A charging pile construction planning method and system based on big data analysis

Abstract

The application relates to the technical field of big data, in particular to a charging pile construction planning method and system based on big data analysis, which comprises the following steps: acquiring a regional grid map, regional vehicle information and charging pile information; judging whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate 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, determining a position where the charging supply is insufficient based on the charging pile information, and determining the association between charging application data and vehicle data in the regional vehicle information according to the position where the charging supply is insufficient, the regional grid map and the regional vehicle information; generating a charging construction scheme according to the association, and controlling the display of the charging construction scheme. The application improves the problem that the regional charging demand is greater than the charging pile supply.

Description

TECHNICAL FIELD

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

[0002] With the rapid development of the electric vehicle industry, the construction 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 reasonably plan the layout of charging piles. The traditional charging pile planning method mainly relies on manual experience or simple data statistics, which is difficult to fully consider the mobility of electric vehicles and the changes in charging demand, resulting in unreasonable charging pile layout and the problem of charging demand greater than charging pile supply in some areas. SUMMARY

[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 a first aspect, the present application provides a charging pile construction planning method based on big data analysis, which adopts the following technical solution:

[0005] Obtain a region grid map, region vehicle information and charging pile information, the region grid map is a grid map of a region to be planned for charging pile construction, the region vehicle information is the number of electric vehicles in the region to be planned for charging pile construction and the change of electric vehicle flow distribution in a historical period of time, and the charging pile information is the location information of the constructed charging pile in the historical period of time and the charging application data of each constructed charging pile in the historical period of time;

[0006] Determine whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range based on the charging pile information;

[0007] 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 exceeds the preset charging utilization rate range, determine the position of insufficient charging based on the charging pile information that does not meet the preset charging utilization power range, and determine the association between the charging application data and the vehicle data in the region vehicle information according to the position of insufficient charging, the region grid map and the region vehicle information;

[0008] Generate a charging construction scheme according to the association, and control to display the charging construction scheme.

[0009] In a possible implementation, the determining whether the overall charging utilization of each charging pile satisfies a preset charging utilization range based on the charging pile information comprises:

[0010] determining charging power data of charging piles located at different location information in the historical period of time according to the charging pile information;

[0011] determining a charging application duration of the charging pile based on the charging power data, performing ratio calculation on the charging application duration and the unit time duration, and performing percentage calculation on the calculated duration ratio to obtain the charging utilization of each charging pile in the unit time;

[0012] performing accumulation calculation on the charging utilization corresponding to each unit time in the historical period of time, and calculating the accumulation calculation result as a numerator and the total number of unit time in the historical period of time as a denominator to obtain the first charging utilization of each charging pile;

[0013] determining the responsible charging area of different charging piles according to the charging pile information;

[0014] performing area group division on each charging pile according to the responsible charging area to obtain different charging field areas;

[0015] determining whether the charging utilization of each charging pile in the charging field area satisfies the preset charging utilization, if the charging utilization of each charging pile in the charging field area satisfies the preset charging utilization, recording a historical full load time at which the phenomenon occurs in the historical period of time, and obtaining the number of vehicle stays and the vehicle stay interval at a target position of the charging field area corresponding to the historical full load time, the number of vehicle stays being the number of electric vehicles that stay at the target position in a single action direction of different vehicles, and the vehicle stay interval being the time interval at which adjacent vehicles stay at the target position;

[0016] determining the second charging utilization of each charging pile in the responsible charging area according to the number of vehicle stays and the vehicle stay interval;

[0017] obtaining a vehicle update time after the historical full load time, the vehicle update time being a time at which 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;

[0018] determining a next full load time at which the responsible charging area changes from the full load state to the non-full load state after the vehicle change time based on the historical full load time, and calculating a change time interval between the next full load time and the vehicle change time.

[0019] determining a third charging utilization rate of each charging pile in the responsible charging area according to the change time interval;

[0020] integrating 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;

[0021] matching the overall charging utilization rate with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range.

[0022] In a possible implementation manner, the determining of 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 comprises:

[0023] determining an abnormal charging service area according to the non-charging position and the regional grid map;

[0024] performing 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 in the historical period;

[0025] determining abnormal charging data corresponding to the non-charging position in the charging application data;

[0026] performing association analysis on 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.

[0027] In a possible implementation manner, the determining of 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 comprises:

[0028] respectively creating a vehicle coordinate system and a charging coordinate system, and mapping the vehicle change information to the vehicle coordinate system and mapping the abnormal charging data to the charging coordinate system, wherein an X-axis of the vehicle coordinate system is different time nodes in the historical period, a Y-axis of the vehicle coordinate system is vehicle data corresponding to the different time nodes in the historical period, an X-axis of the charging coordinate system is time nodes corresponding to the X-axis of the vehicle coordinate system, and a Y-axis of the charging coordinate system is charging power corresponding to the different time nodes in the historical period;

[0029] integrating the vehicle coordinate system and the charging coordinate system according to the time nodes to obtain an integrated comprehensive coordinate system;

[0030] determine a vehicle waveform corresponding to the vehicle change information and a charging waveform corresponding to the abnormal charging data according to the comprehensive coordinate system respectively;

[0031] determine a periodic change frequency of the charging waveform when the vehicle waveform changes at the same frequency at different time nodes, and determine the correlation between the charging application data and the vehicle data in the regional vehicle information according to the periodic change frequency.

[0032] In a possible implementation, the method further includes:

[0033] if 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 does not exceed the preset charging utilization rate range, determining an overcharging location that does not meet the preset charging utilization rate range based on the charging pile information;

[0034] determining an overcharging service area according to the overcharging location and the regional grid map;

[0035] performing regional correlation query on the overcharging service area and the regional vehicle information to obtain target vehicle information of the overcharging service area in the historical period;

[0036] performing periodic change analysis on the target vehicle information to obtain future vehicle change information in a future preset time period;

[0037] determining future charging data of the overcharging location in the future preset time according to the future vehicle change information and the correlation, and determining whether an overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range, and if not, generating a charging pile planning scheme based on the future vehicle change information, the future charging data and the overcharging location.

[0038] In a possible implementation, the periodic change analysis on the target vehicle information to obtain future vehicle change information in a future preset time period includes:

[0039] determining a quantity change of the target vehicle information in the historical period, and determining a vehicle change trend based on the quantity change;

[0040] performing unsupervised time series data arrangement on the vehicle change trend to obtain vehicle matrix data;

[0041] The vehicle matrix data is input into the trained feature extraction model for vector feature extraction, feature dimensions and a feature dimension quantity are obtained, and the obtained feature dimensions and the feature dimension quantity are combined with the vehicle matrix data for data processing to generate future vehicle matrix data.

[0042] The future vehicle matrix data is input into a preset algorithm model for data calculation to obtain future vehicle change information in a future preset time period.

[0043] In a possible implementation, the inputting of the vehicle matrix data into the trained feature extraction model for vector feature extraction to obtain feature dimensions and a feature dimension quantity comprises:

[0044] Based on the vehicle matrix data, a period time interval of different peak change periods in the quantity change situation and an electric vehicle quantity average corresponding to the period time interval are determined;

[0045] The period time interval and the electric vehicle quantity average are respectively input into the feature extraction model for vector feature extraction to obtain a time vector feature corresponding to the period time interval and a data vector feature corresponding to the electric vehicle quantity average;

[0046] The data vector feature and the time vector feature are subjected to quantity statistics to obtain feature dimensions and a feature dimension quantity.

[0047] In a second aspect, the application provides a charging pile construction planning system based on big data analysis, which adopts the following technical scheme:

[0048] A charging pile construction planning system based on big data analysis comprises:

[0049] An information acquisition module is configured to acquire a regional grid map, regional vehicle information, and charging pile information, the regional grid map being a grid map of a region to be planned for charging pile construction, the regional vehicle information being electric vehicle quantity change information and electric vehicle flow distribution change information in the region to be planned for charging pile construction in a historical period time interval, and the charging pile information being location information of constructed charging piles in the historical period time interval and charging application data of each constructed charging pile in the historical period time interval;

[0050] A utilization rate judgment module is configured 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.

[0051] determining, by the relationship determining module, a position of insufficient charging based on the charging pile information 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, according to the position of insufficient charging, the regional grid map and the regional vehicle information, a correlation between the charging application data and vehicle data in the regional vehicle information;

[0052] generating, by the scheme generating module, a charging construction scheme according to the correlation, and controlling the charging construction scheme to be displayed.

[0053] In a possible implementation, when judging whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information, the utilization rate judging module is specifically configured to:

[0054] determining, according to the charging pile information, charging power data of the charging piles located at different position information in the historical period of time;

[0055] determining, based on the charging power data, a charging application duration of the charging piles, performing ratio calculation on the charging application duration and the unit time duration, performing percentage calculation on the calculated duration ratio, and obtaining a charging utilization rate of each charging pile in a unit of time;

[0056] performing accumulation calculation on the charging utilization rate corresponding to each unit of time in the historical period of time, and taking the accumulation calculation result as a numerator and the total number of unit of time in the historical period of time as a denominator to obtain a first charging utilization rate of each charging pile;

[0057] determining, according to the charging pile information, a responsible charging region of each charging pile;

[0058] performing regional group division on each charging pile according to the responsible charging region to obtain different charging field regions;

[0059] judging whether the charging utilization rate of each charging pile in the charging field region meets the preset charging utilization rate, and if so, recording a historical full-load time when the phenomenon occurs in the historical period of time, and obtaining a vehicle stay number and a vehicle stay interval at a target position of the charging field region corresponding to the historical full-load time, the vehicle stay number being a number of electric vehicles of different vehicles staying at the target position in a single action direction, and the vehicle stay interval being a time interval of adjacent vehicles staying at the target position;

[0060] determine a second charging utilization rate of each charging pile in the responsible charging area according to the number of times of vehicle staying and the interval of vehicle staying;

[0061] obtain a vehicle update time after the historical full-load time, the vehicle update time being a time when a full-load state changes to a non-full-load state in the responsible charging area, 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;

[0062] determine a next full-load time when the responsible charging area reenters the full-load state after the vehicle change time based on the historical full-load time, and calculate a change time interval between the next full-load time and the vehicle change time;

[0063] determine a third charging utilization rate of each charging pile in the responsible charging area according to the change time interval;

[0064] integrate and calculate the first charging utilization rate, the second charging utilization rate and the third charging utilization rate according to preset weight coefficients to obtain an overall charging utilization rate;

[0065] match the overall charging utilization rate with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range.

[0066] In another possible implementation, when determining the association relationship between the charging application data and vehicle data in the regional vehicle information according to the charging-unavailable location, the regional grid map and the regional vehicle information, the relationship determination module is specifically configured to:

[0067] determine an abnormal charging service area according to the charging-unavailable location and the regional grid map;

[0068] perform 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 in the historical period;

[0069] determine abnormal charging data corresponding to the charging-unavailable location in the charging application data;

[0070] perform association analysis on the vehicle change information and the abnormal charging data to determine the association relationship between the charging application data and vehicle data in the regional vehicle information.

[0071] In another possible implementation, when analyzing the vehicle change information and the abnormal charging data to determine the correlation between the charging application data and the vehicle data in the regional vehicle information, the relationship determining module is specifically configured to:

[0072] The vehicle coordinate system and the charging coordinate system are respectively created, 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, the Y-axis of the vehicle coordinate system is vehicle data corresponding to different time nodes in the historical period, the X-axis of the charging coordinate system is time nodes corresponding to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is charging power corresponding to different time nodes in the historical period;

[0073] The vehicle coordinate system and the charging coordinate system are integrated according to time nodes to obtain an integrated comprehensive coordinate system;

[0074] The vehicle waveform graph corresponding to the vehicle change information and the charging waveform graph corresponding to the abnormal charging data are respectively determined according to the comprehensive coordinate system;

[0075] Periodic change rates of the charging waveform graph when the vehicle waveform graph has the same wave rate change at different time nodes are 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 rates.

[0076] In another possible implementation, the system further includes a supply judgment module, a region determining module, an association query module, a vehicle prediction module, and a scheme planning module, wherein,

[0077] The supply judgment module is configured to determine, if 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 does not exceed the preset charging utilization rate range, a supply charging position that does not meet the preset charging utilization power range based on the charging pile information.

[0078] The region determining module is configured to determine a supply charging service region according to the supply charging position and the region grid map.

[0079] The association query module is configured to perform regional association query on the supply charging service region and the regional vehicle information to obtain target vehicle information of the supply charging service region in the historical period.

[0080] The vehicle prediction module is configured to perform periodic change analysis on the target vehicle information to obtain future vehicle change information in a future preset time period.

[0081] The scheme planning module is configured to determine future charging data of the power supply and charging position within the future preset time according to the future vehicle change information and the correlation, and determine whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If the overall charging utilization rate does not meet the preset charging utilization rate range, a charging pile planning scheme is generated based on the future vehicle change information, the future charging data, and the power supply and charging position.

[0082] 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, the vehicle prediction module is specifically configured to:

[0083] determine a quantity change of the target vehicle information within the historical periodic time period, and determine a vehicle change trend based on the quantity change;

[0084] perform unsupervised time series data arrangement on the vehicle change trend to obtain vehicle matrix data;

[0085] input the vehicle matrix data into a trained feature extraction model to perform vector feature extraction, obtain a feature dimension and a feature dimension quantity, and perform data combination processing on the feature dimension and the feature dimension quantity obtained and the vehicle matrix data to generate future vehicle matrix data;

[0086] input the future vehicle matrix data into a preset algorithm model to perform data calculation, and obtain future vehicle change information within a future preset time period.

[0087] In another possible implementation, when the vehicle prediction module inputs the vehicle matrix data into a trained feature extraction model to perform vector feature extraction and obtain a feature dimension and a feature dimension quantity, the vehicle prediction module is specifically configured to:

[0088] determine, based on the vehicle matrix data, a periodic time period of different peak value change periods in the quantity change and an electric vehicle quantity average corresponding to the periodic time period;

[0089] input the periodic time period and the electric vehicle quantity average into the feature extraction model respectively to perform vector feature extraction, and obtain a time vector feature corresponding to the periodic time period and a data vector feature corresponding to the electric vehicle quantity average;

[0090] perform quantity statistics on the data vector feature and the time vector feature to obtain a feature dimension and a feature dimension quantity.

[0091] In a third aspect, the present application provides an electronic device, which adopts the technical scheme as follows:

[0092] at least one processor;

[0093] a memory;

[0094] at least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the big data analysis based charging pile construction planning method according to any one of the first aspect.

[0095] In a fourth aspect, the present application provides a computer readable storage medium, which adopts the technical scheme as follows:

[0096] A computer readable storage medium, which stores a computer program, and when the computer program is executed in a computer, the computer is caused to execute the big data analysis based charging pile construction planning method according to any one of the first aspect.

[0097] In summary, the present application includes at least one of the following beneficial technical effects:

[0098] In order to timely find and solve the problem that the regional charging demand is greater than the charging pile supply, the present application acquires a regional grid map, regional vehicle information and charging pile information, the regional grid map is a grid map of a region to be planned for charging pile construction, the regional vehicle information is electric vehicle quantity change information and electric vehicle flow distribution change information in the region to be planned for charging pile construction in a historical period, and the charging pile information is location information of the constructed charging pile in the historical period and charging application data of each constructed charging pile in the historical period, then whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range is judged 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, it indicates that the current charging pile is in a situation of supply not meeting demand, i.e. the regional charging demand is greater than the charging pile supply, therefore, the position of supply not meeting demand which does not meet the preset charging utilization power range is determined based on the charging pile information, the association between the charging application data and vehicle data in the regional vehicle information is determined according to the position of supply not meeting demand, the regional grid map and the regional vehicle information, the charging construction scheme is generated according to the association, and the charging construction scheme is controlled to be displayed, and the working personnel plans and adjusts the number of charging piles to be assembled and constructed in the region according to the prompted charging construction scheme, thereby fundamentally solving the problem that the regional charging demand is greater than the charging pile supply. BRIEF DESCRIPTION OF DRAWINGS

[0099] Figure 1 A flowchart of a charging pile construction planning method based on big data analysis provided by an embodiment of the present application is shown.

[0100] Figure 2 A structural diagram of a charging pile construction planning system based on big data analysis provided by an embodiment of the present application is shown.

[0101] Figure 3 A structural diagram of an electronic device provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0102] The embodiments of the present application will be described below in detail with reference to the accompanying drawings. Figures 1-3 The present application will be described in further detail.

[0103] The embodiments of the present application are merely illustrative of the present application, and are not intended to limit the present application. Those skilled in the art can make modifications to the embodiments of the present application without creative contribution, but as long as the modifications are within the scope of the present application, they are protected by the patent law.

[0104] To make the objectives, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all of the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative contribution fall within the scope of the present application.

[0105] In addition, the term "and / or" in this paper is merely to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B can represent the following three cases: A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in this paper generally represents an "or" relationship between the associated objects unless otherwise specified.

[0106] The embodiments of the present application will be described in further detail below with reference to the accompanying drawings.

[0107] The embodiments of the present application provide a charging pile construction planning method based on big data analysis, which is executed by an electronic device. The electronic device can be an independent physical electronic device, an electronic device cluster or a distributed system composed of multiple physical electronic devices, or a cloud electronic device providing cloud computing services. The embodiments of the present application do not limit this, for example, as shown in the figure, the method comprises the following steps. Figure 1

[0108] Step S10, obtaining a regional grid map, regional vehicle information and charging pile information.

[0109] ​The region grid map is a grid map of a region to be planned for charging pile construction, the region vehicle information is information about changes in the number of electric vehicles in the region to be planned for charging pile construction and changes in the flow distribution of the electric vehicles in the region to be planned for charging pile construction in a historical period, and the charging pile information is location information of charging piles constructed in the historical period and charging application data of each charging pile constructed in the historical period.

[0110] For the embodiment of the application, the region grid map represents a graphical representation of a specific geographic region to be planned for charging pile construction, which is divided into several small grids (grids), and is used to visually display the geographical features and layout of the region. The region vehicle information refers to information about the increase and decrease of the number of electric vehicles in the region and the changes in the flow distribution of these electric vehicles in the region in a specific historical period. The charging pile information refers to the specific location information of the charging piles constructed in the historical period and the usage of each charging pile, including the number of charging times, the charging duration and other charging application data. These data provide a basis for the usage efficiency and rational distribution of existing charging facilities.

[0111] In the embodiment of the application, the power equipment uses GIS (Geographic Information System) technology to draw and obtain the region grid map, ensuring that the grid division is reasonable and can accurately reflect the regional topography. Secondly, through the vehicle monitoring equipment (such as cameras, sensors, etc.) installed in the region, the electric vehicle driving trajectory data is collected, combined with big data analysis technology, the vehicle quantity change and flow distribution information are obtained, and then through the data interface provided by the charging pile management system or the operator, the location information of the constructed charging piles and the charging application data are obtained.

[0112] In step S11, whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range is determined based on the charging pile information.

[0113] For the embodiment of the application, the charging power data of the charging piles located at different location information in the historical period is determined according to the charging pile information, the charging application duration of the charging piles is determined based on the charging power data, the charging application duration is calculated by ratio with the unit time duration, the duration ratio calculated is percentized to obtain the charging utilization rate of each charging pile in the unit time, then the charging utilization rate corresponding to each unit time in the historical period is accumulated and calculated, and the accumulation result is taken as the numerator, and the total number of unit times in the historical period is taken as the denominator to calculate the first charging utilization rate of each charging pile, wherein the first charging utilization rate represents the utilization rate of the charging pile itself in the unit time.

[0114] Then, according to the charging pile information, the responsible charging area of each charging pile is determined, and each charging pile is divided into a region group according to the responsible charging area, so as to obtain different charging field regions. It is judged whether the charging utilization rate of each charging pile in the charging field region meets the preset charging utilization rate. If so, the historical full load time when this phenomenon occurs in the historical period of time is recorded, and the vehicle stay number and vehicle stay interval at the target position of the corresponding charging field region at the historical full load time are obtained. The vehicle stay number is the number of electric vehicles that stay at the target position in a single action direction of different vehicles, and the vehicle stay interval is the time interval of adjacent vehicles staying at the target position. Then, according to the vehicle stay number and the vehicle stay interval, the second charging utilization rate of each charging pile in the responsible charging area is determined, wherein the second charging utilization rate represents the overflow of the existing charging pile in the full load state.

[0115] Then, the vehicle update time after the historical full load time is obtained, wherein the vehicle update time is the time when the responsible charging area changes from a full load state to a 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 at least one charging pile in the responsible charging area is in a non-working state. Based on the historical full load time, the next full load time when the responsible charging area reenters the full load state after the vehicle change time is determined, and the change time interval between the next full load time and the vehicle change time is calculated. According to the change time interval, the third charging utilization rate of each charging pile in the responsible charging area is determined, wherein the third charging utilization rate represents the switching rate of the working state of the charging pile.

[0116] 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, so as 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.

[0117] In the embodiments of the present application, the preset weight coefficient is set 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 reflect the urgency of the current charging pile application, so its weight is less than 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 pile is working, because the number of vehicles is much larger than the number of charging piles, so even if the utilization rate of the charging pile is 100%, it cannot represent that the charging pile in the current area can meet the charging expectation of the electric vehicle, therefore, the second charging utilization rate is used to reflect the charging expectation of the user for the charging pile, but the charging expectation is not a kind of display transaction, it needs to be virtually calculated through the overflow of the charging vehicle, so the weight of the second charging utilization rate should be lower than that of 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.

[0118] Specifically, the preset charging utilization rate range is set by the staff according to different overall charging utilization rates and corresponding actual charging conditions.

[0119] In step S12, 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 exceeds the preset charging utilization rate range, the position that does not meet the preset charging utilization power range is determined based on the charging pile information, and the association between the charging application data and the vehicle data in the regional vehicle information is determined according to the position that does not meet the charging, the regional grid map and the regional vehicle information.

[0120] For the embodiments of the present application, the preset charging utilization rate range refers to a reasonable interval of the overall charging utilization rate of a charging pile set in advance according to factors such as operation strategy or equipment performance, which is used to judge whether the charging efficiency of the charging pile meets the requirements. The position that does not meet the charging refers to the position of the charging pile that is in the state of "over supply" because the overall charging utilization rate of the charging pile exceeds the upper limit of the preset range, which cannot provide charging service for more vehicles at present. The association between the charging application data and the vehicle data refers to matching and associating the charging application data of the charging pile (such as charging time, charging capacity, etc.) with the specific information of the vehicles in the region (such as the number of vehicles, etc.) in order to carry out subsequent data analysis or operation decision.

[0121] Specifically, the abnormal charging service area is determined according to the charging position and the area grid map, the abnormal charging service area is regionally associated with the area vehicle information, vehicle change information of the abnormal charging service area in a historical period is obtained, abnormal charging data corresponding to the charging position in the charging application data is determined, and the vehicle change information and the abnormal charging data are associated and analyzed to determine the association between the charging application data and the vehicle data in the area vehicle information.

[0122] Specifically, a vehicle coordinate system and a charging coordinate system are respectively created, 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 a historical period, the Y-axis of the vehicle coordinate system is vehicle data corresponding to the different time nodes in the historical period, the X-axis of the charging coordinate system is time nodes corresponding to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is charging power corresponding to the 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, then a vehicle waveform graph corresponding to the vehicle change information and a charging waveform graph corresponding to the abnormal charging data are respectively determined according to the comprehensive coordinate system, a periodic change rate of the charging waveform graph when the vehicle waveform graph has the same wave rate change at different time nodes is determined, and the association between the charging application data and the vehicle data in the area vehicle information is determined according to the periodic change rate.

[0123] In step S13, a charging construction scheme is generated according to the association, and the charging construction scheme is displayed.

[0124] For the embodiments of the application, since the current charging pile is in short supply, charging pile construction planning is needed, and how to determine that the charging pile after construction can meet the charging demand of people and the utilization rate of the charging pile after construction is high. The application binds the vehicle condition and the charging utilization rate of the charging pile by data according to the association to determine the corresponding relationship between the number of electric vehicles and the number of charging piles, and constructs the charging pile according to the corresponding relationship to achieve the most reasonable construction requirement. For example, when there are 40 electric vehicles in the A area, the charging power of a charging pile is 40W, the charging power of b charging pile is 40W, the charging power of c charging pile is 35W, the charging power of d charging pile is 0W, and the charging power of e charging pile is 0W, which indicates that the 40 electric vehicles in the A area need to be matched with 4 charging piles. However, the 4 charging piles are not fixed, because at different time stages, the 40 electric vehicles may be matched with different numbers of application charging piles, therefore, when the charging construction scheme of the charging pile is determined, the application is displayed in the form of charging number range, for example, 40 electric vehicles are matched with 4-5 charging piles.

[0125] In the embodiment of the present application, in order to timely find and solve the problem that the regional charging demand is greater than the charging pile supply, the present application acquires the regional grid map, the regional vehicle information and the charging pile information, the regional grid map is the grid map of the region to be planned for charging pile construction, the regional vehicle information is the number change information and the flow distribution change information of electric vehicles in the region to be planned for charging pile construction in the historical period, and the charging pile information is the location information of the constructed charging pile in the historical period and the charging application data of each constructed charging pile in the historical period. Then, whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range is determined 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, it indicates that the current charging pile is in a situation of insufficient supply, i.e. the regional charging demand is greater than the charging pile supply. Therefore, the position of insufficient supply that does not meet the preset charging utilization power range is determined based on the charging pile information, the association between the charging application data and the vehicle data in the regional vehicle information is determined according to the position of insufficient supply, the regional grid map and the regional vehicle information, the charging construction scheme is generated according to the association, and the charging construction scheme is controlled to be displayed. The staff plans and adjusts the number of charging piles to be constructed in the region according to the prompted charging construction scheme, thereby fundamentally solving the problem that the regional charging demand is greater than the charging pile supply.

[0126] In one possible implementation of the embodiment of the present application, whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range is determined based on the charging pile information. Then, 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 position of excessive supply that does not meet the preset charging utilization power range is determined based on the charging pile information. The excessive supply service area is determined according to the position of excessive supply and the regional grid map. The excessive supply service area is regionally associated with the regional vehicle information to obtain the target vehicle information of the excessive supply service area in the historical period. The future vehicle change information in the future preset time period is obtained by periodically analyzing the target vehicle information. The future charging data of the position of excessive supply in the future preset time is determined according to the future vehicle change information and the association. Whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range is determined. If not, the charging pile planning scheme is generated based on the future vehicle change information, the future charging data and the position of excessive supply.

[0127] Specifically, the vehicle matrix data is input into the trained feature extraction model for vector feature extraction to obtain feature dimensions and a feature dimension quantity, including: determining, based on the vehicle matrix data, period time periods of different peak value change periods in the quantity change and an average quantity of electric vehicles corresponding to the period time periods, inputting the period time periods and the average quantity of electric vehicles into the feature extraction model for vector feature extraction to obtain time vector features corresponding to the period time periods and data vector features corresponding to the average quantity of electric vehicles, and performing quantity statistics on the data vector features and the time vector features to obtain the feature dimensions and the feature dimension quantity.

[0128] A charging pile construction planning system based on big data analysis provided in the embodiments of the present application is introduced below. The charging pile construction planning system based on big data analysis described below can be correspondingly referred to the charging pile construction planning method based on big data analysis described above. Please refer to Figure 2 , Figure 2 FIG. 1 is a structural schematic diagram of a charging pile construction planning system 20 based on big data analysis provided in the embodiments of the present application, including:

[0129] An information acquisition module 21 is configured to acquire a regional grid map, regional vehicle information, and charging pile information. The regional grid map is a grid map of a region to be planned for charging pile construction. The regional vehicle information is electric vehicle quantity change information and electric vehicle flow distribution change information in the region to be planned for charging pile construction in a historical period time. The charging pile information is location information of charging piles constructed in the historical period time and charging application data of each charging pile constructed in the historical period time.

[0130] A utilization rate judgment module 22 is configured to judge, based on the charging pile information, whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range.

[0131] A relationship determination module 23 is configured to, 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, determine, based on the charging pile information, a position where power supply does not meet power consumption that does not meet the preset charging utilization power range, and determine, according to the position where power supply does not meet power consumption, the regional grid map, and the regional vehicle information, a correlation between the charging application data and vehicle data in the regional vehicle information.

[0132] A scheme generation module 24 is configured to generate a charging construction scheme according to the correlation, and control the charging construction scheme to be displayed.

[0133] In a possible implementation manner in the embodiments of the present application, when the utilization rate judgment module 22 judges, based on the charging pile information, whether the overall charging utilization rate of each charging pile meets the preset charging utilization rate range, the utilization rate judgment module 22 is specifically configured to:

[0134] determining charging power data of the charging piles located at different location information in the historical period time segment according to the charging pile information;

[0135] determining charging application time length of the charging pile based on the charging power data, and calculating the charging application time length and the unit time length by ratio, and performing percentage on the calculated time length ratio to obtain charging utilization rate of each charging pile in unit time;

[0136] performing accumulation calculation on the charging utilization rate corresponding to each unit time in the historical period time segment, and taking the accumulation calculation result as the numerator, and taking the total number of unit time in the historical period time segment as the denominator to calculate the first charging utilization rate of each charging pile;

[0137] determining the responsible charging area of different charging piles according to the charging pile information;

[0138] dividing each charging pile into area groups according to the responsible charging area to obtain different charging field areas;

[0139] judging whether the charging utilization rate of each charging pile in the charging field area meets the preset charging utilization rate, if so, recording the historical full load time in the historical period time segment when this phenomenon occurs, and obtaining the vehicle stay number and vehicle stay interval at the target position of the charging field area corresponding to the historical full load time, the vehicle stay number is the number of electric vehicles parked at the target position in a single action direction of different vehicles, and the vehicle stay interval is the time interval of adjacent vehicles parked at the target position;

[0140] determining the second charging utilization rate of each charging pile in the responsible charging area according to the vehicle stay number and the vehicle stay interval;

[0141] obtaining the vehicle update time after the historical full load time, the vehicle update time is the time when the responsible charging area changes from full load state to non-full load state, the full load state is that each charging pile in the responsible charging area is in working state, and the non-full load state is that at least one charging pile in the responsible charging area is in non-working state;

[0142] determining the next full load time when the responsible charging area re-enters the full load state after the vehicle change time based on the historical full load time, and calculating the change time interval between the next full load time and the vehicle change time;

[0143] determining the third charging utilization rate of each charging pile in the responsible charging area according to the change time interval;

[0144] The first charging utilization rate, the second charging utilization rate and the third charging utilization rate are integrated and calculated according to preset weight coefficients to obtain an overall charging utilization rate;

[0145] 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.

[0146] In another possible implementation manner of the embodiment of the application, the relationship determining module 23, when determining the correlation 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, is specifically used for:

[0147] determining an abnormal charging service area according to the non-charging position and the regional grid map;

[0148] performing regional correlation query on the abnormal charging service area and the regional vehicle information to obtain vehicle change information of the abnormal charging service area in a historical period;

[0149] determining abnormal charging data corresponding to the non-charging position in the charging application data;

[0150] 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.

[0151] In another possible implementation manner of the embodiment of the application, the relationship determining module 23, when performing 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, is specifically used for:

[0152] creating a vehicle coordinate system and a charging coordinate system respectively, and mapping the vehicle change information to the vehicle coordinate system and mapping the abnormal charging data to the charging coordinate system, wherein an X axis of the vehicle coordinate system is different time nodes in a historical period, a Y axis of the vehicle coordinate system is vehicle data corresponding to the different time nodes in the historical period, an X axis of the charging coordinate system is time nodes corresponding to the X axis of the vehicle coordinate system, and a Y axis of the charging coordinate system is charging power corresponding to the different time nodes in the historical period;

[0153] integrating the vehicle coordinate system and the charging coordinate system according to the time nodes to obtain an integrated comprehensive coordinate system;

[0154] determining a vehicle waveform graph corresponding to the vehicle change information and a charging waveform graph corresponding to the abnormal charging data according to the comprehensive coordinate system respectively;

[0155] The periodic change rate of the charging waveform graph is determined when the vehicle waveform graph has the same wave rate change at different time nodes, 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.

[0156] In another possible implementation manner of the embodiment of the application, the system 20 further includes a supply judgment module, a region determination module, an association query module, a vehicle prediction module, and a scheme planning module, wherein,

[0157] The supply judgment module is configured to determine, based on the charging pile information, a supply and charge position that does not satisfy the preset charging utilization power range, if the overall charging utilization rate of at least one charging pile does not satisfy the preset charging utilization rate range and the overall charging utilization rate does not exceed the preset charging utilization rate range.

[0158] The region determination module is configured to determine a supply and charge service region according to the supply and charge position and the region grid graph.

[0159] The association query module is configured to perform regional association query on the supply and charge service region and the regional vehicle information, to obtain target vehicle information of the supply and charge service region in a historical period.

[0160] The vehicle prediction module is configured to perform periodic change analysis on the target vehicle information, to obtain future vehicle change information in a future preset time period.

[0161] The scheme planning module is configured to determine future charging data of the supply and charge position in a future preset time according to the future vehicle change information and the association, and to determine whether an overall charging utilization rate corresponding to the future charging data satisfies a preset charging utilization rate range, if not, to generate a charging pile planning scheme based on the future vehicle change information, the future charging data, and the supply and charge position.

[0162] In another possible implementation manner of the embodiment of the application, when the vehicle prediction module performs periodic change analysis on the target vehicle information to obtain future vehicle change information in a future preset time period, the vehicle prediction module is specifically configured to:

[0163] Determine a quantity change condition of the target vehicle information in a historical period, and determine a vehicle change trend based on the quantity change condition.

[0164] Perform unsupervised time series data arrangement on the vehicle change trend, to obtain vehicle matrix data.

[0165] Input the vehicle matrix data into a trained feature extraction model to perform vector feature extraction, to obtain a feature dimension and a feature dimension quantity, and perform data combination processing on the obtained feature dimension and feature dimension quantity and the vehicle matrix data, to generate future vehicle matrix data.

[0166] The future vehicle matrix data is input into a preset algorithm model for data calculation, and future vehicle change information in a future preset time period is obtained.

[0167] In another possible implementation manner of the embodiment, when the vehicle prediction module inputs the vehicle matrix data into the trained feature extraction model for vector feature extraction, the vehicle prediction module is specifically configured to:

[0168] determine a period time segment of different peak change periods in the quantity change situation based on the vehicle matrix data and an electric vehicle quantity average corresponding to the period time segment;

[0169] input the period time segment and the electric vehicle quantity average into the feature extraction model for vector feature extraction, to obtain a time vector feature corresponding to the period time segment and a data vector feature corresponding to the electric vehicle quantity average;

[0170] perform quantity statistics on the data vector feature and the time vector feature, to obtain the feature dimension and the feature dimension quantity.

[0171] An electronic device is provided in the embodiment, as shown in Figure 3 FIG. 1, Figure 3 FIG. 1 is a structural schematic diagram of an electronic device provided in the embodiment, Figure 3 The electronic device 300 shown in FIG. 1 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, through a bus 302. Optionally, the electronic device 300 can further 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 embodiment.

[0172] The processor 301 can be a CPU (Central Processing Unit, central processor), a general-purpose processor, a DSP (Digital Signal Processor, data signal processor), an ASIC (Application Specific Integrated Circuit, application-specific integrated circuit), an FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of the embodiment. The processor 301 can also be a combination of computing functions, such as one or more microprocessor combinations, combinations of DSP and microprocessor, etc.

[0173] The bus 302 can include a path that transmits information between the above-described components. The bus 302 can be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus 302 can be divided into an address bus, a data bus, a control bus, etc. For convenience of representation, Figure 3 Only one thick line is used to represent the bus in the middle, but it does not mean that there is only one bus or one type of bus.

[0174] The memory 303 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited thereto.

[0175] The memory 303 is used to store application program codes for implementing the embodiments of the present application, and is controlled by the processor 301 to perform. The processor 301 is used to execute the application program codes stored in the memory 303 to realize the contents shown in the foregoing method embodiments.

[0176] The electronic device includes, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (Personal Digital Assistant), a PAD (Tablet Personal Computer), a PMP (Portable Multimedia Player), a car terminal (for example, a car navigation terminal), and the like, and a fixed terminal such as a digital TV, a desktop computer, and the like. Figure 3 The electronic device shown is only an example, and should not bring any limitation to the functions and use range of the embodiments of the present application.

[0177] A computer readable storage medium provided by the embodiments of the present application is described below. The computer readable storage medium described below can be mutually referred to with the method described above.

[0178] The embodiment of the present application provides a computer readable storage medium, and the computer readable storage medium stores a computer program. The computer program is executed by a processor to implement the steps of the charging pile construction planning method based on big data analysis.

[0179] Since the embodiments of the computer readable storage medium part correspond to the embodiments of the method part, the embodiments of the computer readable storage medium part are described with reference to the description of the embodiments of the method part.

[0180] It should be understood that, although each step in the flowchart of the accompanying drawings is displayed in sequence according to the indication of the arrow, these steps are not necessarily executed in sequence according to the indication of the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences. Moreover, at least part of the steps in the flowchart of the accompanying drawings can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0181] The above is only some embodiments of the present application, and it should be pointed out that, for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A charging pile construction planning method based on big data analysis, characterized in that, The method comprises the following steps: obtaining a regional grid map, regional vehicle information and charging pile information, the regional grid map being a grid map of a region to be planned for charging pile construction, the regional vehicle information being information about the number of electric vehicles in the region to be planned for charging pile construction and information about the flow distribution of electric vehicles in the region to be planned for charging pile construction in a historical period, and the charging pile information being information about the location of a charging pile constructed in the historical period and charging application data of each charging pile constructed in 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; 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 comprises the following steps: determining charging power data of charging piles located at different location information in the historical period according to the charging pile information; determining the charging application time length of the charging pile based on the charging power data, calculating the ratio of the charging application time length to the unit time length, and performing percentage calculation on the calculated time length ratio to obtain the charging utilization rate of each charging pile in the unit time; performing accumulation calculation on the charging utilization rate corresponding to each unit time in the historical period, and taking the accumulation calculation result as the numerator and the total number of unit time in the historical period as the denominator to obtain the first charging utilization rate of each charging pile; determining the responsible charging area of different charging piles according to the charging pile information; dividing each charging pile into a regional group according to the responsible charging area to obtain different charging field areas; determining whether the charging utilization rate of each charging pile in the charging field area meets the preset charging utilization rate, and if so, recording the historical full load time in the historical period when the phenomenon occurs, and obtaining the vehicle stay number and the vehicle stay interval at the target location of the charging field area in the historical full load time, the vehicle stay number being the number of electric vehicles that stay at the target location in a single action direction of different vehicles, and the vehicle stay interval being the time interval of adjacent vehicles staying at the target location; determining the second charging utilization rate of each charging pile in the responsible charging area according to the vehicle stay number and the vehicle stay interval; obtaining a vehicle update time after the historical full load time, the vehicle update time being a 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; determining a next full load time when the responsible charging area changes from a full load state to a non-full load state after the vehicle change time based on the historical full load time, and calculating the change time interval between the next full load time and the vehicle change time; determining the third charging utilization rate of each charging pile in the responsible charging area according to the change time interval; The first charging utilization rate, the second charging utilization rate and the third charging utilization rate are integrated and calculated according to preset weight coefficients 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; 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 exceeds the preset charging utilization rate range, a position of insufficient charging power 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 vehicle data in the regional vehicle information is determined according to the position of insufficient charging power, the regional grid map and the regional vehicle information; A charging construction scheme is generated according to the association relationship, and the charging construction scheme is displayed. 2.The charging pile construction planning method based on big data analysis of claim 1, wherein, The association relationship between the charging application data and vehicle data in the regional vehicle information is determined according to the position of insufficient charging power, the regional grid map and the regional vehicle information, and includes: An abnormal charging service area is determined according to the position of insufficient charging power and the regional grid map; The abnormal charging service area is regionally associated with the regional vehicle information to obtain vehicle change information of the abnormal charging service area in the historical period; Abnormal charging data corresponding to the position of insufficient charging power in the charging application data is determined; The vehicle change information and the abnormal charging data are associated and analyzed to determine the association relationship between the charging application data and vehicle data in the regional vehicle information. 3.The charging pile construction planning method based on big data analysis of claim 2, characterized in that, The association relationship between the charging application data and vehicle data in the regional vehicle information is determined by associating and analyzing the vehicle change information and the abnormal charging data, and includes: A vehicle coordinate system and a charging coordinate system are respectively created, 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, the Y-axis of the vehicle coordinate system is vehicle data corresponding to different time nodes in the historical period, the X-axis of the charging coordinate system is time nodes corresponding to the X-axis of the vehicle coordinate system, and the Y-axis of the charging coordinate system is charging power corresponding to different time nodes in the historical period; The vehicle coordinate system and the charging coordinate system are integrated according to time nodes to obtain an integrated comprehensive coordinate system; A vehicle waveform graph corresponding to the vehicle change information and a charging waveform graph corresponding to the abnormal charging data are respectively determined according to the comprehensive coordinate system; When the vehicle waveform graph has the same wave rate change at different time nodes, a periodic change wave rate of the charging waveform graph is determined, and the association relationship between the charging application data and vehicle data in the regional vehicle information is determined according to the periodic change wave rate. 4.The charging pile construction planning method based on big data analysis of claim 2, wherein, The association relationship between the charging application data and vehicle data in the regional vehicle information is determined according to the position of insufficient charging power, the regional grid map and the regional vehicle information, and includes: If 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 does not exceed the preset charging utilization rate range, a position of overcharging that does not meet the preset charging utilization power range is determined based on the charging pile information; An overcharging service area is determined according to the position of overcharging and the area grid map; The overcharging service area is regionally associated with the area vehicle information to obtain target vehicle information of the overcharging service area in the historical period; Periodic change analysis is performed on the target vehicle information to obtain future vehicle change information in a future preset time period; Future charging data of the position of overcharging in the future preset time is determined according to the future vehicle change information and the association relationship, and it is determined whether the overall charging utilization rate corresponding to the future charging data meets the preset charging utilization rate range. If not, a charging pile planning scheme is generated based on the future vehicle change information, the future charging data and the position of overcharging.

5. The charging pile construction planning method based on big data analysis according to claim 4, characterized in that, The periodic change analysis of the target vehicle information to obtain future vehicle change information in a future preset time period includes: The number change of the target vehicle information in the historical period is determined, and a vehicle change trend is determined based on the number change; Unsupervised time series data is arranged for the vehicle change trend to obtain vehicle matrix data; The vehicle matrix data is input into a trained feature extraction model to extract vector features, obtain feature dimensions and feature dimension numbers, and the obtained feature dimensions and feature dimension numbers are combined with the vehicle matrix data for data processing 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 in a future preset time period. 6.The charging pile construction planning method based on big data analysis of claim 5, wherein, The vehicle matrix data is input into a trained feature extraction model to extract vector features, obtain feature dimensions and feature dimension numbers, including: The period time of different peak change periods in the number change and the average number of electric vehicles corresponding to the period time are determined based on the vehicle matrix data; The period time and the average number of electric vehicles are input into the feature extraction model respectively to extract vector features, to obtain time vector features corresponding to the period time and data vector features corresponding to the average number of electric vehicles; The data vector features and the time vector features are counted to obtain feature dimensions and feature dimension numbers. 7.A charging pile construction planning system based on big data analysis, characterized in that, including: The information acquisition module is configured to acquire a regional grid map, regional vehicle information, and charging pile information. The regional grid map is a grid map of a region to be planned for charging pile construction. The regional vehicle information is information about changes in the number of electric vehicles in the region to be planned for charging pile construction and information about changes in the flow distribution of electric vehicles in the region to be planned for charging pile construction in a historical period. The charging pile information is information about the locations of charging piles that have been constructed in the historical period and charging application data of each charging pile that has been constructed in the historical period. The utilization rate judgment module is configured 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. When determining whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range based on the charging pile information, the utilization rate judgment module is specifically configured to: determine charging power data of charging piles located at different location information in the historical period based on the charging pile information; determine charging application time lengths of the charging piles based on the charging power data, calculate a ratio of the charging application time lengths to a unit time length, and perform percentage calculation on the calculated time length ratio to obtain charging utilization rates of each charging pile in a unit time; perform accumulation calculation on the charging utilization rates corresponding to each unit time in the historical period, and calculate the accumulation calculation result as a numerator and the total number of unit times in the historical period as a denominator to obtain a first charging utilization rate of each charging pile; determine charging areas responsible for by different charging piles based on the charging pile information; perform regional group division on each charging pile based on the charging areas responsible for by the charging piles to obtain different charging field areas; determine whether the charging utilization rates of each charging pile in the charging field areas all meet a preset charging utilization rate, and if so, record a historical full-load time at which the phenomenon occurs in the historical period, and acquire a vehicle stay number and a vehicle stay interval at a target location of the charging field area at the historical full-load time. The vehicle stay number is the number of electric vehicles that stay at the target location in a single action direction of different vehicles. The vehicle stay interval is a time interval at which adjacent vehicles stay at the target location. determine a second charging utilization rate of each charging pile in the charging area responsible for based on the vehicle stay number and the vehicle stay interval; acquire a vehicle update time after the historical full-load time. The vehicle update time is a time at which the charging area responsible for changes from a full-load state to a non-full-load state. The full-load state is a state in which each charging pile in the charging area responsible for is in a working state. The non-full-load state is a state in which at least one charging pile in the charging area responsible for is in a non-working state. determine a next full-load time at which the charging area responsible for changes from the full-load state to the non-full-load state after the vehicle change time based on the historical full-load time, and calculate a 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 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; match the overall charging utilization rate with the charging utilization rate range to determine whether the overall charging utilization rate of each charging pile meets a preset charging utilization rate range; a relationship determination module configured to, when 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, determine a position of insufficient charging based on the charging pile information, and determine an association relationship between the charging application data and vehicle data in the regional vehicle information according to the position of insufficient charging, the regional grid map and the regional vehicle information; a scheme generation module configured to generate a charging construction scheme according to the association relationship and control the charging construction scheme to be displayed.

8. An electronic device, comprising: The electronic device includes: at least one processor; a memory; at least one application program, wherein the at least one application program is stored in the memory and is configured to be executed by the at least one processor, and the at least one application program is configured to execute the method for charging pile construction planning based on big data analysis according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, including: a computer program stored in the memory and capable of being loaded and executed by the processor to execute the method for charging pile construction planning based on big data analysis according to any one of claims 1-6.

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