Charging station energy storage configuration optimization method based on surrounding new energy traffic flow big data

Abstract

The invention discloses a charging station energy storage configuration optimization method based on surrounding new energy traffic flow big data, and belongs to the technical field of charging stations, and the method comprises the following steps: S1, data collection and preprocessing; s2, model training and test optimization; s3, charging station energy storage configuration prediction: performing prediction analysis on the new energy traffic flow characteristic data in the surrounding area based on the optimal charging station energy storage configuration prediction model, and determining a charging station energy storage configuration prediction result; and S4, charging station energy storage configuration optimization: adjusting and optimizing the charging station energy storage configuration condition based on the charging station energy storage configuration feedback suggestions. According to the invention, the problems that the energy cannot be fully utilized and the user experience feeling is reduced because the energy storage configuration of the charging station cannot be optimized based on the new energy traffic flow big data in the surrounding area in the prior art are solved. According to the invention, the energy storage configuration of the charging station can be optimized based on the new energy traffic flow big data in the surrounding area, and the energy utilization rate and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging stations, and in particular to a method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data. Background Art

[0002] With the rapid development of my country's new energy vehicle industry, the frequency and scope of use of new energy vehicles are constantly expanding. However, in existing charging stations, the configuration of energy storage equipment often results in a large waste, resulting in high energy consumption, which is not conducive to energy conservation, emission reduction and environmental protection. In order to meet the growing charging demand and improve the operating efficiency and user experience of charging stations, it is necessary to optimize the energy storage configuration of charging stations.

[0003] The Chinese patent with publication number CN108394298A discloses a self-service charging station for a distributed photovoltaic-storage-charging AC / DC hybrid microgrid on a highway. The charging stations are respectively arranged on both sides of the highway. The charging stations mainly include a power generation side and a load side. The power generation side includes a renewable energy power generation system, a storage power station and a DC bus. The load side includes a dual-mode AC / DC charging pile and an AC bus. By building a photovoltaic-storage-charging distributed AC / DC hybrid microgrid, relay charging of new energy vehicles can be provided for inter-city or long-distance travel on highways. However, the patent has the following defects:

[0004] The existing charging station energy storage configuration cannot be optimized based on the big data of new energy vehicle traffic in the surrounding area, resulting in insufficient utilization of energy and reduced user experience. Summary of the invention

[0005] The purpose of the present invention is to provide a charging station energy storage configuration optimization method based on the surrounding new energy vehicle traffic big data, which can optimize the charging station energy storage configuration based on the new energy vehicle traffic big data in the surrounding area, improve energy utilization and user experience, and solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data includes the following steps:

[0008] S1. Data collection and preprocessing:

[0009] Acquire the real-time data of new energy vehicle flow in the surrounding area, pre-process the real-time data of new energy vehicle flow in the surrounding area, and determine the characteristic data of new energy vehicle flow in the surrounding area;

[0010] S2. Model training and testing optimization:

[0011] According to the demand for energy storage configuration optimization of charging stations based on the big data of new energy vehicle traffic in the surrounding area, a charging station energy storage configuration prediction model based on the big data of new energy vehicle traffic in the surrounding area is constructed, and the charging station energy storage configuration prediction model is tested and optimized to determine the optimal charging station energy storage configuration prediction model;

[0012] S3. Charging station energy storage configuration prediction:

[0013] Based on the optimal charging station energy storage configuration prediction model, the characteristic data of new energy vehicle traffic in the surrounding area is predicted and analyzed, and the charging station energy storage configuration prediction results based on the big data of new energy vehicle traffic in the surrounding area are determined, and charging station energy storage equipment is configured for new energy vehicles in the surrounding area;

[0014] S4. Optimization of energy storage configuration of charging stations:

[0015] Based on the feedback on the energy storage configuration of charging stations, an energy storage configuration optimization plan for charging stations is formulated, the energy storage configuration of charging stations is adjusted and optimized, and a visual operation report of the energy storage equipment of charging stations is generated to provide a reference basis for users to charge.

[0016] Preferably, in S1, real-time data of new energy vehicle traffic in the surrounding area is obtained, and the following operations are performed:

[0017] Install cameras and sensors on roads in the surrounding area, and install GPS devices on new energy vehicles;

[0018] Real-time monitoring and collection of the number and type of new energy vehicles in the surrounding area based on cameras;

[0019] Real-time monitoring and collection of the passing time and speed of new energy vehicles in the surrounding area based on sensors;

[0020] Based on GPS equipment, the real-time location and driving trajectory of new energy vehicles in the surrounding area are monitored and collected in real time;

[0021] Among them, the real-time data of new energy vehicle traffic in the surrounding area is determined based on the number and type of new energy vehicles in the surrounding area, the passing time and speed of new energy vehicles, and the real-time location and driving trajectory of new energy vehicles.

[0022] Preferably, in S1, the real-time data of new energy vehicle flow in the surrounding area is preprocessed, and the following operations are performed:

[0023] Obtain real-time data on new energy vehicle traffic in the surrounding area;

[0024] Clean the real-time data of new energy vehicle traffic in the surrounding area, including:

[0025] Conduct consistency checks on real-time data on new energy vehicle traffic in the surrounding area;

[0026] Check whether the real-time data of new energy vehicle traffic in the surrounding area meets the requirements based on the reasonable value range and mutual relationship of each parameter in the real-time data of new energy vehicle traffic in the surrounding area;

[0027] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time data of new energy vehicle traffic in the surrounding area;

[0028] Process invalid and missing values ​​in the real-time data of new energy vehicle traffic in the surrounding area;

[0029] Among them, each parameter in the real-time data of new energy vehicle traffic in the surrounding area is checked one by one to determine whether it is invalid data or missing data;

[0030] Remove invalid and missing data that are of no value to the optimization of energy storage configuration of charging stations from the real-time data of new energy vehicle traffic in the surrounding area;

[0031] Determine the real-time data of new energy vehicle traffic in the surrounding area that is valuable for optimizing the energy storage configuration of the charging station.

[0032] Preferably, in S1, the real-time data of new energy vehicle flow in the surrounding area is preprocessed, and the following operations are further performed:

[0033] Obtain real-time data on new energy vehicle traffic in the surrounding area that is valuable for optimizing the energy storage configuration of charging stations;

[0034] Convert the real-time data of new energy vehicle traffic in the surrounding areas where the energy storage configuration of the charging station is valuable for optimization, and eliminate the dimensional differences between the real-time data of new energy vehicle traffic in the surrounding areas;

[0035] Determine the real-time data of new energy vehicle traffic in the surrounding area with data standardization;

[0036] Extract features from the real-time data of new energy vehicle traffic in the surrounding area with data standardization;

[0037] Extract features that can reflect the optimization of energy storage configuration in charging stations;

[0038] Determine the characteristic data of new energy vehicle traffic in the surrounding area.

[0039] Preferably, in S2, the optimal charging station energy storage configuration prediction model is determined, and the following operations are performed:

[0040] According to the demand for optimizing energy storage configuration of charging stations based on the big data of new energy vehicle traffic in the surrounding area, historical data of new energy vehicle traffic in the surrounding area is collected;

[0041] Divide the historical data of new energy vehicle traffic in the surrounding area to determine the training set and test set;

[0042] Select a neural network model framework suitable for optimizing the energy storage configuration of charging stations based on big data of new energy vehicle traffic in the surrounding area;

[0043] Based on the training set, a neural network model framework suitable for optimizing the energy storage configuration of charging stations based on the big data of new energy vehicle traffic in the surrounding area is trained;

[0044] Determine a prediction model for energy storage configuration of charging stations based on big data of new energy vehicle traffic in the surrounding area;

[0045] Based on the test set, the performance test and evaluation of the charging station energy storage configuration prediction model based on the big data of new energy vehicle traffic in the surrounding area was carried out;

[0046] Determine the performance test evaluation results based on the charging station energy storage configuration prediction model;

[0047] According to the performance test evaluation results based on the charging station energy storage configuration prediction model, the charging station energy storage configuration prediction model is adjusted and iteratively optimized;

[0048] Determine the optimal charging station energy storage configuration prediction model.

[0049] Preferably, in S3, the characteristic data of new energy vehicle flow in the surrounding area is predicted and analyzed, and the following operations are performed:

[0050] Obtaining new energy vehicle traffic characteristic data in the surrounding area, and inputting the new energy vehicle traffic characteristic data in the surrounding area into the optimal charging station energy storage configuration prediction model;

[0051] Based on the optimal charging station energy storage configuration prediction model, the traffic characteristic data of new energy vehicles in the surrounding area is predicted and analyzed;

[0052] Determine the prediction results of charging station energy storage configuration based on the big data of new energy vehicle traffic in the surrounding area;

[0053] Among them, according to the prediction results of charging station energy storage configuration based on the big data of new energy vehicle traffic in the surrounding area, a charging station energy storage configuration plan is formulated, and charging station energy storage equipment is configured for new energy vehicles in the surrounding area based on the charging station energy storage configuration plan, so that the charging station energy storage equipment can meet the charging needs of new energy vehicles in the surrounding area.

[0054] Preferably, in S4, the energy storage configuration of the charging station is adjusted and optimized, and the following operations are performed:

[0055] Real-time monitoring of energy storage equipment at charging stations for new energy vehicles in the surrounding area, collection of operating status and charging requirements of energy storage equipment at charging stations, and acquisition of feedback on energy storage configuration at charging stations;

[0056] Develop an optimization plan for charging station energy storage configuration based on feedback from charging station energy storage configuration;

[0057] Based on the charging station energy storage configuration optimization plan, adjust and optimize the charging station energy storage configuration;

[0058] Among them, according to the charging demand of new energy vehicles in the surrounding area and the distribution of charging peak and trough periods, the energy storage configuration plan of the charging station is optimized, and the charging price of the charging station is adjusted according to the charging behavior pattern of new energy vehicles in the surrounding area. The energy storage capacity and power parameters of the charging station are adjusted to enable new energy vehicles in the surrounding area to use the charging station energy storage equipment, making the charging station energy storage configuration intelligent.

[0059] Preferably, in S4, a visualized charging station energy storage device operation report is generated by performing the following operations:

[0060] After the energy storage configuration of the charging station is adjusted and optimized, the usage of energy storage equipment in the surrounding area is monitored in real time to obtain the usage data of energy storage equipment in the surrounding area;

[0061] The usage data of the energy storage equipment in the charging station is mined and analyzed to generate a visual operation report of the energy storage equipment in the charging station, and the visual operation report of the energy storage equipment in the charging station is transmitted to the user to provide a reference for the user's charging.

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] 1. The present invention obtains real-time data on the flow of new energy vehicles in the surrounding area, pre-processes the real-time data on the flow of new energy vehicles in the surrounding area, determines the characteristic data of the flow of new energy vehicles in the surrounding area, and constructs a charging station energy storage configuration prediction model based on the big data of the flow of new energy vehicles in the surrounding area according to the optimization demand of the charging station energy storage configuration based on the big data of the flow of new energy vehicles in the surrounding area. The charging station energy storage configuration prediction model is tested and optimized to determine the optimal charging station energy storage configuration prediction model. Based on the optimal charging station energy storage configuration prediction model, prediction and analysis are performed on the characteristic data of the flow of new energy vehicles in the surrounding area, and the prediction result of the charging station energy storage configuration based on the big data of the flow of new energy vehicles in the surrounding area is determined. Charging station energy storage equipment is configured for new energy vehicles in the surrounding area, so that the charging station energy storage equipment meets the charging demand of new energy vehicles in the surrounding area.

[0064] 2. The present invention conducts real-time monitoring of energy storage devices at charging stations equipped with new energy vehicles in the surrounding area, collects the operating status and charging demand of the energy storage devices at the charging stations, obtains feedback on the energy storage configuration of the charging stations, and formulates an energy storage configuration optimization plan for the charging stations based on the feedback on the energy storage configuration of the charging stations. The energy storage configuration of the charging stations is adjusted and optimized to make the energy storage configuration of the charging stations intelligent. The usage data of the energy storage devices at the charging stations is mined and analyzed to generate a visualized operation report of the energy storage devices at the charging stations. The visualized operation report of the energy storage devices at the charging stations is transmitted to the users to provide a reference for the users to charge. The energy storage configuration of the charging stations can be optimized based on the big data of the new energy vehicle traffic in the surrounding area to improve the energy utilization rate and the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of the method for optimizing the energy storage configuration of charging stations based on the surrounding new energy vehicle traffic big data of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] In order to solve the problem that the existing charging station energy storage configuration cannot be optimized based on the big data of new energy vehicle traffic in the surrounding area, resulting in insufficient utilization of energy and reduced user experience, please refer to Figure 1 , this embodiment provides the following technical solutions:

[0068] The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data includes the following steps:

[0069] S1. Data collection and preprocessing:

[0070] Acquire the real-time data of new energy vehicle flow in the surrounding area, pre-process the real-time data of new energy vehicle flow in the surrounding area, and determine the characteristic data of new energy vehicle flow in the surrounding area;

[0071] In this embodiment, the real-time data of new energy vehicle traffic in the surrounding area is obtained by performing the following operations:

[0072] Install cameras and sensors on roads in the surrounding area, and install GPS devices on new energy vehicles;

[0073] Real-time monitoring and collection of the number and type of new energy vehicles in the surrounding area based on cameras;

[0074] Real-time monitoring and collection of the passing time and speed of new energy vehicles in the surrounding area based on sensors;

[0075] Based on GPS equipment, the real-time location and driving trajectory of new energy vehicles in the surrounding area are monitored and collected in real time;

[0076] Among them, the real-time data of new energy vehicle traffic in the surrounding area is determined based on the number and type of new energy vehicles in the surrounding area, the passing time and speed of new energy vehicles, and the real-time location and driving trajectory of new energy vehicles.

[0077] In this embodiment, the real-time data of new energy vehicle flow in the surrounding area is preprocessed, and the following operations are performed:

[0078] Obtain real-time data on new energy vehicle traffic in the surrounding area;

[0079] Clean the real-time data of new energy vehicle traffic in the surrounding area, including:

[0080] Conduct consistency checks on real-time data on new energy vehicle traffic in the surrounding area;

[0081] Check whether the real-time data of new energy vehicle traffic in the surrounding area meets the requirements based on the reasonable value range and mutual relationship of each parameter in the real-time data of new energy vehicle traffic in the surrounding area;

[0082] Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time data of new energy vehicle traffic in the surrounding area;

[0083] Process invalid and missing values ​​in the real-time data of new energy vehicle traffic in the surrounding area;

[0084] Among them, each parameter in the real-time data of new energy vehicle traffic in the surrounding area is checked one by one to determine whether it is invalid data or missing data;

[0085] Remove invalid and missing data that are of no value to the optimization of energy storage configuration of charging stations from the real-time data of new energy vehicle traffic in the surrounding area;

[0086] Determine the real-time data of new energy vehicle traffic in the surrounding area that is valuable for optimizing the energy storage configuration of the charging station;

[0087] Convert the real-time data of new energy vehicle traffic in the surrounding areas where the energy storage configuration of the charging station is valuable for optimization, and eliminate the dimensional differences between the real-time data of new energy vehicle traffic in the surrounding areas;

[0088] Determine the real-time data of new energy vehicle traffic in the surrounding area with data standardization;

[0089] Extract features from the real-time data of new energy vehicle traffic in the surrounding area with data standardization;

[0090] Extract features that can reflect the optimization of energy storage configuration in charging stations;

[0091] Determine the characteristic data of new energy vehicle traffic in the surrounding area.

[0092] S2. Model training and testing optimization:

[0093] According to the demand for energy storage configuration optimization of charging stations based on the big data of new energy vehicle traffic in the surrounding area, a charging station energy storage configuration prediction model based on the big data of new energy vehicle traffic in the surrounding area is constructed, and the charging station energy storage configuration prediction model is tested and optimized to determine the optimal charging station energy storage configuration prediction model;

[0094] In this embodiment, the optimal charging station energy storage configuration prediction model is determined by performing the following operations:

[0095] According to the demand for optimizing energy storage configuration of charging stations based on the big data of new energy vehicle traffic in the surrounding area, historical data of new energy vehicle traffic in the surrounding area is collected;

[0096] Divide the historical data of new energy vehicle traffic in the surrounding area to determine the training set and test set;

[0097] Select a neural network model framework suitable for optimizing the energy storage configuration of charging stations based on big data of new energy vehicle traffic in the surrounding area;

[0098] Based on the training set, a neural network model framework suitable for optimizing the energy storage configuration of charging stations based on the big data of new energy vehicle traffic in the surrounding area is trained;

[0099] Determine a prediction model for energy storage configuration of charging stations based on big data of new energy vehicle traffic in the surrounding area;

[0100] Based on the test set, the performance test and evaluation of the charging station energy storage configuration prediction model based on the big data of new energy vehicle traffic in the surrounding area was carried out;

[0101] Determine the performance test evaluation results based on the charging station energy storage configuration prediction model;

[0102] According to the performance test evaluation results based on the charging station energy storage configuration prediction model, the charging station energy storage configuration prediction model is adjusted and iteratively optimized;

[0103] Determine the optimal charging station energy storage configuration prediction model.

[0104] S3. Charging station energy storage configuration prediction:

[0105] Based on the optimal charging station energy storage configuration prediction model, the characteristic data of new energy vehicle traffic in the surrounding area is predicted and analyzed, and the charging station energy storage configuration prediction results based on the big data of new energy vehicle traffic in the surrounding area are determined, and charging station energy storage equipment is configured for new energy vehicles in the surrounding area;

[0106] In this embodiment, the new energy vehicle flow characteristic data in the surrounding area is predicted and analyzed, and the following operations are performed:

[0107] Obtaining new energy vehicle traffic characteristic data in the surrounding area, and inputting the new energy vehicle traffic characteristic data in the surrounding area into the optimal charging station energy storage configuration prediction model;

[0108] Based on the optimal charging station energy storage configuration prediction model, the traffic characteristic data of new energy vehicles in the surrounding area is predicted and analyzed;

[0109] Determine the prediction results of charging station energy storage configuration based on the big data of new energy vehicle traffic in the surrounding area;

[0110] Among them, according to the prediction results of charging station energy storage configuration based on the big data of new energy vehicle traffic in the surrounding area, a charging station energy storage configuration plan is formulated, and charging station energy storage equipment is configured for new energy vehicles in the surrounding area based on the charging station energy storage configuration plan, so that the charging station energy storage equipment can meet the charging needs of new energy vehicles in the surrounding area.

[0111] S4. Optimization of energy storage configuration of charging stations:

[0112] Based on the feedback on the energy storage configuration of charging stations, an energy storage configuration optimization plan for charging stations is formulated, the energy storage configuration of charging stations is adjusted and optimized, and a visual operation report of the energy storage equipment of charging stations is generated to provide a reference basis for users to charge.

[0113] In this embodiment, the energy storage configuration of the charging station is adjusted and optimized, and the following operations are performed:

[0114] Real-time monitoring of energy storage equipment at charging stations for new energy vehicles in the surrounding area, collection of operating status and charging requirements of energy storage equipment at charging stations, and acquisition of feedback on energy storage configuration at charging stations;

[0115] Develop an optimization plan for charging station energy storage configuration based on feedback from charging station energy storage configuration;

[0116] Based on the charging station energy storage configuration optimization plan, adjust and optimize the charging station energy storage configuration;

[0117] Among them, according to the charging demand of new energy vehicles in the surrounding area and the distribution of charging peak and trough periods, the energy storage configuration plan of the charging station is optimized, and the charging price of the charging station is adjusted according to the charging behavior pattern of new energy vehicles in the surrounding area. The energy storage capacity and power parameters of the charging station are adjusted to enable new energy vehicles in the surrounding area to use the charging station energy storage equipment, making the charging station energy storage configuration intelligent.

[0118] In this embodiment, a visualized charging station energy storage device operation report is generated by performing the following operations:

[0119] After the energy storage configuration of the charging station is adjusted and optimized, the usage of energy storage equipment in the surrounding area is monitored in real time to obtain the usage data of energy storage equipment in the surrounding area;

[0120] The usage data of the energy storage equipment in the charging station is mined and analyzed to generate a visual operation report of the energy storage equipment in the charging station, and the visual operation report of the energy storage equipment in the charging station is transmitted to the user to provide a reference for the user's charging.

[0121] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0122] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data, characterized in that: The steps include: S1. Data collection and preprocessing: Acquire the real-time data of new energy vehicle flow in the surrounding area, pre-process the real-time data of new energy vehicle flow in the surrounding area, and determine the characteristic data of new energy vehicle flow in the surrounding area; S2. Model training and testing optimization: According to the demand for energy storage configuration optimization of charging stations based on the big data of new energy vehicle traffic in the surrounding area, a charging station energy storage configuration prediction model based on the big data of new energy vehicle traffic in the surrounding area is constructed, and the charging station energy storage configuration prediction model is tested and optimized to determine the optimal charging station energy storage configuration prediction model; S3. Charging station energy storage configuration prediction: Based on the optimal charging station energy storage configuration prediction model, the characteristic data of new energy vehicle traffic in the surrounding area is predicted and analyzed, and the charging station energy storage configuration prediction results based on the big data of new energy vehicle traffic in the surrounding area are determined, and charging station energy storage equipment is configured for new energy vehicles in the surrounding area; S4. Optimization of energy storage configuration of charging stations: Based on the feedback on the energy storage configuration of charging stations, an energy storage configuration optimization plan for charging stations is formulated, the energy storage configuration of charging stations is adjusted and optimized, and a visual operation report of the energy storage equipment of charging stations is generated to provide a reference basis for users to charge.

2. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 1 is characterized in that: In S1, the real-time data of new energy vehicle traffic in the surrounding area is obtained, and the following operations are performed: Install cameras and sensors on roads in the surrounding area, and install GPS devices on new energy vehicles; Real-time monitoring and collection of the number and type of new energy vehicles in the surrounding area based on cameras; Real-time monitoring and collection of the passing time and speed of new energy vehicles in the surrounding area based on sensors; Based on GPS equipment, the real-time location and driving trajectory of new energy vehicles in the surrounding area are monitored and collected in real time; Among them, the real-time data of new energy vehicle traffic in the surrounding area is determined based on the number and type of new energy vehicles in the surrounding area, the passing time and speed of new energy vehicles, and the real-time location and driving trajectory of new energy vehicles.

3. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 2 is characterized in that: In S1, the real-time data of new energy vehicle flow in the surrounding area is preprocessed, and the following operations are performed: Obtain real-time data on new energy vehicle traffic in the surrounding area; Clean the real-time data of new energy vehicle traffic in the surrounding area, including: Conduct consistency checks on real-time data on new energy vehicle traffic in the surrounding area; Check whether the real-time data of new energy vehicle traffic in the surrounding area meets the requirements based on the reasonable value range and mutual relationship of each parameter in the real-time data of new energy vehicle traffic in the surrounding area; Remove inconsistent data that is beyond the normal range, logically unreasonable or contradictory in the real-time data of new energy vehicle traffic in the surrounding area; Process invalid and missing values ​​in the real-time data of new energy vehicle traffic in the surrounding area; Among them, each parameter in the real-time data of new energy vehicle traffic in the surrounding area is checked one by one to determine whether it is invalid data or missing data; Remove invalid and missing data that are of no value to the optimization of energy storage configuration of charging stations from the real-time data of new energy vehicle traffic in the surrounding area; Determine the real-time data of new energy vehicle traffic in the surrounding area that is valuable for optimizing the energy storage configuration of the charging station.

4. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 3 is characterized in that: In S1, the real-time data of the flow of new energy vehicles in the surrounding area is preprocessed, and the following operations are performed: Obtain real-time data on new energy vehicle traffic in the surrounding area that is valuable for optimizing the energy storage configuration of charging stations; Convert the real-time data of new energy vehicle traffic in the surrounding areas where the energy storage configuration of the charging station is valuable for optimization, and eliminate the dimensional differences between the real-time data of new energy vehicle traffic in the surrounding areas; Determine the real-time data of new energy vehicle traffic in the surrounding area with data standardization; Extract features from the real-time data of new energy vehicle traffic in the surrounding area with data standardization; Extract features that can reflect the optimization of energy storage configuration in charging stations; Determine the characteristic data of new energy vehicle traffic in the surrounding area.

5. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 4 is characterized in that: In S2, the optimal charging station energy storage configuration prediction model is determined, and the following operations are performed: According to the demand for optimizing the energy storage configuration of charging stations based on the big data of new energy vehicle traffic in the surrounding area, historical data of new energy vehicle traffic in the surrounding area is collected; Divide the historical data of new energy vehicle traffic in the surrounding area to determine the training set and test set; Select a neural network model framework suitable for optimizing the energy storage configuration of charging stations based on big data of new energy vehicle traffic in the surrounding area; Based on the training set, a neural network model framework suitable for optimizing the energy storage configuration of charging stations based on the big data of new energy vehicle traffic in the surrounding area is trained; Determine a prediction model for energy storage configuration of charging stations based on big data of new energy vehicle traffic in the surrounding area; Based on the test set, the performance test and evaluation of the charging station energy storage configuration prediction model based on the big data of new energy vehicle traffic in the surrounding area was carried out; Determine the performance test evaluation results based on the charging station energy storage configuration prediction model; According to the performance test evaluation results based on the charging station energy storage configuration prediction model, the charging station energy storage configuration prediction model is adjusted and iteratively optimized; Determine the optimal charging station energy storage configuration prediction model.

6. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 5 is characterized in that: In S3, the characteristic data of new energy vehicle flow in the surrounding area is predicted and analyzed, and the following operations are performed: Obtaining new energy vehicle traffic characteristic data in the surrounding area, and inputting the new energy vehicle traffic characteristic data in the surrounding area into the optimal charging station energy storage configuration prediction model; Based on the optimal charging station energy storage configuration prediction model, the traffic characteristic data of new energy vehicles in the surrounding area is predicted and analyzed; Determine the prediction results of charging station energy storage configuration based on the big data of new energy vehicle traffic in the surrounding area; Among them, according to the prediction results of charging station energy storage configuration based on the big data of new energy vehicle traffic in the surrounding area, a charging station energy storage configuration plan is formulated, and charging station energy storage equipment is configured for new energy vehicles in the surrounding area based on the charging station energy storage configuration plan, so that the charging station energy storage equipment can meet the charging needs of new energy vehicles in the surrounding area.

7. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 6 is characterized in that: In S4, the energy storage configuration of the charging station is adjusted and optimized, and the following operations are performed: Real-time monitoring of energy storage equipment at charging stations for new energy vehicles in the surrounding area, collection of operating status and charging requirements of energy storage equipment at charging stations, and acquisition of feedback on energy storage configuration at charging stations; Develop an optimization plan for charging station energy storage configuration based on feedback from charging station energy storage configuration; Based on the charging station energy storage configuration optimization plan, adjust and optimize the charging station energy storage configuration; Among them, according to the charging demand of new energy vehicles in the surrounding area and the distribution of charging peak and trough periods, the energy storage configuration plan of the charging station is optimized, and the charging price of the charging station is adjusted according to the charging behavior pattern of new energy vehicles in the surrounding area. The energy storage capacity and power parameters of the charging station are adjusted to enable new energy vehicles in the surrounding area to use the charging station energy storage equipment, making the charging station energy storage configuration intelligent.

8. The method for optimizing energy storage configuration of charging stations based on surrounding new energy vehicle traffic big data according to claim 7 is characterized in that: In S4, a visual charging station energy storage device operation report is generated by performing the following operations: After the energy storage configuration of the charging station is adjusted and optimized, the usage of energy storage equipment in the surrounding area is monitored in real time to obtain the usage data of energy storage equipment in the surrounding area; The usage data of the energy storage equipment in the charging station is mined and analyzed to generate a visual operation report of the energy storage equipment in the charging station, and the visual operation report of the energy storage equipment in the charging station is transmitted to the user to provide a reference for the user's charging.

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