Charging pile construction planning method and system based on big data analysis

Abstract

The invention relates to the technical field of big data, in particular to a charging pile construction planning method and system based on big data analysis, and the method comprises the steps: obtaining a regional grid map, regional vehicle information and charging pile information, and judging whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range or not based on the charging pile information, when the overall charging utilization rate of at least one charging pile does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, determining a power supply and non-charging position which does not meet the preset charging utilization power range based on the charging pile information, and determining an association relationship between the charging application data and vehicle data in the regional vehicle information according to the charging / non-charging position, the regional grid map and the regional vehicle information, generating a charging construction scheme according to the association relationship, and controlling and displaying the charging construction scheme. The problem that the regional charging demand is larger than the charging pile supply quantity is solved.

Description

Technical Field

[0001] The present application relates to the technical field of big data, and in particular to a charging pile construction planning method and system based on big data analysis. Background Art

[0002] With the rapid development of the electric vehicle industry, the construction and planning of charging piles has become an indispensable part of urban infrastructure construction. In order to efficiently utilize resources and meet the growing demand for electric vehicle charging, it is particularly important to scientifically and rationally plan the layout of charging piles. Traditional charging pile planning methods mainly rely on manual experience or simple data statistics, which makes it difficult to fully consider the mobility of electric vehicles and changes in charging demand, resulting in unreasonable layout of charging piles and the problem that charging demand in some areas is greater than the supply of charging piles. Summary of the invention

[0003] In order to solve at least one of the above technical problems, the present application provides a charging pile construction planning method and system based on big data analysis.

[0004] In the first aspect, the present application provides a charging pile construction planning method based on big data analysis, which adopts the following technical solutions: Acquire a regional grid map, regional vehicle information, and charging pile information, wherein the regional grid map is a grid map of a region where charging pile construction is to be planned, the regional vehicle information is information on changes in the number of electric vehicles and flow distribution of electric vehicles in the region where charging pile construction is to be planned during a historical period, and the charging pile information is information on locations of charging piles that have been built during the historical period and charging application data of each built charging pile during the historical period; Determining whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information; If there is at least one charging pile whose overall charging utilization rate does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, a non-charging position that does not meet the preset charging utilization power range is determined based on the charging pile information, and an association relationship between the charging application data and the vehicle data in the regional vehicle information is determined based on the non-charging position, the regional grid map and the regional vehicle information; A charging construction plan is generated according to the association relationship, and the charging construction plan is controlled to be displayed.

[0005] In a possible implementation, judging whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information includes: Determine the charging power data of charging piles located at different locations within the historical period according to the charging pile information; Determine the charging application time of the charging pile based on the charging power data, calculate the ratio of the charging application time to the unit time, and percentile the calculated time ratio to obtain the charging utilization rate of each charging pile per unit time; Accumulate and calculate the charging utilization rate corresponding to each unit time in the historical cycle time period, use the accumulated calculation result as the numerator, and use the total number of unit time in the historical cycle time period as the denominator to calculate, so as to obtain a first charging utilization rate of each charging pile; Determine the charging areas that different charging piles are responsible for according to the charging pile information; Divide each charging pile into regional groups according to the responsible charging area to obtain different charging field areas; Determine whether the charging utilization rate of each charging pile in the charging field area satisfies the preset charging utilization rate. If so, record the historical full load moment when this phenomenon occurs in the historical period time period, and obtain the number of vehicle stops and the vehicle stop interval at the target position of the charging field area corresponding to the historical full load moment. The number of vehicle stops is the number of electric vehicles that stop at the target position in different vehicle single movement directions, and the vehicle stop interval is the time interval between adjacent vehicles stopping at the target position. Determine a second charging utilization rate of each charging pile in the responsible charging area according to the number of vehicle stops and the vehicle stop interval; Obtaining a vehicle update time after the historical full-load time, the vehicle update time being the time when the responsible charging area changes from a full-load state to a non-full-load state, the full-load state being that each charging pile in the responsible charging area is in a working state, and the non-full-load state being that at least one charging pile in the responsible charging area is in a non-working state; Determine the next full load time at which the responsible charging area becomes fully loaded again after the vehicle change time based on the historical full load time, and calculate the change time interval between the next full load time and the vehicle change time; Determine a third charging utilization rate of each charging pile in the responsible charging area according to the change time interval; Integrate and calculate the first charging utilization rate, the second charging utilization rate, and the third charging utilization rate according to a preset weight coefficient to obtain an overall charging utilization rate; The overall charging utilization rate is matched with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range.

[0006] In a possible implementation, determining the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the non-charging location, the regional grid map, and the regional vehicle information includes: Determine an abnormal charging service area according to the non-charging location and the regional grid map; Performing a regional association query on the abnormal charging service area and the regional vehicle information to obtain vehicle change information of the abnormal charging service area within the historical period; Determining abnormal charging data corresponding to the non-charging position in the charging application data; The vehicle change information and the abnormal charging data are subjected to correlation analysis to determine a correlation relationship between the charging application data and the vehicle data in the regional vehicle information.

[0007] In a possible implementation, the analyzing the vehicle change information and the abnormal charging data to determine the association between the charging application data and the vehicle data in the regional vehicle information includes: A vehicle coordinate system and a charging coordinate system are created respectively, and the vehicle change information is mapped to the vehicle coordinate system, and the abnormal charging data is mapped to the charging coordinate system. The X-axis of the vehicle coordinate system is the different time nodes in the historical period, and the Y-axis of the vehicle coordinate system is the vehicle data corresponding to the different time nodes in the historical period. The X-axis of the charging coordinate system is the time node equivalent to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is the charging power corresponding to the different time nodes in the historical period. Integrate the vehicle coordinate system and the charging coordinate system according to time nodes to obtain an integrated comprehensive coordinate system; Determine respectively a vehicle waveform diagram corresponding to the vehicle change information and a charging waveform diagram corresponding to the abnormal charging data according to the comprehensive coordinate system; Determine the periodic change rate of the charging waveform diagram when the vehicle waveform diagram undergoes the same rate change at different time nodes, and determine the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the periodic change rate.

[0008] In a possible implementation, the determining, based on the charging pile information, whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range further includes: If there is at least one charging pile whose overall charging utilization rate does not meet the preset charging utilization rate range and the overall charging utilization rate does not exceed the preset charging utilization rate range, then determining the overcharging position that does not meet the preset charging utilization power range based on the charging pile information; Determine an overcharging service area according to the overcharging position and the regional grid map; Performing a regional association query on the overcharging service area and the regional vehicle information to obtain target vehicle information of the overcharging service area within the historical period; Performing periodic change analysis on the target vehicle information to obtain future vehicle change information within a future preset time period; The future charging data of the overcharging location within the future preset time is determined according to the future vehicle change information and the association relationship, and it is judged whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If it still does not meet the range, a charging pile planning scheme is generated based on the future vehicle change information, the future charging data and the overcharging location.

[0009] In a possible implementation, the periodic change analysis of the target vehicle information to obtain future vehicle change information within a future preset time period includes: Determine the quantity change of the target vehicle information within the historical period time period, and determine the vehicle change trend based on the quantity change; Performing unsupervised time series data sorting on the vehicle change trend to obtain vehicle matrix data; Inputting the vehicle matrix data into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the vehicle matrix data to generate future vehicle matrix data; The future vehicle matrix data is input into a preset algorithm model for data calculation to obtain future vehicle change information within a preset time period.

[0010] In a possible implementation, the vehicle matrix data is input into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, including: Determine, based on the vehicle matrix data, periodic time periods of different peak value change periods in the quantity change situation and the average number of electric vehicles corresponding to the periodic time periods; Inputting the periodic time period and the mean value of the number of electric vehicles into the feature extraction model to extract vector features, respectively, to obtain time vector features corresponding to the periodic time period and data vector features corresponding to the mean value of the number of electric vehicles; Quantitative statistics are performed on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

[0011] In the second aspect, the present application provides a charging pile construction planning system based on big data analysis, which adopts the following technical solutions: A charging pile construction planning system based on big data analysis, comprising: An information acquisition module, used to acquire a regional grid map, regional vehicle information, and charging pile information, wherein the regional grid map is a grid map of a charging pile construction planning area to be carried out, the regional vehicle information is information on changes in the number of electric vehicles and changes in the flow distribution of electric vehicles in the charging pile construction planning area to be carried out during a historical period, and the charging pile information is information on the location of the charging piles that have been built during the historical period and charging application data of each built charging pile during the historical period; A utilization rate judgment module, used to judge whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information; a relationship determination module, for determining, based on the charging pile information, a non-charging position that does not meet the preset charging utilization power range when the overall charging utilization rate of at least one of the charging piles does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, and determining, based on the non-charging position, the regional grid map and the regional vehicle information, an association relationship between the charging application data and the vehicle data in the regional vehicle information; A scheme generating module is used to generate a charging construction scheme according to the association relationship, and control the display of the charging construction scheme.

[0012] In a possible implementation, when the utilization rate determination module determines whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information, it is specifically used to: Determine the charging power data of charging piles located at different locations within the historical period according to the charging pile information; Determine the charging application time of the charging pile based on the charging power data, calculate the ratio of the charging application time to the unit time, and percentile the calculated time ratio to obtain the charging utilization rate of each charging pile per unit time; Accumulate and calculate the charging utilization rate corresponding to each unit time in the historical cycle time period, use the accumulated calculation result as the numerator, and use the total number of unit time in the historical cycle time period as the denominator to calculate, so as to obtain a first charging utilization rate of each charging pile; Determine the charging areas that different charging piles are responsible for according to the charging pile information; Divide each charging pile into regional groups according to the responsible charging area to obtain different charging field areas; Determine whether the charging utilization rate of each charging pile in the charging field area satisfies the preset charging utilization rate. If so, record the historical full load moment when this phenomenon occurs in the historical period time period, and obtain the number of vehicle stops and the vehicle stop interval at the target position of the charging field area corresponding to the historical full load moment. The number of vehicle stops is the number of electric vehicles that stop at the target position in different vehicle single movement directions, and the vehicle stop interval is the time interval between adjacent vehicles stopping at the target position. Determine a second charging utilization rate of each charging pile in the responsible charging area according to the number of vehicle stops and the vehicle stop interval; Obtaining a vehicle update time after the historical full-load time, the vehicle update time being the time when the responsible charging area changes from a full-load state to a non-full-load state, the full-load state being that each charging pile in the responsible charging area is in a working state, and the non-full-load state being that at least one charging pile in the responsible charging area is in a non-working state; Determine the next full load time at which the responsible charging area becomes fully loaded again after the vehicle change time based on the historical full load time, and calculate the change time interval between the next full load time and the vehicle change time; Determine a third charging utilization rate of each charging pile in the responsible charging area according to the change time interval; Integrate and calculate the first charging utilization rate, the second charging utilization rate, and the third charging utilization rate according to a preset weight coefficient to obtain an overall charging utilization rate; The overall charging utilization rate is matched with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range.

[0013] In another possible implementation, when the relationship determination module determines the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the non-charging position, the regional grid map, and the regional vehicle information, it is specifically used to: Determine an abnormal charging service area according to the non-charging location and the regional grid map; Performing a regional association query on the abnormal charging service area and the regional vehicle information to obtain vehicle change information of the abnormal charging service area within the historical period; Determining abnormal charging data corresponding to the non-charging position in the charging application data; The vehicle change information and the abnormal charging data are subjected to correlation analysis to determine a correlation relationship between the charging application data and the vehicle data in the regional vehicle information.

[0014] In another possible implementation, when the relationship determination module analyzes the vehicle change information and the abnormal charging data to determine the association relationship between the charging application data and the vehicle data in the regional vehicle information, it is specifically used to: A vehicle coordinate system and a charging coordinate system are created respectively, and the vehicle change information is mapped to the vehicle coordinate system, and the abnormal charging data is mapped to the charging coordinate system. The X-axis of the vehicle coordinate system is the different time nodes in the historical period, and the Y-axis of the vehicle coordinate system is the vehicle data corresponding to the different time nodes in the historical period. The X-axis of the charging coordinate system is the time node equivalent to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is the charging power corresponding to the different time nodes in the historical period. Integrate the vehicle coordinate system and the charging coordinate system according to time nodes to obtain an integrated comprehensive coordinate system; Determine respectively a vehicle waveform diagram corresponding to the vehicle change information and a charging waveform diagram corresponding to the abnormal charging data according to the comprehensive coordinate system; Determine the periodic change rate of the charging waveform diagram when the vehicle waveform diagram undergoes the same rate change at different time nodes, and determine the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the periodic change rate.

[0015] In another possible implementation, the system further includes: a supply judgment module, an area determination module, an association query module, a vehicle prediction module, and a solution planning module, wherein: The overcharge judgment module is used to determine the overcharge position that does not meet the preset charging utilization power range based on the charging pile information if the overall charging utilization rate of at least one charging pile does not meet the preset charging utilization rate range and the overall charging utilization rate does not exceed the preset charging utilization rate range; The area determination module is used to determine the overcharging service area according to the overcharging position and the area grid map; The association query module is used to perform regional association query on the overcharging service area and the regional vehicle information to obtain target vehicle information of the overcharging service area within the historical period; The vehicle prediction module is used to perform periodic change analysis on the target vehicle information to obtain future vehicle change information within a future preset time period; The scheme planning module is used to determine the future charging data of the overcharging position within the future preset time according to the future vehicle change information and the association relationship, and to judge whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If it still does not meet the range, a charging pile planning scheme is generated based on the future vehicle change information, the future charging data and the overcharging position.

[0016] In another possible implementation, when the vehicle prediction module performs periodic change analysis on the target vehicle information to obtain future vehicle change information within a future preset time period, it is specifically used to: Determine the quantity change of the target vehicle information within the historical period time period, and determine the vehicle change trend based on the quantity change; Performing unsupervised time series data sorting on the vehicle change trend to obtain vehicle matrix data; Inputting the vehicle matrix data into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the vehicle matrix data to generate future vehicle matrix data; The future vehicle matrix data is input into a preset algorithm model for data calculation to obtain future vehicle change information within a preset time period.

[0017] In another possible implementation, when the vehicle prediction module inputs the vehicle matrix data into a trained feature extraction model to extract vector features and obtain feature dimensions and the number of feature dimensions, it is specifically used to: Determine, based on the vehicle matrix data, periodic time periods of different peak value change periods in the quantity change situation and the average number of electric vehicles corresponding to the periodic time periods; Inputting the periodic time period and the mean value of the number of electric vehicles into the feature extraction model to extract vector features, respectively, to obtain time vector features corresponding to the periodic time period and data vector features corresponding to the mean value of the number of electric vehicles; Quantitative statistics are performed on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

[0018] In a third aspect, the present application provides an electronic device, which adopts the following technical solution: at least one processor; Memory; At least one application, wherein at least one application is stored in a memory and configured to be executed by at least one processor, and at least one application is configured to: execute a charging pile construction planning method based on big data analysis as described in any one of the first aspects.

[0019] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to execute any of the charging pile construction planning methods based on big data analysis as described in the first aspect.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: In order to promptly discover and solve the problem that the regional charging demand is greater than the supply of charging piles, the present application obtains a regional grid map, regional vehicle information and charging pile information, wherein the regional grid map is a grid map of the area where the charging pile construction planning is to be carried out, the regional vehicle information is the change information of the number of electric vehicles and the change information of the flow distribution of electric vehicles in the area where the charging pile construction planning is to be carried out during the historical period time period, the charging pile information is the location information of the charging piles that have been built during the historical period time period and the charging application data of each built charging pile during the historical period time period, and then based on the charging pile information, it is judged whether the overall charging utilization rate of each of the charging piles meets the preset charging utilization rate range, when there is at least one charging pile whose overall charging utilization rate does not meet the preset charging utilization rate range, When the charging utilization rate range is preset and the overall charging utilization rate exceeds the preset charging utilization rate range, it means that there is a shortage of charging piles at present, that is, the regional charging demand is greater than the supply of charging piles. Therefore, based on the charging pile information, the charging or non-charging positions that do not meet the preset charging utilization power range are determined, and the correlation between the charging application data and the vehicle data in the regional vehicle information is determined according to the charging or non-charging positions, the regional grid map and the regional vehicle information, and a charging construction plan is generated according to the correlation, and the charging construction plan is controlled to be displayed. According to the prompted charging construction plan, the staff shall promptly plan and adjust the number of charging piles to be assembled and constructed in the area, thereby fundamentally solving the problem that the regional charging demand is greater than the supply of charging piles. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a charging pile construction planning method based on big data analysis is provided in an embodiment of the present application.

[0022] Figure 2 A schematic diagram of the structure of a charging pile construction planning system based on big data analysis provided in an embodiment of the present application.

[0023] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is combined with Figure 1-3 This application is described in further detail.

[0025] This specific embodiment is merely an explanation of the present application and is not a limitation of the present application. After reading this specification, a person skilled in the art may make non-creative modifications to the present embodiment as needed, but such modifications are protected by the patent law as long as they are within the scope of the present application.

[0026] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0028] The embodiments of the present application are further described in detail below in conjunction with the drawings in the specification.

[0029] The embodiment of the present application provides a method for planning the construction of charging piles based on big data analysis, which is executed by an electronic device, wherein the electronic device can be an independent physical electronic device, or an electronic device cluster or distributed system composed of multiple physical electronic devices, or a cloud electronic device that provides cloud computing services. The embodiment of the present application is not limited here, such as Figure 1 As shown, the method includes: Step S10: Obtain regional grid map, regional vehicle information and charging pile information.

[0030] Among them, the regional raster map is a raster map of the area where the charging pile construction is to be planned, the regional vehicle information is the change information on the number of electric vehicles and the change information on the flow distribution of electric vehicles in the area where the charging pile construction is to be planned during the historical period, and the charging pile information is the location information of the charging piles that have been built during the historical period and the charging application data of each built charging pile during the historical period.

[0031] For the embodiment of the present application, the regional grid map represents a graphical representation of the specific geographical area where the charging pile construction is to be planned, which is divided into several small grids (grids). It is used to intuitively display the geographical features and layout of the area. The regional vehicle information refers to the information about the increase or decrease in the number of electric vehicles in the area and the changes in the flow distribution of these electric vehicles in the area during a specific historical period. The charging pile information refers to the specific location information of the charging piles that have been built during the historical period and the usage of each charging pile, including charging application data such as the number of charging times and charging time. These data provide a basis for the utilization efficiency and distribution rationality of existing charging facilities.

[0032] In the embodiment of the present application, the power equipment uses GIS (geographic information system) technology to draw and obtain regional grid maps to ensure that the grid division is reasonable and can accurately reflect the regional topography; secondly, the vehicle monitoring equipment (such as cameras, sensors, etc.) installed in the area collects the driving trajectory data of electric vehicles, and combines big data analysis technology to obtain vehicle quantity changes and flow distribution information, and then obtains the location information and charging application data of the built charging piles through the charging pile management system or the data interface provided by the operator.

[0033] Step S11: judging whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information.

[0034] For the embodiment of the present application, the charging power data of the charging piles located at different location information within the historical period time period is determined according to the charging pile information, the charging application time of the charging pile is determined based on the charging power data, and the ratio of the charging application time to the unit time duration is calculated, and the calculated time ratio is percentage-percentaged to obtain the charging utilization rate of each charging pile per unit time, and then the charging utilization rate corresponding to each unit time in the historical period time period is accumulated and calculated, and the accumulated calculation result is used as the numerator, and the total number of unit time in the historical period time period is used as the denominator for calculation to obtain the first charging utilization rate of each charging pile, wherein the first charging utilization rate represents the utilization rate of the charging pile itself per unit time.

[0035] Then, the responsible charging areas of different charging piles are determined according to the charging pile information, and each charging pile is divided into regional groups according to the responsible charging areas to obtain different charging field areas, and it is judged whether the charging utilization rate of each charging pile in the charging field area meets the preset charging utilization rate. If so, the historical full load moment when this phenomenon occurs within the historical cycle time period is recorded, and the number of vehicle stops and the vehicle stop interval at the target position of the charging field area corresponding to the historical full load moment are obtained, the number of vehicle stops is the number of electric vehicles that stop at the target position in different vehicle single movement directions, and the vehicle stop interval is the time interval between adjacent vehicles stopping at the target position, and then the second charging utilization rate of each charging pile in the responsible charging area is determined according to the number of vehicle stops and the vehicle stop interval, wherein the second charging utilization rate indicates the overflow of vehicles to be charged when the charging pile is in a fully loaded state.

[0036] Then, the vehicle update time after the historical full load time is obtained, wherein the vehicle update time is the time when the full load state in the responsible charging area is changed to the non-full load state, the full load state is that each charging pile in the responsible charging area is in a working state, and the non-full load state is that there is at least one charging pile in the responsible charging area that is not in a working state, and the next full load time when the responsible charging area becomes full load again after the vehicle change time is determined based on the historical full load time, and the change time interval between the next full load time and the vehicle change time is calculated, and the third charging utilization rate of each charging pile in the responsible charging area is determined according to the change time interval, wherein the third charging utilization rate represents the switching rate of the charging pile working state.

[0037] Then, the first charging utilization rate, the second charging utilization rate and the third charging utilization rate are integrated and calculated according to a preset weight coefficient to obtain an overall charging utilization rate, and the overall charging utilization rate is matched with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range.

[0038] In the embodiment of the present application, the preset weight coefficient is preset by the staff, for example: the first charging utilization rate of the charging pile is related to the charging pile itself, so the weight of the first charging utilization rate of the charging pile is 0.5, and the third charging utilization rate of the charging pile is also related to the charging pile itself, but the third charging utilization rate can only highlight the urgency of the current charging pile application, so its weight is second to the first charging utilization rate, which is 0.3, and the second charging utilization rate of the charging pile is to indicate the number of vehicles waiting for charging when the charging piles are in operation. Since the number of vehicles is much greater than the number of charging piles, even if the utilization rate of the charging pile is 100%, it cannot indicate that the charging piles in the current area can meet the current charging expectations of electric vehicles. Therefore, the present application uses the second charging utilization rate to highlight the user's charging expectations for the charging piles, but the charging expectation is not a matter that is displayed, and it needs to be virtually calculated through the overflow of the charging vehicle, so the weight of the second charging utilization rate is lower than the third charging utilization rate, which is 0.2. That is, the overall charging utilization rate = 0.5*first charging utilization rate + 0.3*third charging utilization rate + 0.2*second charging utilization rate.

[0039] Specifically, the preset charging utilization rate range is a range set by the staff according to different overall charging utilization rates and corresponding actual charging conditions.

[0040] Step S12: If there is at least one charging pile whose overall charging utilization does not meet the preset charging utilization range and the overall charging utilization exceeds the preset charging utilization range, the charging or non-charging position that does not meet the preset charging utilization power range is determined based on the charging pile information, and the association relationship between the charging application data and the vehicle data in the regional vehicle information is determined based on the charging or non-charging position, the regional grid map and the regional vehicle information.

[0041] For the embodiments of the present application, the preset charging utilization rate range refers to a reasonable range of the overall charging utilization rate of a charging pile set in advance according to factors such as operating strategies or equipment performance, which is used to determine whether the charging efficiency of the charging pile meets the requirements. A location that is not available for charging refers to a location that is currently unable to provide charging services to more vehicles because the overall charging utilization rate of the charging pile exceeds the upper limit of the preset range, that is, a charging pile location that is in an "oversupply" state. The association between charging application data and vehicle data refers to matching and associating the charging application data of the charging pile (such as charging time, charging amount, etc.) with the specific information of vehicles in the area (such as the number of vehicles, etc.) for subsequent data analysis or operational decisions.

[0042] Specifically, the abnormal charging service area is determined according to the charging and non-charging locations and the regional grid map, and a regional correlation query is performed on the abnormal charging service area and the regional vehicle information to obtain the vehicle change information of the abnormal charging service area within the historical period, and the abnormal charging data corresponding to the charging and non-charging locations in the charging application data is determined, and a correlation analysis is performed on the vehicle change information and the abnormal charging data to determine the correlation relationship between the charging application data and the vehicle data in the regional vehicle information.

[0043] Specifically, a vehicle coordinate system and a charging coordinate system are created respectively, and the vehicle change information is mapped to the vehicle coordinate system, and the abnormal charging data is mapped to the charging coordinate system. The X-axis of the vehicle coordinate system is different time nodes in the historical period, and the Y-axis of the vehicle coordinate system is the vehicle data corresponding to different time nodes in the historical period. The X-axis of the charging coordinate system is the time node equivalent to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is the charging power corresponding to different time nodes in the historical period. The vehicle coordinate system and the charging coordinate system are integrated according to the time nodes to obtain an integrated comprehensive coordinate system, and then the vehicle waveform corresponding to the vehicle change information and the charging waveform corresponding to the abnormal charging data are determined according to the comprehensive coordinate system, and the periodic change rate of the charging waveform when the vehicle waveform undergoes the same rate change at different time nodes is determined, and the correlation between the charging application data and the vehicle data in the regional vehicle information is determined according to the periodic change rate.

[0044] Step S13: Generate a charging construction plan according to the association relationship, and control the display of the charging construction plan.

[0045] For the embodiment of the present application, since the charging piles are currently in short supply, it is necessary to plan the construction of charging piles. How to determine whether the charging piles after construction can meet people's charging needs and the utilization rate of the charging piles after construction is high? This application uses the association relationship to bind the vehicle situation with the charging utilization rate of the charging piles, and determines the corresponding relationship between the number of electric vehicles and the number of charging piles. According to the corresponding relationship, the charging piles are constructed, which has reached the most reasonable construction requirements. For example: when there are 40 electric vehicles in area A, the charging power of charging pile a is 40W, the charging power of charging pile b is 40W, the charging power of charging pile c is 35W, the charging power of charging pile d is 0W, and the charging power of charging pile e is 0W, which means that the 40 electric vehicles in area A need to correspond to 4 charging piles. However, these 4 charging piles are not fixed, because at different time stages, 40 electric vehicles may correspond to different numbers of application charging piles. Therefore, when determining the charging construction plan of the charging piles, this application is displayed in the form of a range of charging numbers, for example: 40 electric vehicles correspond to 4-5 charging piles.

[0046] In an embodiment of the present application, in order to promptly discover and solve the problem that the regional charging demand is greater than the supply of charging piles, the present application obtains a regional grid map, regional vehicle information, and charging pile information. The regional grid map is a grid map of the area to be planned for the construction of charging piles. The regional vehicle information is the change information of the number of electric vehicles and the change information of the flow distribution of electric vehicles in the area to be planned for the construction of charging piles in the historical period time period. The charging pile information is the location information of the charging piles that have been built in the historical period time period and the charging application data of each built charging pile in the historical period time period. Then, based on the charging pile information, it is determined whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range. When there is at least one charging pile with an overall charging utilization rate that satisfies the preset charging utilization rate range, the charging pile information is used to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range. When the utilization rate does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, it means that there is a shortage of charging piles at present, that is, the regional charging demand is greater than the supply of charging piles. Therefore, based on the charging pile information, the charging or non-charging positions that do not meet the preset charging utilization power range are determined, and the correlation between the charging application data and the vehicle data in the regional vehicle information is determined according to the charging or non-charging positions, the regional grid map and the regional vehicle information, and a charging construction plan is generated according to the correlation, and the charging construction plan is controlled and displayed. According to the prompted charging construction plan, the staff promptly plans and adjusts the number of charging piles to be assembled and constructed in the area, thereby fundamentally solving the problem that the regional charging demand is greater than the supply of charging piles.

[0047] A possible implementation method of the embodiment of the present application is to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range based on the charging pile information, and then also include: if there is at least one charging pile whose overall charging utilization rate does not meet the preset charging utilization rate range and the overall charging utilization rate does not exceed the preset charging utilization rate range, then determine the overcharging position that does not meet the preset charging utilization power range based on the charging pile information, determine the overcharging service area according to the overcharging position and the regional grid map, perform regional correlation query on the overcharging service area and regional vehicle information to obtain target vehicle information of the overcharging service area within the historical period, perform periodic change analysis on the target vehicle information to obtain future vehicle change information within a future preset time period, determine future charging data of the overcharging position within the future preset time period based on the future vehicle change information and the correlation relationship, and determine whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If it still does not meet the range, generate a charging pile planning scheme based on the future vehicle change information, future charging data and the overcharging position.

[0048] Specifically, the vehicle matrix data is input into a trained feature extraction model for vector feature extraction to obtain feature dimensions and the number of feature dimensions, including: determining the periodic time periods of different peak change periods in the quantity change situation and the mean number of electric vehicles corresponding to the periodic time periods based on the vehicle matrix data, respectively inputting the periodic time periods and the mean number of electric vehicles into the feature extraction model for vector feature extraction to obtain time vector features corresponding to the periodic time periods and data vector features corresponding to the mean number of electric vehicles, performing quantitative statistics on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

[0049] The following is an introduction to a charging pile construction planning system based on big data analysis provided in an embodiment of the present application. The charging pile construction planning system based on big data analysis described below and the charging pile construction planning method based on big data analysis described above can be referred to each other. Please refer to Figure 2 , Figure 2 : is a structural diagram of a charging pile construction planning system 20 based on big data analysis provided in an embodiment of the present application, including: The information acquisition module 21 is used to obtain a regional grid map, regional vehicle information and charging pile information. The regional grid map is a grid map of the area where the charging pile construction planning is to be carried out. The regional vehicle information is the change information of the number of electric vehicles and the change information of the flow distribution of electric vehicles in the area where the charging pile construction planning is to be carried out in the historical period time period. The charging pile information is the location information of the charging piles that have been built in the historical period time period and the charging application data of each built charging pile in the historical period time period; A utilization rate determination module 22 is used to determine whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information; A relationship determination module 23 is used to determine the charging or non-charging position that does not meet the preset charging utilization power range based on the charging pile information when the overall charging utilization rate of at least one charging pile does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, and determine the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the charging or non-charging position, the regional grid map and the regional vehicle information; The plan generation module 24 is used to generate a charging construction plan according to the association relationship and control the display of the charging construction plan.

[0050] In a possible implementation of the embodiment of the present application, when the utilization rate judgment module 22 judges whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range based on the charging pile information, it is specifically used to: Determine the charging power data of charging piles located at different locations within the historical period according to the charging pile information; Determine the charging application time of the charging pile based on the charging power data, calculate the ratio of the charging application time to the unit time, and percentile the calculated time ratio to obtain the charging utilization rate of each charging pile per unit time; Accumulate and calculate the charging utilization rate corresponding to each unit time in the historical cycle time period, use the accumulated calculation result as the numerator, and use the total number of unit time in the historical cycle time period as the denominator to calculate, so as to obtain a first charging utilization rate of each charging pile; Determine the charging areas that different charging piles are responsible for according to the charging pile information; Divide each charging pile into regional groups according to the responsible charging area to obtain different charging field areas; Determine whether the charging utilization rate of each charging pile in the charging field area satisfies the preset charging utilization rate. If so, record the historical full load moment when this phenomenon occurs in the historical period time period, and obtain the number of vehicle stops and the vehicle stop interval at the target position of the charging field area corresponding to the historical full load moment. The number of vehicle stops is the number of electric vehicles that stop at the target position in different vehicle single movement directions, and the vehicle stop interval is the time interval between adjacent vehicles stopping at the target position. Determine a second charging utilization rate of each charging pile in the responsible charging area according to the number of vehicle stops and the vehicle stop interval; Obtaining a vehicle update time after the historical full-load time, the vehicle update time being the time when the responsible charging area changes from a full-load state to a non-full-load state, the full-load state being that each charging pile in the responsible charging area is in a working state, and the non-full-load state being that at least one charging pile in the responsible charging area is in a non-working state; Determine the next full load time at which the responsible charging area becomes fully loaded again after the vehicle change time based on the historical full load time, and calculate the change time interval between the next full load time and the vehicle change time; Determine a third charging utilization rate of each charging pile in the responsible charging area according to the change time interval; Integrate and calculate the first charging utilization rate, the second charging utilization rate, and the third charging utilization rate according to a preset weight coefficient to obtain an overall charging utilization rate; The overall charging utilization rate is matched with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range.

[0051] In another possible implementation of the embodiment of the present application, when the relationship determination module 23 determines the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the charging position, the regional grid map and the regional vehicle information, it is specifically used to: Determine the abnormal charging service area based on the charging location and regional grid map; Perform regional association query on the abnormal charging service area and regional vehicle information to obtain vehicle change information in the abnormal charging service area within the historical period; Determine abnormal charging data corresponding to the non-charging position in the charging application data; Perform correlation analysis on vehicle change information and abnormal charging data to determine the correlation relationship between charging application data and vehicle data in regional vehicle information.

[0052] In another possible implementation of the embodiment of the present application, when the relationship determination module 23 analyzes the vehicle change information and the abnormal charging data to determine the association relationship between the charging application data and the vehicle data in the regional vehicle information, it is specifically used to: Create a vehicle coordinate system and a charging coordinate system respectively, and map the vehicle change information to the vehicle coordinate system, and map the abnormal charging data to the charging coordinate system. The X-axis of the vehicle coordinate system is the different time nodes in the historical period, and the Y-axis of the vehicle coordinate system is the vehicle data corresponding to the different time nodes in the historical period. The X-axis of the charging coordinate system is the time node equivalent to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is the charging power corresponding to the different time nodes in the historical period. Integrate the vehicle coordinate system and the charging coordinate system according to the time nodes to obtain an integrated comprehensive coordinate system; Determine the vehicle waveform diagram corresponding to the vehicle change information and the charging waveform diagram corresponding to the abnormal charging data respectively according to the comprehensive coordinate system; Determine the periodic change rate of the charging waveform diagram when the vehicle waveform diagram undergoes the same rate change at different time nodes, and determine the correlation relationship between the charging application data and the vehicle data in the regional vehicle information according to the periodic change rate.

[0053] In another possible implementation of the embodiment of the present application, the system 20 further includes: a supply judgment module, an area determination module, an association query module, a vehicle prediction module and a solution planning module, wherein: An overcharge judgment module is used to determine an overcharge position that does not meet the preset charging utilization power range based on the charging pile information if the overall charging utilization rate of at least one charging pile does not meet the preset charging utilization rate range and the overall charging utilization rate does not exceed the preset charging utilization rate range; An area determination module, used to determine an overcharging service area according to an overcharging location and an area grid map; An associated query module is used to perform regional associated query on the overcharging service area and regional vehicle information to obtain target vehicle information in the overcharging service area within a historical period; The vehicle prediction module is used to analyze the periodic changes of the target vehicle information and obtain the future vehicle change information within a preset time period; The scheme planning module is used to determine the future charging data of the overcharging location within a preset time in the future based on future vehicle change information and associated relationships, and to determine whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If it still does not meet the range, a charging pile planning scheme is generated based on future vehicle change information, future charging data and overcharging locations.

[0054] Another possible implementation method in the embodiment of the present application is to analyze the periodic changes of the target vehicle information by the vehicle prediction module to obtain the future vehicle change information within a preset time period in the future, specifically for: Determine the quantity change of target vehicle information within the historical period, and determine the vehicle change trend based on the quantity change; Unsupervised time series data sorting of vehicle change trends is performed to obtain vehicle matrix data; The vehicle matrix data is input into the trained feature extraction model to extract vector features, and the feature dimensions and the number of feature dimensions are obtained. The obtained feature dimensions and the number of feature dimensions are combined with the vehicle matrix data to generate future vehicle matrix data. The future vehicle matrix data is input into the preset algorithm model for data extrapolation to obtain the future vehicle change information within the preset time period.

[0055] In another possible implementation of the embodiment of the present application, when the vehicle prediction module inputs the vehicle matrix data into the trained feature extraction model to extract vector features and obtains the feature dimension and the number of feature dimensions, it is specifically used to: Determine the periodic time periods of different peak change periods in the quantity change situation and the mean value of the number of electric vehicles corresponding to the periodic time periods based on the vehicle matrix data; Inputting the periodic time period and the mean number of electric vehicles into the feature extraction model to extract vector features, respectively, to obtain time vector features corresponding to the periodic time period and data vector features corresponding to the mean number of electric vehicles; Perform quantitative statistics on data vector features and time vector features to obtain feature dimensions and the number of feature dimensions.

[0056] The present application embodiment provides an electronic device, such as Figure 3 As shown, Figure 3A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, Figure 3 The electronic device 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the electronic device 300 does not constitute a limitation on the embodiments of the present application.

[0057] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of the embodiments of the present application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0058] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0059] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0060] The memory 303 is used to store application code for executing the solution of the embodiment of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the above method embodiment.

[0061] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0062] A computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the method described above can be referenced to each other.

[0063] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned charging pile construction planning method based on big data analysis are implemented.

[0064] Since the embodiments of the computer-readable storage medium part and the embodiments of the method part correspond to each other, the embodiments of the computer-readable storage medium part refer to the description of the embodiments of the method part.

[0065] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0066] The above are only some implementation methods of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A charging pile construction planning method based on big data analysis, characterized in that: include: Acquire a regional grid map, regional vehicle information, and charging pile information, wherein the regional grid map is a grid map of a region where charging pile construction is to be planned, the regional vehicle information is information on changes in the number of electric vehicles and flow distribution of electric vehicles in the region where charging pile construction is to be planned during a historical period, and the charging pile information is information on locations of charging piles that have been built during the historical period and charging application data of each built charging pile during the historical period; Determining whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information; If there is at least one charging pile whose overall charging utilization rate does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, a non-charging position that does not meet the preset charging utilization power range is determined based on the charging pile information, and an association relationship between the charging application data and the vehicle data in the regional vehicle information is determined based on the non-charging position, the regional grid map and the regional vehicle information; A charging construction plan is generated according to the association relationship, and the charging construction plan is controlled to be displayed.

2. A charging pile construction planning method based on big data analysis according to claim 1, characterized in that: The determining, based on the charging pile information, whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range includes: Determine the charging power data of charging piles located at different locations within the historical period according to the charging pile information; Determine the charging application time of the charging pile based on the charging power data, calculate the ratio of the charging application time to the unit time, and percentile the calculated time ratio to obtain the charging utilization rate of each charging pile per unit time; Accumulate and calculate the charging utilization rate corresponding to each unit time in the historical cycle time period, use the accumulated calculation result as the numerator, and use the total number of unit time in the historical cycle time period as the denominator to calculate, so as to obtain a first charging utilization rate of each charging pile; Determine the charging areas that different charging piles are responsible for according to the charging pile information; Divide each charging pile into regional groups according to the responsible charging area to obtain different charging field areas; Determine whether the charging utilization rate of each charging pile in the charging field area satisfies the preset charging utilization rate. If so, record the historical full load moment when this phenomenon occurs in the historical period time period, and obtain the number of vehicle stops and the vehicle stop interval at the target position of the charging field area corresponding to the historical full load moment. The number of vehicle stops is the number of electric vehicles that stop at the target position in different vehicle single movement directions, and the vehicle stop interval is the time interval between adjacent vehicles stopping at the target position. Determine a second charging utilization rate of each charging pile in the responsible charging area according to the number of vehicle stops and the vehicle stop interval; Obtaining a vehicle update time after the historical full-load time, the vehicle update time being the time when the responsible charging area changes from a full-load state to a non-full-load state, the full-load state being that each charging pile in the responsible charging area is in a working state, and the non-full-load state being that at least one charging pile in the responsible charging area is in a non-working state; Determine the next full load time at which the responsible charging area becomes fully loaded again after the vehicle change time based on the historical full load time, and calculate the change time interval between the next full load time and the vehicle change time; Determine a third charging utilization rate of each charging pile in the responsible charging area according to the change time interval; Integrate and calculate the first charging utilization rate, the second charging utilization rate, and the third charging utilization rate according to a preset weight coefficient to obtain an overall charging utilization rate; The overall charging utilization rate is matched with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range.

3. The charging pile construction planning method based on big data analysis according to claim 1 is characterized in that: The determining the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the non-charging position, the regional grid map and the regional vehicle information includes: Determine an abnormal charging service area according to the non-charging location and the regional grid map; Performing a regional association query on the abnormal charging service area and the regional vehicle information to obtain vehicle change information of the abnormal charging service area within the historical period; Determining abnormal charging data corresponding to the non-charging position in the charging application data; The vehicle change information and the abnormal charging data are subjected to correlation analysis to determine a correlation relationship between the charging application data and the vehicle data in the regional vehicle information.

4. A charging pile construction planning method based on big data analysis according to claim 3, characterized in that: The performing correlation analysis on the vehicle change information and the abnormal charging data to determine the correlation relationship between the charging application data and the vehicle data in the regional vehicle information includes: A vehicle coordinate system and a charging coordinate system are created respectively, and the vehicle change information is mapped to the vehicle coordinate system, and the abnormal charging data is mapped to the charging coordinate system. The X-axis of the vehicle coordinate system is the different time nodes in the historical period, and the Y-axis of the vehicle coordinate system is the vehicle data corresponding to the different time nodes in the historical period. The X-axis of the charging coordinate system is the time node equivalent to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is the charging power corresponding to the different time nodes in the historical period. Integrate the vehicle coordinate system and the charging coordinate system according to time nodes to obtain an integrated comprehensive coordinate system; Determine respectively a vehicle waveform diagram corresponding to the vehicle change information and a charging waveform diagram corresponding to the abnormal charging data according to the comprehensive coordinate system; Determine the periodic change rate of the charging waveform diagram when the vehicle waveform diagram undergoes the same rate change at different time nodes, and determine the association relationship between the charging application data and the vehicle data in the regional vehicle information according to the periodic change rate.

5. The charging pile construction planning method based on big data analysis according to claim 3 is characterized in that: The step of determining whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information further includes: If there is at least one charging pile whose overall charging utilization rate does not meet the preset charging utilization rate range and the overall charging utilization rate does not exceed the preset charging utilization rate range, then determining the overcharging position that does not meet the preset charging utilization power range based on the charging pile information; Determine an overcharging service area according to the overcharging position and the regional grid map; Performing a regional association query on the overcharging service area and the regional vehicle information to obtain target vehicle information of the overcharging service area within the historical period; Performing periodic change analysis on the target vehicle information to obtain future vehicle change information within a future preset time period; The future charging data of the overcharging location within the future preset time is determined according to the future vehicle change information and the association relationship, and it is judged whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If it still does not meet the range, a charging pile planning scheme is generated based on the future vehicle change information, the future charging data and the overcharging location.

6. A charging pile construction planning method based on big data analysis according to claim 5, characterized in that: The periodic change analysis of the target vehicle information to obtain future vehicle change information within a future preset time period includes: Determine the quantity change of the target vehicle information within the historical period time period, and determine the vehicle change trend based on the quantity change; Performing unsupervised time series data sorting on the vehicle change trend to obtain vehicle matrix data; Inputting the vehicle matrix data into a trained feature extraction model to perform vector feature extraction to obtain feature dimensions and the number of feature dimensions, and combining the obtained feature dimensions and the number of feature dimensions with the vehicle matrix data to generate future vehicle matrix data; The future vehicle matrix data is input into a preset algorithm model for data calculation to obtain future vehicle change information within a preset time period.

7. A charging pile construction planning method based on big data analysis according to claim 6, characterized in that: The vehicle matrix data is input into a trained feature extraction model to extract vector features, and feature dimensions and the number of feature dimensions are obtained, including: Determine, based on the vehicle matrix data, periodic time periods of different peak value change periods in the quantity change situation and the average number of electric vehicles corresponding to the periodic time periods; Inputting the periodic time period and the mean value of the number of electric vehicles into the feature extraction model to extract vector features, respectively, to obtain time vector features corresponding to the periodic time period and data vector features corresponding to the mean value of the number of electric vehicles; Quantitative statistics are performed on the data vector features and the time vector features to obtain feature dimensions and the number of feature dimensions.

8. A charging pile construction planning system based on big data analysis, characterized in that: include: An information acquisition module, used to acquire a regional grid map, regional vehicle information, and charging pile information, wherein the regional grid map is a grid map of a charging pile construction planning area to be carried out, the regional vehicle information is information on changes in the number of electric vehicles and changes in the flow distribution of electric vehicles in the charging pile construction planning area to be carried out during a historical period, and the charging pile information is information on the location of the charging piles that have been built during the historical period and charging application data of each built charging pile during the historical period; A utilization rate judgment module, used to judge whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information; a relationship determination module, for determining, based on the charging pile information, a non-charging position that does not meet the preset charging utilization power range when the overall charging utilization rate of at least one of the charging piles does not meet the preset charging utilization rate range and the overall charging utilization rate exceeds the preset charging utilization rate range, and determining, based on the non-charging position, the regional grid map and the regional vehicle information, an association relationship between the charging application data and the vehicle data in the regional vehicle information; A scheme generating module is used to generate a charging construction scheme according to the association relationship, and control the display of the charging construction scheme.

9. An electronic device, characterized in that: The electronic device includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a charging pile construction planning method based on big data analysis as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and execute a charging pile construction planning method based on big data analysis as described in any one of claims 1 to 7.

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