vector regression algorithm model using new data to maintain the effectiveness and accuracy of the prediction performance. According to the feedback data in the actual application, the model is adjusted and optimized.

[0075] The method of the present application includes the following steps: data collection and preparation, data preprocessing, feature engineering, data division, model training, model evaluation and optimization, sales prediction, model deployment and continuous optimization. By using the support vector regression algorithm, the vehicle data and geographical location information are comprehensively considered to realize accurate prediction and evaluation of new energy vehicle sales. Through accurate prediction of new energy vehicle sales, the present application can better grasp market opportunities, optimize resource allocation, reduce operating risks and realize sustainable development.

[0076] The following is an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not mentioned in the apparatus embodiment, please refer to the method embodiment of the present application.

[0077] In another embodiment of the present application, a computer device is provided, which includes a processor and a memory. The memory is used to store a computer program, and the computer program includes program instructions. The processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to realize the corresponding method flow or corresponding function. The processor in the embodiment of the present application can be used for the operation of the new energy vehicle sales prediction method based on the support vector regression.

[0078] In still another embodiment of the present application, the present application also provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device, used for storing programs and data. It can be understood that the computer readable storage medium herein can include an internal storage medium in the computer device, and of course can also include an extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, which stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the above-mentioned embodiment about the new energy vehicle sales prediction method based on support vector regression.

[0079] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0080] The present application is described with reference to the flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device implemented in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the function specified in the flow or flows and / or block or blocks.

[0081] These computer program instructions can also be stored in a computer readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including instruction apparatus, which implements the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocksFigure 1 the function specified in the one or more blocks.

[0082] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable devices provide a process for implementing the flow Figure 1 the flow or flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0083] Finally, it should be noted that the above embodiments are merely used to illustrate the technical solutions of the present application but not to limit it, and although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or equivalent replacements without departing from the spirit and scope of the present application, and any modifications or equivalent replacements without departing from the spirit and scope of the present application should be covered in the protection scope of the claims of the present application.

[0084] The basic principles and main features of the present application and the advantages of the present application have been shown and described, and it is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or basic features of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, and the scope of the present application is defined by the appended claims rather than the above description, and it is intended to cover all changes falling within the meaning and scope of the equivalent elements of the claims. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0085] In addition, it should be understood that although the present specification is described in terms of embodiments, each embodiment does not contain only one independent technical solution, and the technical solutions in the embodiments are only for the sake of clarity, and those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be properly combined to form other embodiments that those skilled in the art can understand. The above is only to illustrate the technical idea of the present application, and cannot limit the protection scope of the present application, and any modification made on the basis of the technical solutions falls within the protection scope of the claims of the present application.

Claims

1. A method for predicting new energy vehicle sales based on support vector regression, characterized in that, Includes the following steps: Step 1: Obtain new energy vehicle sales data and new energy vehicle data; Step 2: Perform data preprocessing on the new energy vehicle sales data and new energy vehicle data obtained in Step 1; Step 3: Extract the feature data of the new energy vehicle data from Step 1; Step 4: Divide the dataset into training set, validation set, and test set; Step 5: Introduce the support vector regression algorithm model and train the support vector regression algorithm model using the training dataset from Step 4. Step 6: Use the validation dataset from Step 4 to evaluate the performance of the support vector regression algorithm model, and optimize the model based on the evaluation results; Step 7: Use the trained support vector regression algorithm model to predict the sales volume of new energy vehicles on the test dataset or new data; In step 1, the new energy vehicle sales data includes sales volume and date; the new energy vehicle data includes vehicle mileage, battery capacity, number of charging cycles, and geographical location information. In step 2, the data preprocessing includes cleaning the data obtained in step 1 to remove outliers and duplicates; then filling in missing values; and standardizing or normalizing the data to ensure that the magnitude of different data is consistent. In step 3, the feature data includes vehicle driving characteristics, battery usage characteristics, and geographical location characteristics; The vehicle driving characteristics include average speed, driving time, and driving distance; the battery usage characteristics include number of charging cycles and average charging time. The geographical location features include urban population density and the number of charging facilities.

2. The method for predicting new energy vehicle sales based on support vector regression according to claim 1, characterized in that, In step 4, the training set, validation set, and test set are divided into three groups according to a ratio of 70%, 15%, and 15%, respectively.

3. The method for predicting new energy vehicle sales based on support vector regression according to claim 1, characterized in that, The trained SVR model is deployed to real-world applications for real-time or batch prediction of new energy vehicle sales. The model is then adjusted and optimized based on feedback data from these applications.

4. The method for predicting new energy vehicle sales based on support vector regression according to claim 1, characterized in that, Regularly update the support vector regression algorithm model and retrain it with new data to maintain the effectiveness and accuracy of its predictive performance.

5. A new energy vehicle sales forecasting system based on support vector regression, based on the new energy vehicle sales forecasting method based on support vector regression as described in any one of claims 1-4, characterized in that, It includes a data collection module, a data preprocessing module, a feature engineering module, a data partitioning module, a model training module, a model evaluation and optimization module, and a sales forecasting module; The data collection module is used to collect and prepare new energy vehicle sales data and new energy vehicle data. The data preprocessing module is used to perform data preprocessing; The feature engineering module is used to extract vehicle driving features, battery usage features, and geographic location features; The data partitioning module is used to divide the dataset into a training set, a validation set, and a test set. The model training module is used to train the model using the support vector regression algorithm; The model evaluation and optimization module is used to evaluate model performance and make optimization adjustments. The sales forecasting module is used to forecast sales using a trained model.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the new energy vehicle sales forecasting method based on support vector regression as described in any one of claims 1-4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the new energy vehicle sales forecasting method based on support vector regression as described in any one of claims 1-4.

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