Photovoltaic-based mobile charging pile auxiliary energy supplementing method and system

Abstract

The application discloses a kind of mobile charging pile auxiliary energy supplementing method and system based on photovoltaic, specifically relates to energy supply technical field;It is through collecting the real-time illumination intensity data in the deployment area of photovoltaic mobile charging pile and the real-time charging demand data of electric vehicle, and data preprocessing is carried out, forms regional photovoltaic power generation capacity data and regional user charging demand data;Analysis of the photovoltaic power generation potential of each grid position, generate regional illumination power generation potential spatial distribution data;Identify the spatial aggregation characteristics of user charging demand, generate the spatial aggregation position data of user charging demand;According to the spatial matching degree between illumination power generation potential and charging demand position, determine the spatial matching priority data of optimal deployment position;Based on spatial matching priority data, path planning is carried out, generates real-time optimal energy supplementing scheduling path data, improves the scheduling efficiency of mobile charging pile and photovoltaic energy utilization rate.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy supply, more particularly, the present application relates to a mobile charging pile auxiliary energy supplementing method and system based on photovoltaic. BACKGROUND

[0002] With the rapid growth of the number of electric vehicles, the traditional fixed charging facilities have problems such as inflexible layout, inaccurate matching of supply and demand space, etc. At the same time, the photovoltaic energy also has the problem of low utilization efficiency due to the spatial dispersion and mismatch between energy supply and user demand in distributed application.

[0003] The existing charging pile scheduling method cannot effectively combine the spatial characteristics of photovoltaic resource distribution and the spatial aggregation characteristics of user charging demand in the region, and cannot realize the fine matching of charging pile deployment location and user demand and photovoltaic power generation potential. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present application provide a mobile charging pile auxiliary energy supplementing method and system based on photovoltaic to solve the problems raised in the background art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0006] A mobile charging pile auxiliary energy supplementing method based on photovoltaic, comprising the following steps:

[0007] Collecting real-time illumination intensity data of a photovoltaic mobile charging pile in a preset deployment area and real-time charging demand data of an electric vehicle, and performing data preprocessing to generate regional photovoltaic power generation capacity data and regional user charging demand data;

[0008] According to the regional photovoltaic power generation capacity data, analyzing the photovoltaic power generation potential of different positions in the region to generate spatial distribution data of regional illumination power generation potential;

[0009] According to the regional user charging demand data, identifying the spatial aggregation characteristics of user charging demand to generate spatial aggregation position data of user charging demand;

[0010] According to the spatial distribution data of regional illumination power generation potential and the spatial aggregation position data of user charging demand, analyzing the spatial matching degree of illumination power generation potential and user charging demand to generate spatial matching priority data of the best deployment position;

[0011] According to the spatial matching priority data of the best deployment position, planning a mobile charging pile deployment path to generate initial mobile deployment path data;

[0012] According to the initial mobile deployment path data, dynamically adjusting the charging pile mobile path to generate real-time optimal energy supplementing scheduling path data.

[0013] In a preferred embodiment, real-time light intensity data of a preset deployment area of a photovoltaic mobile charging pile and real-time charging demand data of an electric vehicle are collected and preprocessed to generate regional photovoltaic power generation capacity data and regional user charging demand data, specifically:

[0014] Real-time light intensity data of different positions in the preset deployment area of the photovoltaic mobile charging pile and real-time charging demand data of the electric vehicle are collected;

[0015] The real-time light intensity data and the real-time charging demand data are preprocessed;

[0016] Based on the preprocessed real-time light intensity data, regional photovoltaic power generation capacity data is obtained;

[0017] Based on the preprocessed real-time charging demand data, regional user charging demand data is obtained.

[0018] In a preferred embodiment, according to the regional photovoltaic power generation capacity data, the photovoltaic power generation potential of different positions in the region is analyzed to generate spatial distribution data of the regional light power generation potential, specifically:

[0019] The regional photovoltaic power generation capacity data is divided into a plurality of grid units according to a preset spatial resolution and a grid number is established;

[0020] The light radiation intensity in each grid unit is corrected to obtain a corrected light radiation intensity value;

[0021] Based on the corrected light radiation intensity value and the grid area, the photovoltaic power generation potential index of the grid unit is calculated to obtain a photovoltaic power generation potential index list;

[0022] The photovoltaic power generation potential index list is mapped to the corresponding grid unit coordinates to construct a photovoltaic power generation potential spatial matrix;

[0023] The photovoltaic power generation potential spatial matrix is spatially interpolated to generate spatial distribution data of the regional light power generation potential.

[0024] In a preferred embodiment, according to the regional user charging demand data, the spatial aggregation characteristics of user charging demand are identified to generate spatial aggregation position data of user charging demand, specifically:

[0025] The regional user charging demand data is mapped to a discrete demand point coordinate set according to a preset spatial resolution;

[0026] The spatial density of the discrete demand point coordinate set is analyzed to calculate a neighborhood charging demand density value;

[0027] An initial charging demand aggregation region set is obtained by using a density threshold and a minimum aggregation scale threshold to identify the charging demand aggregation region;

[0028] The initial charging demand aggregation region set is subjected to connectivity inspection, and adjacent overlapping aggregation regions are merged to determine a charging demand aggregation region list;

[0029] The geometric center coordinates of each charging demand aggregation region are extracted to form user charging demand spatial aggregation position data.

[0030] In a preferred embodiment, according to the spatial distribution data of regional light power generation potential and the spatial aggregation position data of user charging demand, the spatial matching degree of light power generation potential and user charging demand is analyzed, and spatial matching priority data of the optimal deployment position is generated, specifically as follows:

[0031] The spatial distribution data of regional light power generation potential and the spatial aggregation position data of user charging demand are rasterized and fused through a unified coordinate system to establish a public grid coordinate system;

[0032] In the public grid coordinate system, the difference between the photovoltaic power generation potential value and the charging demand density value in each grid cell is calculated to form a spatial difference matrix;

[0033] The spatial difference matrix is subjected to normalization processing, and a spatial matching score of each grid cell is calculated with a preset weight coefficient;

[0034] The spatial matching scores are sorted from high to low, and the spatial matching priority data of the optimal deployment position is output.

[0035] In a preferred embodiment, according to the spatial matching priority data of the optimal deployment position, a mobile charging pile deployment path is planned, and initial mobile deployment path data is generated, specifically as follows:

[0036] The spatial matching priority data of the optimal deployment position is read, and a number of optimal deployment positions are selected according to a priority threshold to form a candidate deployment position set;

[0037] The candidate deployment position set is mapped to a node set, and a directed weighted graph is constructed in combination with deployment region road connectivity information;

[0038] The upper limit of the travel distance of the mobile charging pile, the preset dispatching time window, and the turning angle restriction are set as constraint conditions on the directed weighted graph, and a minimum path of the weighted distance is solved to obtain a candidate deployment position access sequence;

[0039] The initial mobile deployment path data is generated according to the candidate deployment position access sequence.

[0040] In a preferred embodiment, the charging pile moving path is dynamically adjusted according to the initial moving deployment path data to generate real-time optimal energy supplement scheduling path data, specifically:

[0041] Real-time light intensity monitoring data and traffic passing state data of the current position of the mobile charging pile are collected;

[0042] The real-time light intensity monitoring data is compared with the regional photovoltaic power generation capacity data to calculate a photovoltaic power generation deviation index;

[0043] The real-time traffic passing state data is compared with the initial moving deployment path data to calculate a travel time deviation index;

[0044] The path correction trigger condition is set based on the photovoltaic power generation deviation index and the travel time deviation index to determine whether the correction threshold is reached;

[0045] When the correction threshold is reached, the node corresponding to the current position of the mobile charging pile is taken as the starting point to solve the minimum weighted distance path on the directed weighted graph to update the candidate deployment position access order;

[0046] The real-time optimal energy supplement scheduling path data is generated according to the updated candidate deployment position access order.

[0047] On the other hand, the application provides a photovoltaic-based mobile charging pile auxiliary energy supplement system, comprising:

[0048] The data collection module collects real-time light intensity data of the photovoltaic mobile charging pile in the preset deployment area and real-time charging demand data of the electric vehicle, and performs data preprocessing to generate regional photovoltaic power generation capacity data and regional user charging demand data;

[0049] The potential analysis module analyzes the photovoltaic power generation potential of different positions in the region according to the regional photovoltaic power generation capacity data to generate spatial distribution data of the regional light power generation potential;

[0050] The demand identification module identifies the spatial aggregation characteristics of user charging demand according to the regional user charging demand data to generate spatial aggregation position data of user charging demand;

[0051] The position matching module analyzes the spatial matching degree of the light power generation potential and the user charging demand according to the spatial distribution data of the regional light power generation potential and the spatial aggregation position data of the user charging demand to generate spatial matching priority data of the best deployment position;

[0052] The path planning module plans the mobile charging pile deployment path according to the spatial matching priority data of the best deployment position to generate initial moving deployment path data;

[0053] A dynamic scheduling module: according to the initial mobile deployment path data, dynamically adjusting the charging pile mobile path, generating real-time optimal energy supplement scheduling path data.

[0054] The technical effects and advantages of the mobile charging pile auxiliary energy supplement method and system based on photovoltaic of the application are as follows:

[0055] Through real-time collection and preprocessing of light intensity and charging demand in the deployment area, high-precision photovoltaic power generation capacity and user demand data are obtained to ensure that the basic information is accurate and reliable; through spatial analysis of the photovoltaic power generation potential and user demand aggregation characteristics of the grid unit, the energy supply and demand distribution in the region is accurately described, avoiding blind deployment under the fixed deployment mode; through spatial matching priority calculation, the best deployment position is determined to realize efficient coupling of light power generation potential and user demand and reduce energy waste; based on priority data, the optimal initial deployment path is generated to shorten the driving distance and time and improve the scheduling efficiency; the path is dynamically adjusted to respond to light fluctuations and traffic changes to ensure continuous and stable energy supplement service. The reliability and response speed of the auxiliary energy supplement system are enhanced, the endurance guarantee capability of the electric vehicle is improved, and the operation cost is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 A mobile charging pile auxiliary energy supplement method based on photovoltaic of the application is shown in the figure;

[0057] Figure 2 A structure diagram of the mobile charging pile auxiliary energy supplement system based on photovoltaic of the application is shown in the figure. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application.

[0059] Embodiment 1: Figure 1 A mobile charging pile auxiliary energy supplement method based on photovoltaic of the application is given, which includes the following steps:

[0060] Collect real-time light intensity data of the preset deployment area of the photovoltaic mobile charging pile and real-time charging demand data of the electric vehicle, and perform data preprocessing to generate regional photovoltaic power generation capacity data and regional user charging demand data;

[0061] According to the regional photovoltaic power generation capacity data, analyze the photovoltaic power generation potential of different positions in the region to generate spatial distribution data of the regional light power generation potential;

[0062] According to the regional user charging demand data, the spatial aggregation characteristics of the user charging demand are identified, and spatial aggregation position data of the user charging demand is generated;

[0063] According to the spatial distribution data of the regional light power generation potential and the spatial aggregation position data of the user charging demand, the spatial matching degree of the light power generation potential and the user charging demand is analyzed, and spatial matching priority data of the optimal deployment position is generated;

[0064] According to the spatial matching priority data of the optimal deployment position, a mobile charging pile deployment path is planned, and initial mobile deployment path data is generated;

[0065] According to the initial mobile deployment path data, the charging pile mobile path is dynamically adjusted, and real-time optimal energy supplement scheduling path data is generated.

[0066] The real-time light intensity data of the preset deployment area of the photovoltaic mobile charging pile and the real-time charging demand data of the electric vehicle are collected, and data preprocessing is performed, and regional photovoltaic power generation capacity data and regional user charging demand data are generated, including:

[0067] The real-time light intensity data of different positions in the preset deployment area of the photovoltaic mobile charging pile and the real-time charging demand data of the electric vehicle are collected;

[0068] The preset photovoltaic mobile charging pile deployment area is, for example, a parking area in a city transportation hub station or a service area on a highway. Assuming that the parking lot in the city transportation hub station is selected as the preset deployment area, the area is a rectangular field with a length of 200 meters and a width of 100 meters. Inside the preset deployment area, the field is divided into a grid according to the spatial coordinates, and the area of each grid unit can be 10 square meters. For example, the entire deployment area is divided into 2000 unit grids. The instantaneous solar radiation intensity data at each grid location is collected in real time by using a light intensity monitoring device, such as a high-precision light sensor, deployed at each grid location. Each light intensity monitoring device records the real-time solar radiation intensity at the grid location at a sampling period of 5 seconds. For example, the real-time light intensity data measured at a grid location in the sampling period is 800 watts per square meter. At the same time, the real-time charging demand information of the electric vehicles in the preset deployment area is collected by the vehicle monitoring and charging demand information collection device deployed at the entrance of the parking lot and each parking space. For example, when the electric vehicle enters the parking area, the unique identification information of the vehicle is recorded by the vehicle identification device arranged at the entrance, and the real-time charging demand data sent from the vehicle is received by the wireless communication module on the parking space after the vehicle is parked in the parking space. The real-time charging demand data includes the current battery remaining capacity, the expected charging capacity, and the expected parking time of the vehicle. For example, the data sent by the electric vehicle is: the battery remaining capacity is 20%, the expected charging capacity is 30 kilowatt-hours, and the expected parking time is 45 minutes. Through the above method, the real-time light intensity data at different positions of the preset deployment area and the real-time charging demand data sent by all electric vehicles parked in the deployment area can be obtained.

[0069] The real-time light intensity data and the real-time charging demand data are preprocessed.

[0070] The data preprocessing includes integrity check, missing data filling, abnormal data elimination and data standardization processing. For real-time light intensity data, if the light intensity data at a certain grid cell position is missing in a certain sampling period, the light intensity of the adjacent grid cell position is used to fill the missing data based on the spatial interpolation method. For example, the missing real-time light intensity data of a certain grid cell can be linearly interpolated and filled using the measured values of the surrounding four adjacent grid cells. For abnormal data, for example, when the measured value exceeds 1200 watts per square meter, the abnormal data is deleted and reasonable light intensity is obtained by interpolation. The collected vehicle charging demand data is subjected to data integrity check, and when it is detected that the charging demand data sent by the vehicle is incomplete or incorrect, for example, the vehicle expects to supplement the charging capacity exceeding the total capacity of the vehicle battery, the real-time charging demand data of the vehicle is eliminated or corrected, including calling the historical charging data of the vehicle or the standard parameters of vehicle charging to fill reasonable data. After completing the data missing filling and abnormal data elimination processing, the real-time light intensity data and the real-time charging demand data are subjected to standardization processing respectively.

[0071] Based on the real-time light intensity data after data preprocessing, the regional photovoltaic power generation capacity data is obtained.

[0072] The real-time light intensity after preprocessing, the photovoltaic panel area in the grid cell and the predetermined photovoltaic power generation efficiency coefficient are used for calculation to obtain the photovoltaic power generation capacity of each grid cell position. For example, the standardized processing of the light intensity of a certain grid cell position is 0.8, the photovoltaic panel area is 5 square meters, and the photovoltaic power generation efficiency coefficient is 20%, then the photovoltaic power generation capacity of the grid cell position is 0.8×800 watts per square meter×5 square meters×20%=640 watts, that is, the photovoltaic power generation capacity of this grid cell position at present is 640 watts. The above calculation is performed for each grid cell position in the deployment area, and finally the regional photovoltaic power generation capacity data of the entire predetermined deployment area is formed.

[0073] Based on the real-time charging demand data after data preprocessing, the regional user charging demand data is obtained.

[0074] The charging demand intensity corresponding to the location of each electric vehicle is calculated using real-time charging demand data of the vehicle, and the demand intensity index is determined in combination with the current electric quantity and the expected supplemental electric quantity of the vehicle. For example, the current battery capacity of the vehicle is 60 kWh, the remaining electric quantity is 20%, the expected supplemental electric quantity is 30 kWh, and the stay time is 45 minutes. The charging demand intensity of the vehicle is calculated as 30 kWh divided by 0.75 hours, and the result is 40 kW. The real-time charging demand intensity of the vehicle is 40 kW. By calculating the real-time charging demand data of all electric vehicles entering the deployment area, the charging demand intensity corresponding to all users in the area is obtained, and the charging demand intensity is associated with the parking space coordinate position of the vehicle to form complete regional user charging demand data.

[0075] According to the regional photovoltaic power generation capacity data, the photovoltaic power generation potential of different positions in the region is analyzed, and the spatial distribution data of the regional light power generation potential is generated, including:

[0076] The regional photovoltaic power generation capacity data is divided into a plurality of grid units according to a preset spatial resolution, and a grid number is established;

[0077] The preset deployment area is divided into a plurality of spatial grid units, for example, the preset deployment area is selected as the parking area in the city traffic hub station, the site size is a rectangular area with a length of 200 meters and a width of 100 meters, the whole deployment area is divided into 2000 grid units with equal area, and the area of each grid unit is 10 square meters (i.e. the preset spatial resolution). Each grid unit is assigned a unique identification number, which can use a horizontal and vertical coordinate method, for example, the grid unit at the lower left corner is numbered (1, 1), the adjacent grid units to the right are numbered in ascending order, the grid unit to the right of the first grid unit is numbered (1, 2), and so on, until the grid unit at the upper right corner is numbered (20, 100). Through the above process, each grid unit has spatial coordinate information and corresponds to a unique grid number, and each grid unit has independent regional photovoltaic power generation capacity data.

[0078] The light radiation intensity in each grid unit is corrected to obtain a corrected light radiation intensity value;

[0079] The shading of buildings, trees or fixed facilities at different grid cell positions can reduce the actual effective light radiation intensity. Therefore, the light radiation intensity in each grid cell needs to be corrected; a shading proportion coefficient in each grid cell is preset, for example, the grid cell is affected by tree shading, and the measured ground shading proportion coefficient of the grid cell is 0.15, at this time, the original regional photovoltaic power generation capacity data of the grid cell is 640 watts, and the corrected light radiation intensity calculation of the grid cell is 640 watts x (1-0.15) = 544 watts. According to the same correction calculation method, each grid cell is calculated respectively to form the corrected light radiation intensity value of each grid cell position.

[0080] Based on the corrected light radiation intensity value and the grid area, the photovoltaic power generation potential index of the grid cell is calculated to obtain a photovoltaic power generation potential index list.

[0081] The photovoltaic power generation potential index represents the photovoltaic power generation capacity that can be achieved at the grid cell position within a given time. When calculating the photovoltaic power generation potential index, the corrected light radiation intensity value, the photovoltaic component area and the preset conversion efficiency coefficient of the photovoltaic component in the grid cell are used. Assuming that a specific grid cell is taken as an example, the corrected light radiation intensity value is 544 watts, the actual deployment area of the photovoltaic component in the grid cell is 5 square meters, and the preset conversion efficiency coefficient of the photovoltaic component is 20%, then the photovoltaic power generation potential index calculation process of the grid cell is 544 watts x 5 square meters x 20% = 544 watts. The above photovoltaic power generation potential index calculation is performed on all grid cells to obtain a complete data set containing all grid cell numbers and corresponding photovoltaic power generation potential indexes, i.e. a photovoltaic power generation potential index list.

[0082] The photovoltaic power generation potential index list is mapped to the corresponding grid cell coordinates to construct a photovoltaic power generation potential space matrix.

[0083] The defined grid cell number is used to map the calculated photovoltaic power generation potential index to the grid cell space coordinate position, for example, the grid cell number (5, 10) corresponds to the position point 50 meters away from the starting point in the horizontal direction and 100 meters away from the starting point in the vertical direction. If the calculated photovoltaic power generation potential index of the grid cell is 550 watts, then in the photovoltaic power generation potential space matrix, the data corresponding to the space coordinate position (5, 10) is 550 watts. Through the above mapping, the photovoltaic power generation potential indexes of all grid cells are mapped to the corresponding coordinate positions to construct a photovoltaic power generation potential space matrix. The photovoltaic power generation potential space matrix can reflect the spatial distribution characteristics of the photovoltaic power generation potential of each position in the preset deployment area.

[0084] The spatial matrix of photovoltaic power generation potential is spatially interpolated to generate spatial distribution data of regional light power generation potential.

[0085] The interpolation method can select a spatial interpolation calculation method, such as inverse distance weighting interpolation. Taking the inverse distance weighting interpolation method as an example, for any unmeasured point position in the spatial matrix, a certain number of grid cells closest to the unmeasured point are taken, for example, the closest 4 grid cells, and the spatial distance from the unmeasured point to the known measured point is calculated respectively. According to the spatial distance, the weight coefficient of each measured point is calculated, the closer the distance, the greater the weight of the measured point, and the farther the distance, the smaller the weight of the measured point. Assuming that the distances of the unmeasured point from the closest 4 measured points are 5 meters, 8 meters, 10 meters and 12 meters respectively, the weight coefficient can be calculated in the form of the proportion of the reciprocal of the distance to the total reciprocal. The photovoltaic power generation potential value of the unmeasured point is calculated by using the calculated weight coefficient, so as to realize the spatial interpolation operation. After the spatial interpolation calculation of all unmeasured point positions in the region is completed, the spatial distribution data of the regional light power generation potential is obtained.

[0086] According to the regional user charging demand data, the spatial aggregation characteristics of the user charging demand are identified, and the spatial aggregation position data of the user charging demand is generated, including:

[0087] The regional user charging demand data is mapped into a discrete demand point coordinate set according to a preset spatial resolution;

[0088] The parking area in the city traffic hub station is selected as the preset deployment area, the parking area size is a rectangular area with a length of 200 meters and a width of 100 meters, and a total of 100 parking spaces are provided in the parking area, each parking space has a unique spatial coordinate. After the vehicle is parked in the parking space, the vehicle monitoring and charging demand information acquisition device deployed on the parking space can obtain the spatial coordinate position of the vehicle entering the parking space and the real-time charging demand information sent by the vehicle, for example, the specific parking space with a parking space number A12 has a spatial coordinate position of 120 meters in the horizontal direction and 30 meters in the vertical direction from the starting point (southwest corner) of the parking area, and the real-time charging demand intensity of the vehicle parked in the sampling period is 40 kW. In the preset spatial resolution, the parking space position is mapped into an independent discrete demand point coordinate, which is recorded as the coordinate position (120 meters, 30 meters), and the charging demand intensity value of 40 kW is saved as the data attribute of the demand point coordinate. In the same way, all parking space positions in the deployment area are mapped into discrete demand point coordinates, and all corresponding real-time charging demand intensities are recorded, so as to form a complete discrete demand point coordinate set. The discrete demand point coordinate set contains all parking space positions and corresponding user charging demand intensities.

[0089] The spatial density of the discrete demand point coordinate set is analyzed, and the neighborhood charging demand density value is calculated.

[0090] The spatial density analysis is to take each discrete demand point in the discrete demand point coordinate set as the center, and to count the sum of the charging demand intensity of other demand points within a given spatial search radius, to determine the neighborhood charging demand density value at the position of the center demand point. The spatial search radius is preset, for example, set to 20 meters. For any one demand point in the discrete demand point coordinate set, the sum of the charging demand intensity of all discrete demand points within a radius of 20 meters around the demand point position is calculated. For example, taking the discrete demand point coordinate (120 meters, 30 meters) as an example, assuming that the charging demand intensity is 40 kW, and there are another three discrete demand point coordinates within the spatial search radius of 20 meters, corresponding to charging demand intensity of 35 kW, 25 kW and 30 kW, respectively, then the neighborhood charging demand density value is 40 kW + 35 kW + 25 kW + 30 kW = 130 kW. The neighborhood density of all discrete demand points in the discrete demand point coordinate set is calculated in the same way, and finally the neighborhood charging demand density value corresponding to each discrete demand point position is obtained.

[0091] An initial charging demand aggregation region set is obtained by using the density threshold and the minimum aggregation scale threshold to identify the charging demand aggregation region.

[0092] In order to realize the identification of the spatial distribution characteristics of user charging demand, it is necessary to preset the density threshold and the minimum aggregation scale threshold. The density threshold represents the minimum requirement for the neighborhood charging demand density value, for example, the density threshold is set to 100 kW, and the minimum aggregation scale threshold is 3 demand points. The charging demand aggregation region is identified: first, check the neighborhood charging demand density value corresponding to each discrete demand point. If the neighborhood density value of the demand point exceeds the preset density threshold of 100 kW, the demand point is temporarily marked as a potential aggregation region core point. Then check whether the number of demand points within the spatial search radius centered on the core point meets the minimum aggregation scale threshold requirement, that is, whether there are at least 3 demand points (including the core point itself) within the 20-meter search radius of the core point. If the above conditions are met, the region within the 20-meter radius of the core point is defined as the initial charging demand aggregation region. For example, the discrete demand point coordinate position (120 meters, 30 meters) has a neighborhood charging demand density value of 130 kW, which exceeds the density threshold of 100 kW, and the number of demand points within the spatial search radius is 4, which meets the minimum aggregation scale threshold condition. Therefore, the region within the 20-meter radius centered on the coordinate position (120 meters, 30 meters) is defined as the initial charging demand aggregation region. The aggregation region is identified at all demand point positions in the deployment region according to the above method, and finally a set of data of multiple initial charging demand aggregation regions, i.e., the initial charging demand aggregation region set, is obtained.

[0093] The connectivity of the initial charging demand aggregation area set is checked, adjacent overlapping aggregation areas are merged, and a charging demand aggregation area list is determined;

[0094] The connectivity of the initial charging demand aggregation area set is checked, adjacent overlapping aggregation areas are merged, and a charging demand aggregation area list is determined;

[0095] The geometric center coordinates of each charging demand aggregation area are extracted to form user charging demand spatial aggregation location data;

[0096] The geometric center coordinates of each charging demand aggregation area are extracted to form user charging demand spatial aggregation location data;

[0097] According to the spatial distribution data of the regional light power generation potential and the spatial aggregation location data of the user charging demand, the spatial matching degree of the light power generation potential and the user charging demand is analyzed, and the spatial matching priority data of the best deployment location is generated, including:

[0098] The spatial distribution data of the regional light power generation potential and the spatial aggregation location data of the user charging demand are rasterized and fused through a unified coordinate system to establish a public grid coordinate system;

[0099] The spatial distribution data of the regional light illumination power generation potential and the spatial aggregation location data of the user charging demand both show a plurality of coordinate points distributed in a preset deployment area in the spatial dimension, and the coordinate positions can be different, so a unified coordinate system needs to be established to ensure that the fusion processing can be performed in the same spatial reference system. Taking a parking area in a city transportation hub station as the preset deployment area for example, the preset deployment area has a size of 200 meters long and 100 meters wide, and an area of 20000 square meters. The establishment method of the public grid coordinate system is as follows: the entire preset deployment area is divided into grid units with a unit area of 10 square meters, and a total of 2000 grid units are divided; each grid unit has a determined spatial position coordinate, for example, the horizontal number of the grid unit is from 1 to 20, and the vertical number is from 1 to 100, forming a unique numbering system; taking the southwest corner of the site as the spatial origin (0 meters, 0 meters), then a grid unit center coordinate is formed every 10 meters in the horizontal direction, and a grid unit center coordinate is formed every 1 meter in the vertical direction; for example, the grid unit number (5, 10) corresponds to a horizontal center position 50 meters away from the spatial origin and a vertical center position 10 meters away from the spatial origin, forming a public grid coordinate system; the spatial distribution data of the regional light illumination power generation potential and the spatial aggregation location data of the user charging demand are uniformly mapped to the public grid coordinate system, and the grid fusion of the data is completed, that is, the corresponding light illumination power generation potential value and the user charging demand aggregation density value are recorded in each grid unit.

[0100] In the public grid coordinate system, the difference between the photovoltaic power generation potential value and the charging demand density value in each grid unit is calculated to form a spatial difference matrix.

[0101] In the grid fusion process, each grid unit has a photovoltaic power generation potential value and a charging demand density value after uniform spatial mapping. For example, the grid unit number is (5, 10), the mapped photovoltaic power generation potential value is 550 watts, the charging demand density value is 480 watts, and the difference is 70 watts. The same method is used to calculate the difference of all grid units in the preset deployment area, so as to complete the construction of the entire spatial difference matrix. The spatial difference matrix can reflect the spatial matching condition between the photovoltaic power generation potential and the user charging demand density at different positions in the preset deployment area. The greater the difference, the higher the photovoltaic power generation potential relative to the charging demand density. The smaller the difference or even the negative value indicates that the photovoltaic power generation potential is insufficient relative to the charging demand density, and there is a matching problem.

[0102] The spatial difference matrix is normalized and calculated with a preset weight coefficient to obtain the spatial matching score of each grid unit.

[0103] The maximum value and the minimum value of the difference values of all grid cells in the spatial difference matrix are calculated, and the difference values of each grid cell are converted to the range of 0 to 1 by using a maximum and minimum value normalization algorithm; taking the grid cell number (5, 10) as an example, the difference value is 70 watts, assuming that the maximum value of the difference values of all grid cells in the spatial difference matrix is 100 watts and the minimum value is -50 watts, then the normalized value of this grid cell is calculated as (70-(-50)) / (100-(-50)) = 120 / 150 = 0.8; all grid cells in the spatial difference matrix are normalized in the same way to form a spatial difference standardized matrix. After standardization, the spatial difference standardized matrix is weighted calculated combined with the pre-set weight coefficient to reflect the priority degree of the spatial matching of the photovoltaic power generation potential and the user demand. The pre-set weight coefficient is set according to the actual application scene, for example, the importance of photovoltaic resource utilization or the importance of charging demand satisfaction is determined as two weight coefficients of 0.6 and 0.4; the calculation method of the spatial matching score of each grid cell is that the standardized spatial difference value is multiplied by 0.6, and then the charging demand density standardized value is multiplied by 0.4; for example, the standardized spatial difference value of the grid cell number (5, 10) is 0.8, and the charging demand density standardized value is 0.7, so the spatial matching score is calculated as 0.76; the spatial matching score of all grid cells in the pre-set deployment area is calculated to form a complete spatial matching score matrix.

[0104] The spatial matching priority data of the best deployment position is sorted from high to low according to the spatial matching score, and the output is the spatial matching priority data of the best deployment position;

[0105] Each grid cell in the spatial matching score matrix has a unique spatial matching score, in order to determine the best deployment position of the mobile charging pile, all grid cells need to be sorted; all grid cells are sorted in order from large to small according to the spatial matching score, and the higher the spatial matching score, the higher the matching degree of the photovoltaic power generation potential and the user charging demand, and after sorting, a spatial matching priority list is formed; for example, after sorting, the first position number is (12, 45), the spatial matching score is 0.95, the second position number is (10, 35), the spatial matching score is 0.93, and so on, until the last grid cell. The spatial matching priority data of the best deployment position is a data set of the grid cell number and the corresponding spatial matching score after sorting.

[0106] According to the spatial matching priority data of the best deployment position, the deployment path of the mobile charging pile is planned, and the initial mobile deployment path data is generated, including:

[0107] The spatial matching priority data of the best deployment position is read, and a number of best deployment positions are selected according to the priority threshold to generate a candidate deployment position set;

[0108] In order to realize the effective deployment of mobile charging piles, a number of grid cells with high spatial matching scores are selected from all grid cells as potential optimal deployment positions, and therefore a priority threshold needs to be set in advance, which can be set in the form of a numerical threshold of spatial matching score or a ranking method. For example, the first grid cell in the parking area of the urban transportation hub station is numbered (12, 45) and has a spatial matching score of 0.95, the second grid cell is numbered (10, 35) and has a spatial matching score of 0.93, the third grid cell is numbered (8, 22) and has a spatial matching score of 0.92, and so on, and a total of 2000 grid cell position data are sorted. At this time, the priority threshold can be set to 0.90, that is, only the grid cells with a spatial matching score greater than or equal to 0.90 are selected as the final candidate deployment positions. After screening, for example, there are 15 grid cells that meet the threshold condition, and the positions of the above 15 grid cells are confirmed as the optimal deployment positions and form a candidate deployment position set. The data of each position in the candidate deployment position set includes the grid cell number and the spatial coordinate position of the grid cell, for example, the spatial coordinates of the grid cell with position number (12, 45) are 120 meters away from the spatial origin in the horizontal direction and 45 meters away from the spatial origin in the vertical direction.

[0109] The candidate deployment position set is mapped to a node set, and a directed weighted graph is constructed in combination with the road connectivity information of the deployment area.

[0110] The candidate deployment position set has formed a plurality of position nodes, each position node having a determined spatial coordinate position; in order to plan the mobile charging pile to move between a plurality of candidate deployment positions, it is necessary to abstract the candidate positions as a node set in graph theory analysis, and construct a directed weighted graph based on the connectivity of the actual road network within the deployment area. Based on the road traffic network around the preset deployment area (for example, the parking lot of the urban traffic hub), the road connectivity between each candidate deployment position in the area is obtained by field measurement or using the road data provided by the existing traffic management system, including road length, road speed limit and one-way or two-way traffic rules. For example, the actual road length between two candidate deployment position grid cell numbers (12, 45) and (10, 35) is 150 meters, the maximum allowable speed is 30 kilometers per hour, and one-way traffic is allowed. In the process of constructing the directed weighted graph, the node set of the graph is each position coordinate point in the candidate deployment position set, and if there is road connectivity between each node, a directed edge is added in the directed graph. The weight data of the directed edge is determined according to the road length, speed and actual traffic rules; for example, the weight of the directed edge between the above two nodes can be calculated as 150 meters divided by (30 kilometers / hour converted to 8.33 meters / second), obtaining the time weight value of the edge as 18 seconds, and at the same time the road length 150 meters can be taken as the distance weight of the edge. Repeat the above method to analyze the connectivity between all candidate deployment position nodes to complete the construction of the entire directed weighted graph.

[0111] Set the upper limit of the driving distance of the mobile charging pile, the preset scheduling time window and the turning angle restriction as the constraint conditions on the directed weighted graph, solve the minimum weighted distance path, and obtain the candidate deployment position access sequence;

[0112] In order to realize the effective scheduling and deployment of mobile charging piles in actual use, considering the endurance, time requirement and road driving restriction conditions of mobile charging piles under real conditions, constraint conditions need to be added when solving the path. The upper limit of the driving distance of the mobile charging pile represents the maximum cumulative driving mileage allowed in the single dispatch driving process of the mobile charging pile, for example, the upper limit value can be set to 1500 meters; The preset dispatch time window represents the maximum running time allowed in the entire dispatch process, for example, the dispatch window is set to 30 minutes; The turning angle limit refers to the maximum turning angle allowed between adjacent roads, for example, considering the turning ability of the actual charging pile vehicle, the upper limit of the turning angle can be set to 90 degrees; For example, the edge from node number (12, 45) to node (10, 35) has a driving distance of 150 meters and a turning angle of 60 degrees, which meets the turning angle limit, so this edge is a valid edge; Using the shortest path algorithm, taking the initial deployment point node number (12, 45) as the starting point, the path is solved in combination with the constraint conditions to calculate the best path to visit all candidate deployment location nodes; Using classical path planning methods such as Dijkstra's shortest path algorithm or heuristic search algorithm, gradually explore the minimum path of weighted distance that meets the above distance, time and turning angle restrictions; Through solving, assuming that the final determined path order is: node (12, 45)-node (10, 35)-node (8, 22)-node (7, 18), all candidate deployment locations are traversed in turn; This result is the candidate deployment location access order.

[0113] According to the candidate deployment location access order, generate initial mobile deployment path data;

[0114] The initial mobile deployment path data is a series of spatial coordinate positions and path segments between adjacent positions arranged in order; For example, according to the candidate deployment location access order, the path starting point is node (12, 45), from node (12, 45) to node (10, 35) along the road network, continue to node (8, 22), and then to node (7, 18), the path data is recorded with spatial coordinates, road numbers, path distances, estimated driving times, and each node stop position data as attributes; For example, the path data format is: "starting point (12, 45)-road number A01, distance 150 meters, estimated 18 seconds-node (10, 35) stop point-road number A02, distance 120 meters, estimated 15 seconds-node (8, 22) stop point-road number A03, distance 100 meters, estimated 12 seconds-node (7, 18) stop point".

[0115] According to the initial mobile deployment path data, dynamically adjust the mobile charging pile path to generate real-time optimal energy supplement scheduling path data, including:

[0116] Real-time acquisition of light intensity monitoring data and traffic passing state data of the current position of the mobile charging pile;

[0117] In order to ensure that the photovoltaic mobile charging pile can continuously provide stable energy supplement service in the actual deployment process, it is necessary to collect the light intensity data of the current position of the charging pile and the current road traffic passing state data in real time. The position data of the mobile charging pile is provided by the vehicle-mounted positioning device installed on the charging pile, and the position coordinates are determined in real time based on the vehicle driving track, for example, the position coordinates are 120 meters away from the horizontal direction of the space origin of the deployment area and 45 meters away from the vertical direction. The light intensity monitoring data collection method is to install a real-time light intensity monitoring device on the top of the charging pile, such as a high-precision solar radiation light sensor, which measures the instantaneous solar radiation light intensity received by the current photovoltaic module surface in real time with a data collection period of 5 seconds. For example, at a certain sampling time, the light intensity at the current position of the charging pile is measured to be 750 watts per square meter. At the same time, through the traffic state monitoring device on the vehicle, such as vehicle-mounted radar or video sensor, the traffic passing state data of the current road position of the mobile charging pile is collected in real time, including real-time average passing speed and other indicators. For example, at a certain sampling time, the vehicle-mounted device measures the average speed of the road traffic passing at the current position of the charging pile to be 20 kilometers per hour, which is much lower than the normal speed of 30 kilometers per hour, indicating that there is a certain degree of congestion or slow-moving state on the current road. The collection process is carried out in a continuous and periodic manner, and the light intensity monitoring data and the road traffic passing state data of the current position of the mobile charging pile are obtained in real time.

[0118] Compare the real-time light intensity monitoring data with the regional photovoltaic power generation capacity data to calculate the photovoltaic power generation deviation index;

[0119] After obtaining the light intensity monitoring data of the current position, the light intensity monitoring data needs to be compared with the pre-calculated regional photovoltaic power generation capacity data to evaluate the difference between the actual photovoltaic power generation capacity of the current position and the predicted value. The regional photovoltaic power generation capacity data is the theoretical photovoltaic power generation capacity value in the grid cell. For example, the grid cell number corresponding to the current position is (12, 45), and the initial calculated regional photovoltaic power generation capacity is 640 watts. The actual photovoltaic power generation capacity calculated from the real-time monitoring data may be different, assuming that the real-time measured light intensity is 750 watts per square meter, the actual calculated photovoltaic power generation capacity is 600 watts. At this time, the photovoltaic power generation deviation index is calculated by subtracting the real-time measured power generation capacity from the regional predicted power generation capacity and then dividing by the regional predicted power generation capacity, i.e. (640 watts-600 watts) / 640 watts=6.25%. The deviation index is positive, indicating that the real-time photovoltaic power generation capacity is lower than the predicted value, and negative, indicating that the real-time power generation capacity is higher than the predicted value. According to the above method, the photovoltaic power generation deviation index of each sampling time is obtained.

[0120] The real-time traffic passing state data is compared with the initial mobile deployment path data to calculate a travel time deviation index;

[0121] The real-time traffic passing state data describes the traffic situation of the road where the mobile charging pile is currently located, and the initial mobile deployment path data contains the expected travel time and distance of each road section. In order to evaluate whether the current actual traffic condition affects the scheduled time arrangement of the initial deployment path, it is necessary to compare the real-time passing state data with the expected travel time of the corresponding road section in the initial path data. For example, the initial mobile deployment path data stipulates that the road section length between position (12, 45) and position (10, 35) is 150 meters, and the expected travel time is 18 seconds. The real-time traffic passing state data measures that the current actual passing speed is 20 kilometers per hour, which is converted to 5.56 meters per second. At this time, the actual expected travel time is 150 meters / 5.56 meters per second≈27 seconds, and the calculation method of the travel time deviation index is (27 seconds-18 seconds) / 18 seconds=50%. The positive value of the deviation index indicates that the actual passing time exceeds the initial prediction time, and the negative value indicates that the actual passing time is shorter than the initial prediction time. The travel time deviation index of each path section is calculated in real time in the same way to monitor the difference between the actual scheduling of the charging pile and the planned path.

[0122] The path correction trigger condition is set based on the photovoltaic power generation deviation index and the travel time deviation index, and it is judged whether the correction threshold is reached;

[0123] In order to ensure that the real-time scheduling path can be adjusted in time according to the actual conditions, it is necessary to set the path correction trigger condition. The path correction trigger condition is a preset photovoltaic power generation deviation index threshold and a travel time deviation index threshold. For example, the threshold of the photovoltaic power generation deviation index is set to ±5%, and the travel time deviation index threshold is set to ±30%. If any of the real-time monitoring and calculation indexes exceeds the preset threshold, it indicates that the actual situation has changed and it is not suitable to continue to follow the initial planned path. For example, the current photovoltaic power generation deviation index calculation result is 6.25%, and the travel time deviation index is 50%, both of which exceed the preset threshold, so the path correction trigger condition is met, and the decision of real-time path adjustment is needed.

[0124] When the correction threshold is reached, the node corresponding to the current position of the mobile charging pile is taken as the starting point, and the weighted distance minimum path is solved on the directed weighted graph to update the candidate deployment position access order;

[0125] The path correction trigger condition is met, indicating that the real-time situation is no longer suitable for continuing to deploy according to the initial path, so the path needs to be recalculated. At this time, the real-time position of the current mobile charging pile is taken as the starting point, for example, the current node position is (12, 45), and the path is solved again in the established directed weighted graph. The original mobile charging pile travel distance upper limit, scheduling time window and turning angle restriction are still considered when solving the path, and the path optimization algorithm such as Dijkstra algorithm or other classical shortest path algorithm is used again to solve the shortest path between the current position and all the remaining candidate deployment positions. For example, the path order after re-solving is node (12, 45)-node (8, 22)-node (10, 35)-node (7, 18), and the access order of the candidate deployment position is updated to reflect the real-time changes of traffic and photovoltaic power generation.

[0126] According to the updated candidate deployment position access order, real-time optimal energy supplement scheduling path data is generated;

[0127] After the updated candidate deployment position access order is determined, the access order and the path segment information in the directed weighted graph are used to generate real-time optimal energy supplement scheduling path data again. The real-time optimal energy supplement scheduling path data includes the coordinates of each node position, the road number, the travel distance and the expected real-time travel time of the path segment between nodes. For example, the real-time path data after regeneration is: "starting point (12, 45)-road number A05, distance 160 meters, expected travel time 20 seconds-node (8, 22) stopping point-road number A06, distance 140 meters, expected travel time 18 seconds-node (10, 35) stopping point-road number A07, distance 130 meters, expected travel time 16 seconds-node (7, 18) stopping point", which constitutes the real-time optimal charging pile scheduling path. The real-time optimal energy supplement scheduling path data replaces the initial path data and becomes the actual execution path of the charging pile in real-time operation, ensuring that the charging pile energy supplement task can continue efficiently according to the real-time traffic conditions and photovoltaic power generation capacity changes.

[0128] Embodiment 2: The difference between the embodiment 2 and the embodiment 1 of the application is that the embodiment 2 is to introduce a kind of photovoltaic-based mobile charging pile auxiliary energy supplement system.

[0129] Figure 2 The structure diagram of the photovoltaic-based mobile charging pile auxiliary energy supplement system is given, and the photovoltaic-based mobile charging pile auxiliary energy supplement system comprises:

[0130] The data acquisition module acquires the real-time illumination intensity data of the photovoltaic mobile charging pile in the preset deployment area and the real-time charging demand data of the electric vehicle, and performs data preprocessing to generate regional photovoltaic power generation capacity data and regional user charging demand data.

[0131] Potential analysis module: according to the regional photovoltaic power generation capacity data, the photovoltaic power generation potential of different positions in the region is analyzed, and the spatial distribution data of the regional photovoltaic power generation potential is generated;

[0132] Demand identification module: according to the regional user charging demand data, the spatial aggregation characteristics of user charging demand are identified, and the spatial aggregation position data of user charging demand is generated;

[0133] Position matching module: according to the spatial distribution data of the regional photovoltaic power generation potential and the spatial aggregation position data of the user charging demand, the spatial matching degree of the photovoltaic power generation potential and the user charging demand is analyzed, and the spatial matching priority data of the best deployment position is generated;

[0134] Path planning module: according to the spatial matching priority data of the best deployment position, the mobile charging pile deployment path is planned, and the initial mobile deployment path data is generated;

[0135] Dynamic scheduling module: according to the initial mobile deployment path data, the charging pile mobile path is dynamically adjusted, and the real-time optimal energy supplement scheduling path data is generated.

[0136] The above formulas are all dimensionless numerical calculations, the formulas are obtained by collecting a large amount of data to simulate the most recent real situation, and the preset parameters and threshold values in the formulas are set by the person skilled in the art according to the actual situation.

[0137] The above embodiments can be realized wholly or partially by software, hardware, firmware or any other combination. When realized by software, the above embodiments can be realized in the form of a computer program product wholly or partially. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices. The computer instructions can be stored in a computer readable storage medium or transferred from one computer readable storage medium to another, for example, the computer instructions can be transferred from one website, computer, server or data center to another by wired (for example, infrared, wireless, microwave, etc.) mode. The computer readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center and the like containing one or more available medium sets. The available medium can be a magnetic medium (for example, floppy disk, hard disk, magnetic tape), optical medium (for example, DVD) or semiconductor medium. The semiconductor medium can be a solid state disk.

[0138] Those of skill in the art would understand that the modules and algorithms described in connection with the examples described herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. The

[0139] Those of skill in the art would understand that, for the purposes of description and enabling clarity, the specific process of the system, device and module described above can refer to the corresponding process in the foregoing method embodiments, which will not be described again.

[0140] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0141] The modules described as separate components can or can not be physically separated, and the components displayed as modules can or can not be physical modules, which can be located in one place or distributed on multiple network modules. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiment.

[0142] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically, or two or more modules can be integrated in one module.

[0143] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application essentially or the parts that contribute to the prior art or parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0145] Finally: the above is only a preferred embodiment of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A photovoltaic-based auxiliary energy replenishment method for mobile charging piles, characterized in that, Includes the following steps: Real-time solar irradiance data and real-time charging demand data of electric vehicles are collected from the pre-deployment areas of photovoltaic mobile charging piles. Data preprocessing is then performed to generate regional photovoltaic power generation capacity data and regional user charging demand data. Specifically, Collect real-time light intensity data and real-time charging demand data of electric vehicles at different locations within the pre-deployment area of ​​photovoltaic mobile charging piles; Data preprocessing is performed on real-time light intensity data and real-time charging demand data; Based on the real-time light intensity data after data preprocessing, the regional photovoltaic power generation capacity data is obtained; Based on the real-time charging demand data after data preprocessing, regional user charging demand data is obtained. Based on regional photovoltaic (PV) power generation capacity data, the PV power generation potential at different locations within the region is analyzed, generating spatial distribution data of the region's solar power generation potential. Specifically, The regional photovoltaic power generation capacity data is divided into multiple grid units according to a preset spatial resolution and grid numbers are established. The illumination intensity within each grid cell is corrected to obtain the corrected illumination intensity value. Based on the corrected solar radiation intensity value and grid area, the photovoltaic power generation potential index of the grid cell is calculated, and a list of photovoltaic power generation potential indexes is obtained. Map the list of photovoltaic power generation potential indicators to the corresponding grid cell coordinates to construct a photovoltaic power generation potential spatial matrix; Spatial interpolation is performed on the spatial matrix of photovoltaic power generation potential to generate spatial distribution data of regional solar power generation potential. Based on regional user charging demand data, identify the spatial clustering characteristics of user charging demand and generate spatial clustering location data of user charging demand. Based on the spatial distribution data of regional solar power generation potential and the spatial clustering data of user charging demand, analyze the spatial matching degree between solar power generation potential and user charging demand, and generate spatial matching priority data for the best deployment location. Based on the spatial matching priority data of the optimal deployment location, the deployment path of the mobile charging pile is planned, and the initial mobile deployment path data is generated. Based on the initial mobile deployment path data, the mobile path of the charging pile is dynamically adjusted to generate real-time optimal energy replenishment scheduling path data.

2. The photovoltaic-based mobile charging pile auxiliary energy replenishment method according to claim 1, characterized in that, Based on regional user charging demand data, spatial clustering characteristics of user charging demand are identified, and spatial clustering location data of user charging demand is generated, specifically: The regional user charging demand data is mapped into a set of discrete demand point coordinates according to a preset spatial resolution. Spatial density analysis is performed on the set of discrete demand point coordinates to calculate the neighborhood charging demand density value. The density threshold and the minimum cluster size threshold are used to identify charging demand cluster areas, and an initial set of charging demand cluster areas is obtained. Perform connectivity checks on the initial set of charging demand clusters, merge adjacent and overlapping clusters, and determine the list of charging demand clusters. Extract the geometric center coordinates of each charging demand cluster area to form spatial cluster location data of user charging demand.

3. The photovoltaic-based mobile charging pile auxiliary energy replenishment method according to claim 2, characterized in that, Based on the spatial distribution data of regional solar power generation potential and the spatial clustering data of user charging demand, the spatial matching degree between solar power generation potential and user charging demand is analyzed to generate spatial matching priority data for optimal deployment locations, specifically: The spatial distribution data of regional solar power generation potential and the spatial aggregation data of user charging demand are rasterized and integrated through a unified coordinate system to establish a public grid coordinate system. Within the common grid coordinate system, the difference between the photovoltaic power generation potential value and the charging demand density value in each grid cell is calculated to form a spatial difference matrix; The spatial difference matrix is ​​normalized and the spatial matching score of each grid cell is calculated with preset weight coefficients. Sort the spatial matching scores from high to low and output the spatial matching priority data for the best deployment location.

4. The photovoltaic-based mobile charging pile auxiliary energy replenishment method according to claim 3, characterized in that, Based on the spatial matching priority data of the optimal deployment location, the deployment path of the mobile charging pile is planned, and the initial mobile deployment path data is generated, specifically as follows: Read the spatial matching priority data of the best deployment location, select several best deployment locations according to the priority threshold, and generate a set of candidate deployment locations; The candidate deployment location set is mapped to a node set, and a directed weighted graph is constructed by combining the road connectivity information of the deployment area; On a directed weighted graph, the upper limit of the driving distance of mobile charging piles, the preset scheduling time window and the turning angle limit are set as constraints. The path with the minimum weighted distance is solved to obtain the access order of candidate deployment locations. Initial mobile deployment path data is generated based on the access order of candidate deployment locations.

5. The photovoltaic-based mobile charging pile auxiliary energy replenishment method according to claim 4, characterized in that, Based on the initial mobile deployment path data, the mobile path of the charging piles is dynamically adjusted to generate real-time optimal energy replenishment scheduling path data, specifically: Real-time collection of light intensity monitoring data and traffic status data at the current location of mobile charging piles; The real-time light intensity monitoring data is compared with the regional photovoltaic power generation capacity data to calculate the photovoltaic power generation deviation index. The real-time traffic status data is compared with the initial mobile deployment path data to calculate the travel time deviation index; Based on the photovoltaic power generation deviation index and the driving time deviation index, set path correction trigger conditions and determine whether the correction threshold has been reached. When the correction threshold is reached, the node corresponding to the current location of the mobile charging pile is taken as the starting point, and the path with the minimum weighted distance is solved on the directed weighted graph to update the access order of the candidate deployment locations. Real-time optimal power replenishment scheduling path data is generated based on the updated access order of candidate deployment locations.

6. A photovoltaic-based mobile charging pile auxiliary power replenishment system, used to implement the photovoltaic-based mobile charging pile auxiliary power replenishment method according to any one of claims 1-5, characterized in that, include: Data acquisition module: Collects real-time irradiance data and real-time charging demand data of electric vehicles in the pre-deployment area of ​​photovoltaic mobile charging piles, and performs data preprocessing to generate regional photovoltaic power generation capacity data and regional user charging demand data. Potential Analysis Module: Based on regional photovoltaic power generation capacity data, analyze the photovoltaic power generation potential at different locations within the region and generate spatial distribution data of regional solar power generation potential; Demand identification module: Based on regional user charging demand data, identify the spatial clustering characteristics of user charging demand and generate spatial clustering location data of user charging demand; Location matching module: Based on the spatial distribution data of regional solar power generation potential and the spatial clustering data of user charging demand, analyze the degree of spatial matching between solar power generation potential and user charging demand, and generate spatial matching priority data for the best deployment location; Path planning module: Based on spatial matching priority data of the optimal deployment location, it plans the deployment path of the mobile charging pile and generates initial mobile deployment path data; Dynamic scheduling module: Based on the initial mobile deployment path data, dynamically adjust the mobile path of the charging pile and generate real-time optimal energy replenishment scheduling path data.

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