Cloud Platform-based Charging Pile Operation and Management Method, System and Platform

Abstract

The present application discloses a method, system and platform for charging pile operation management based on a cloud platform, which relates to the field of charging pile management. The method includes: generating a user profile for each charging station; predicting the power consumption of the charging station at a future time node according to the power redundancy of the target area and the user profile of the charging station, and determining whether the charging station is connected to a grid energy storage device; for one of the charging stations, if the charging station is connected to a grid energy storage device, determining a distribution strategy between the target charging pile in the charging station and the grid energy storage device according to the device information; generating a predicted result of the power demand of the target charging pile at a future time node according to the user profile of each charging station, the power redundancy of the target area and the distribution strategy; and obtaining a regulation strategy for the target charging pile in the charging station at a future time node according to the predicted result of the power demand. The present application can effectively improve the user charging experience.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of charging pile management, and in particular, to a charging pile operation management method, system and platform based on a cloud platform. Background Art

[0002] In recent years, with the rapid development of the new energy industry, the construction of new energy charging piles has been increasing day by day. Although the number of charging piles is constantly growing, from the current situation, the charging experience of users has not been correspondingly improved. The utilization rate of existing charging piles is very low, resulting in a great waste of resources. For example, in some cases, the demand for charging piles is too large, and the insufficient construction of charging piles leads to the phenomenon of queuing for charging, while in some locations, the charging piles are unused for a long time, which will not only affect the charging experience of users, but also cause waste of resources.

[0003] Therefore, there is an urgent need for a charging pile operation management method, system and platform based on a cloud platform to predict the power consumption of charging piles and ensure the charging experience of users. Summary of the Invention

[0004] The embodiments of the present application provide a charging pile operation management method, system and platform based on a cloud platform for improving the charging experience of users.

[0005] To achieve the above object, the embodiments of the present application adopt the following technical solutions:

[0006] In a first aspect, a charging pile operation management method based on a cloud platform, characterized by comprising:

[0007] Obtain the power redundancy of the target area, where the target area includes at least one sub-area, and each sub-area includes at least one charging station;

[0008] Obtain the historical charging information and the site location of each charging station in the target area within a preset time period;

[0009] Combine the historical charging information and the site location to generate a user portrait of each charging station;

[0010] Predict the power consumption of the charging station at a future time node according to the power redundancy of the target area and the user portrait of the charging station, and determine whether the charging station is connected to the grid energy storage device according to the power consumption;

[0011] For one of the charging stations, if the charging station is connected to the grid energy storage device, determine the target charging pile in the charging station, obtain the device information of the target charging pile, and determine the distribution strategy between the target charging pile in the charging station and the grid energy storage device according to the device information;

[0012] Generate the power demand prediction result of the target charging pile at a future time node according to the user portrait of each charging site, the power redundancy of the target area, and the allocation strategy.

[0013] Obtain the regulation strategy of the target charging pile in the charging site at a future time node according to the power demand prediction result.

[0014] In a possible implementation manner of the first aspect, generating the user portrait of each charging site by combining the historical charging information and the site location includes:

[0015] Construct a homogeneous graph of the charging site according to the historical charging information and the site location, where the homogeneous graph includes nodes and edges.

[0016] Input the homogeneous graph into a pre-trained graph neural network model and output the node vectors of each charging site.

[0017] Generate the user portrait of each charging site through the node vectors, where the node vectors correspond one-to-one with the user portraits, and the user portrait of each charging site includes the charging type and charging habit of the vehicles in each charging site.

[0018] In a possible implementation manner of the first aspect, the nodes of the homogeneous graph are the historical charging information of each charging site, and the edges of the homogeneous graph are the distances between two charging sites.

[0019] In a possible implementation manner of the first aspect, predicting the power consumption of the charging site at a future time node according to the power redundancy of the target area and the user portrait of the charging site, and determining whether the charging site is connected to the grid energy storage device according to the power consumption includes:

[0020] Predict the power consumption of each charging site at a future time node through the user portrait of each charging site, and obtain the peak power data of each charging site according to the power consumption.

[0021] Obtain the dynamic power usage data of the target area, and determine the power regulation data of each charging site according to the peak power data of each charging site and the dynamic power usage data of the target area.

[0022] If the power regulation data of each charging site is greater than the power redundancy of the target area, determine that the charging site is connected to the grid energy storage device.

[0023] In a possible implementation of the first aspect, determining the power regulation data for each charging station according to the peak power data of each charging station and the dynamic power change data of the target area includes:

[0024] Determining the dynamic power change data of the target area within the preset time interval where the peak power data of each charging station is located as the first dynamic data;

[0025] Regarding the areas where the first dynamic data is less than the average value of the dynamic power change data of the target area as low-energy consumption areas;

[0026] Respectively obtaining the power redundancy of the low-energy consumption areas during the peak power data time period of each charging station;

[0027] Respectively regulating the power redundancy of the low-energy consumption areas into each charging station to determine the power regulation data for each charging station.

[0028] In a possible implementation of the first aspect, predicting the power consumption of each charging station at future time nodes through the user profile of each charging station includes:

[0029] Obtaining the power consumption of each charging station through the user profile of each charging station;

[0030] Inputting the power consumption of each charging station into a pre-trained model to predict the power consumption of each charging station at future time nodes.

[0031] In a possible implementation of the first aspect, determining the allocation strategy between the target charging pile in the charging station and the grid energy storage device according to the device information includes:

[0032] In response to the selection instruction of the target charging pile in the charging station, obtaining the location of the target charging pile and identifying the grid energy storage device closest to the location of the target charging pile as the first energy storage device;

[0033] Obtaining the information of the first energy storage device;

[0034] Comparing the information of the first energy storage device with the information of the target charging pile to determine the allocation strategy between the target charging pile in the charging station and the grid energy storage device.

[0035] In a possible implementation of the first aspect, the information of the first energy storage device and the information of the charging pile include power regulation data, the location information of the grid energy storage device and the charging pile, and the fault information of the grid energy storage device. Comparing the information of the first energy storage device with the information of the target charging pile to determine the allocation strategy between the target charging pile and the grid energy storage device in the charging station includes:

[0036] Obtain the dynamic power change data of the target charging pile, and determine the first regulation data of the target charging pile through the dynamic power change data of the target charging pile;

[0037] Compare the first regulation data of the target charging pile with the power redundancy of the first energy storage device. If the power redundancy is greater than the first regulation data, determine the first matching degree between the target charging pile and the first energy storage device;

[0038] Compare the geographical location information of the target charging pile with the geographical location information of the first energy storage device. If the geographical location of the target charging pile is less than a preset distance from the geographical location of the first energy storage device, determine the second matching degree between the target charging pile and the first energy storage device;

[0039] Determine whether the grid energy storage device connected to the target charging pile is faulty;

[0040] If the grid energy storage device connected to the target charging pile is not faulty, obtain the power output state of the grid energy storage device. If the power output state of the grid energy storage device is a stable output state, determine the third matching degree between the target charging pile and the first energy storage device;

[0041] When the first matching degree, the second matching degree, and the third matching degree all meet the corresponding preset standards, determine the allocation strategy between the target charging pile and the grid energy storage device in the charging station.

[0042] In a second aspect, the present application provides a charging pile operation management platform, including:

[0043] A power redundancy acquisition module that acquires the power redundancy of a target area, where the target area includes at least one sub-area, and each sub-area includes at least one charging station;

[0044] A historical charging information and site location acquisition module that acquires the historical charging information and site location of each charging station in the target area within a preset time period;

[0045] A user portrait generation module that generates a user portrait of each charging station by combining the historical charging information and the site location;

[0046] The grid energy storage device access module predicts the power consumption of the charging station at future time nodes based on the power redundancy of the target area and the user profile of the charging station, and determines whether the charging station accesses the grid energy storage device according to the power consumption.

[0047] The allocation strategy determination module, for one of the charging stations, if the charging station accesses the grid energy storage device, determines the target charging pile in the charging station, obtains the device information of the target charging pile, and determines the allocation strategy between the target charging pile in the charging station and the grid energy storage device according to the device information.

[0048] The power demand prediction result generation module generates the power demand prediction result of the target charging pile at future time nodes according to the user profile of each charging station, the power redundancy of the target area, and the allocation strategy.

[0049] The power regulation strategy module obtains the regulation strategy of the target charging pile in the charging station at future time nodes according to the power demand prediction result.

[0050] In a third aspect, the present application provides a charging pile operation management system based on a cloud platform, including:

[0051] A memory configured to store instructions; and

[0052] A processor configured to call the instructions from the memory and be able to implement the above-mentioned charging pile operation management method based on the cloud platform when executing the instructions.

[0053] Through the above technical solutions, by constructing the user profiles of each charging station in the target area, the information of each charging station in the target area can be accurately grasped, which helps to provide more accurate services for the users using the charging stations. Judging whether the target area accesses the grid energy storage device can provide electric energy for the charging piles in the target area and ensure the stable power supply of the charging piles in the areas with relatively large charging demands. In addition, through the user profiles of each charging station, the power redundancy of the target area, and the allocation strategy, the power demand prediction result of the target charging pile at future time nodes is generated, and the usage situation of the charging piles in the target area can be grasped in advance, and the electric energy can be regulated according to the current usage situation of the electric energy, which can effectively ensure the stable power supply of the target area.

[0054] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1Schematic flowchart of a charging pile operation management method provided by an embodiment of the present application;

[0056] Figure 2 Schematic structural diagram of a charging pile operation management platform provided by an embodiment of the present application;

[0057] Figure 3 Schematic structural diagram of a homogeneous graph neural network constructed by an embodiment of the present application. Detailed implementation manners

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. It should be understood that the specific implementation manners described herein are only for explaining and illustrating the embodiments of the present application, and are not used to limit the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts belong to the scope protected by the present application.

[0059] It should be noted that if there are directional indications (such as up, down, left, right, front, back,...) involved in the embodiments of the present application, such directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly.

[0060] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present application, such descriptions of "first", "second", etc. are only for descriptive purposes, and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the fact that those of ordinary skill in the art can implement them. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present application.

[0061] Figure 1 Schematically shows a schematic flowchart of a charging pile operation management method based on a cloud platform according to an embodiment of the present application. As Figure 1 shown, an embodiment of the present application provides a charging pile operation management method based on a cloud platform, and the method may include the following steps.

[0062] S110. Obtain the power redundancy of the target area, where the target area includes at least one sub-area, and each sub-area includes at least one charging station;

[0063] S120. Obtain the historical charging information and station locations of each charging station in the target area within a preset time period;

[0064] S130. Combine the historical charging information and station locations to generate user portraits of each charging station;

[0065] S140. Based on the power redundancy in the target area and the user portraits of the charging stations, predict the power consumption of the charging stations at future time nodes, and determine whether the charging stations are connected to the grid energy storage device according to the power consumption;

[0066] S150. For one of the charging stations, if the charging station is connected to the grid energy storage device, determine the target charging pile in the charging station, obtain the device information of the target charging pile, and determine the allocation strategy between the target charging pile in the charging station and the grid energy storage device according to the device information;

[0067] S160. Generate the predicted result of the power demand of the target charging pile at future time nodes according to the user portraits of each charging station, the power redundancy in the target area, and the allocation strategy;

[0068] S170. Obtain the regulation strategy of the target charging pile in the charging station at future time nodes according to the predicted result of the power demand;

[0069] First, obtain the power redundancy in the target area. In this embodiment, the target area can be the city where the users using the charging piles are located. For example, if the city where the users are located is Shanghai, determine that the target area is Shanghai, and the power redundancy is the remaining power reserve in the grid system of the target area on the basis of meeting the normal power load operation. The target area includes at least one sub-region, and each sub-region includes at least one charging station.

[0070] Second, obtain the historical charging information and station locations of each charging station in the target area within a preset time period. In this embodiment, the preset time period can be determined according to the actual situation. The historical charging information of each charging station includes the historical power consumption of the charging station, as well as the number of vehicles entering the charging station and the vehicle basic information.

[0071] By combining historical charging information and site locations, user portraits of each charging station are generated. Specifically, first, the historical information of each charging station is obtained. In this embodiment, the historical information of the charging station includes the historical power consumption of the charging station, as well as the number of vehicles entering the charging station and the vehicle basic information. Through the corresponding vehicle basic information and the historical information of each charging station, the vehicle charging data of each charging station is obtained. That is to say, the number of vehicles entering the charging station and the vehicle basic information are obtained. In this embodiment, the vehicle basic information includes the power consumed when the vehicle is fully charged and the energy consumption of the vehicle during charging. For example, the battery capacity of the vehicle is 82 kWh, and it requires 98 degrees of electricity to be fully charged. Factors such as the efficiency of the charging equipment, the aging of the battery, and the ambient temperature will affect the energy consumption of the vehicle during charging. Based on the power consumed when the vehicle is fully charged and the energy consumption of the vehicle during charging, the power consumed by the vehicle during the period from entering the charging station to leaving the charging station is determined, and the vehicle charging data of each charging station is determined within a preset time period. Then, the vehicle charging data of each charging station is input into a pre-trained neural network model to generate the charging preference labels of each charging station. The pre-trained neural network model refers to a neural network model that has been pre-trained on a large-scale data set and is often used for trend prediction and other aspects. The charging preference labels of each charging station and each site location form the user portrait of each charging station. A user portrait refers to a user information profile constructed for a specific user group through means such as data analysis, market research, and user behavior. The user portrait of the site is generated through the preferences of the charging station and the site location.

[0072] After generating the user profile, based on the power redundancy in the target area and the user profile of the charging stations, the power consumption of the charging stations at future time nodes is predicted. Specifically, first, through the user profile of each charging station, the power consumption of each charging station at future time nodes is predicted. For example, through the user profile of the station, the consumption of each station at historical time nodes can be obtained, and the power consumption at future time nodes is predicted based on the consumption of each station at historical time nodes. The power consumption at future time nodes can be obtained through a prediction model, where the prediction model can be a neural network model. Second, by obtaining the dynamic power usage data of the target area, based on the peak power data of each charging station and the dynamic power usage data of the target area, the peak power data of each charging station is determined. The power regulation data of all charging stations is compared with the power redundancy in the target area. If the power regulation data of all charging stations is greater than the power redundancy in the target area, it is determined that the charging stations are connected to the grid energy storage device. For example, after determining the power regulation data of each charging station, the power regulation data is added up to obtain the power regulation data of all charging stations. For example, the power regulation data of all charging stations is 5000 WH, and the power redundancy in the target area is 4000 WH. At this time, the power regulation data of all charging stations is greater than the power redundancy in the target area, and the power redundancy in the target area is insufficient, so the grid energy storage device needs to be connected.

[0073] When it is determined that the charging stations need to be connected to the grid energy storage device, the target charging piles in the charging stations are determined, and the device information of the target charging piles is obtained. In this embodiment, the target charging station refers to the charging station that needs to be connected to the grid energy storage device, and the target charging pile refers to the charging pile in the charging station. According to the device information, the allocation strategy between the target charging piles in the charging stations and the grid energy storage device is determined. That is, by obtaining the device information of the target charging piles, in this embodiment, the device information of the target charging piles is the location information of the target charging piles and the amount of electrical energy required by the charging piles. Through the device location information of the target charging piles in the charging station, the grid energy storage device closest to the target charging piles is determined. By judging the power reserve and the state of the device of the target charging piles and the closest grid energy storage device, the most suitable grid energy storage device for the target charging piles is determined, so as to determine the allocation strategy between the target charging piles and the grid energy storage device.

[0074] Generate the predicted result of the power demand of the target charging pile at a future time node based on the user portraits of each site, the power redundancy of the target area, and the distribution strategy. That is to say, the power consumption of each site can be obtained through the user portraits of each site, and the power consumption of each site at a future time node can be predicted based on the power consumption of each site, the power redundancy of the target area, and the distribution strategy. According to the predicted power consumption of each site at a future time node, the predicted result of the power demand of the target charging pile at a future time node can be generated.

[0075] Obtain the regulation strategy of the target charging pile in the charging site at a future time node based on the predicted result of the power demand. For example, if the power required by the charging site obtained from the predicted result of the power demand is 900 kWh, and the power from the power grid regulation to the target charging pile in the target area is 600 kWh, there is a power gap of 300 kWh. Therefore, a power regulation strategy of 300 kWh for the target charging pile in the charging site at a future time node will be generated.

[0076] Through the above technical solution, by constructing the user portraits of each charging site in the target area, the information of each charging site in the target area can be accurately grasped, which helps to provide more accurate services for the users using the charging sites. Judging whether the target area is connected to the grid energy storage device can provide power for the charging piles in the target area and ensure the stable power supply of the charging piles in the areas with relatively large charging demands. In addition, by generating the predicted result of the power demand of the target charging pile at a future time node based on the user portraits of each charging site, the power redundancy of the target area, and the distribution strategy, the usage situation of the charging piles in the target area can be grasped in advance, and the power can be regulated according to the current power usage situation, which can effectively ensure the stable power supply of the target area.

[0077] In one implementation manner of this embodiment, combine the historical charging information and the site location to generate the user portrait of each charging site, including:

[0078] S210. Construct a homogeneous graph of the charging site according to the historical charging information and the site location. The homogeneous graph includes nodes and edges;

[0079] S220. Input the homogeneous graph into a pre-trained graph neural network model and output the node vectors of each charging site;

[0080] S230. Generate the user portrait of each charging site through the node vectors. Among them, the node vectors correspond one-to-one with the user portraits, and the user portrait of each charging site includes the charging type and charging habit of the vehicles in each charging site.

[0081] Combining historical charging information and station locations, user portraits of each charging station are generated. Specifically, first, a homogeneous graph neural network of charging stations is constructed based on historical charging information and station locations. A homogeneous graph means that all nodes and edges in the graph are of the same type, with relatively unified structures and attributes, and is suitable for simple and unified graph neural network models. Each charging station is used as a node in the homogeneous graph neural network. In the homogeneous graph neural network, there are nodes corresponding to the homogeneous graph of each charging station. The nodes of the homogeneous graph of each charging station are mainly obtained from the historical charging information of each charging station. The historical charging information of each charging station includes the historical power consumption of the charging station, the number of vehicles entering the charging station, and the vehicle basic information. The homogeneous graph neural network of the station is constructed through the location information of each charging station and the historical charging information of each charging station.

[0082] The homogeneous graph is input into a pre-trained graph neural network model to output the node vectors of each charging station. Specifically, the homogeneous graph neural network of charging stations constructed according to historical charging information and station locations is input into the pre-trained graph neural network model to output the node vectors of each charging station. A graph neural network is a deep learning model specifically used to process graph-structured data. It can capture the neighborhood structure information of nodes and is widely used in various fields such as social network analysis, bioinformatics, recommendation systems, etc. The homogeneous graph neural network is input into the graph neural network model to obtain the node vectors of each charging station. In this embodiment, the node vectors of each charging station include vehicle charging types, vehicle charging habits, vehicle charging time characteristics, and vehicle charging geographical characteristics.

[0083] After obtaining the node vectors of each charging station through the graph neural network model, user portraits of each charging station are obtained based on the characteristics of the node vectors of each charging station. A user portrait refers to a user information profile constructed for a specific user group through means such as data analysis, market research, and user behavior. In this embodiment, the user portraits of each charging station include the proportion of different vehicle types at each charging station, the vehicle charging habits at each charging station such as the average charging duration and charging frequency, the time characteristics at each charging station such as peak charging time periods, the geographical characteristics at each charging station such as different charging station locations resulting in different main sources of users, and the consumption characteristics of users at each charging station such as the average charging fee payment method.

[0084] Generate user portraits for each charging station through node vectors, where the node vectors correspond one-to-one with the user portraits. The user portrait of each charging station includes the charging types and charging habits of the vehicles in each charging station. For example, through the node vectors of each station, such as any node feature in a homogeneous graph neural network, the vehicle charging habits, vehicle charging time features, and vehicle charging geographical features in the node features can be obtained, and the user portrait of this node can be obtained, thereby determining the user portrait of this charging station.

[0085] By constructing a homogeneous graph neural network and inputting the homogeneous graph neural network into a pre-trained graph neural network model, the node vectors of the charging stations are output, and the user portraits of the charging stations are obtained, so that the basic information of the charging stations can be determined more accurately, thereby making the services of the charging stations closer to the needs of users, more effectively allocating electric energy resources, and improving the market competitiveness of the charging stations.

[0086] In one implementation manner of this embodiment, the nodes of the homogeneous graph are the historical charging information of each charging station, and the edges of the homogeneous graph are the distances between two charging stations.

[0087] Construct a homogeneous graph neural network of the charging station according to the historical charging information and the station location. A homogeneous graph means that all nodes and edges in the graph are of the same type, with relatively unified structures and attributes, and is suitable for a simple and unified graph neural network model. The homogeneous graph neural network includes the nodes and edges of the homogeneous graph. A homogeneous graph means that all nodes and edges in the graph are of the same type, with relatively unified structures and attributes, and is suitable for a simple and unified graph neural network model. That is to say, in this embodiment, the nodes (historical charging information) of the homogeneous graph corresponding to the charging stations in the homogeneous graph neural network include vehicle charging types, vehicle charging habits, vehicle charging time features, and vehicle charging geographical features. Among them, the vehicle charging type is the charging form of different types of vehicles, such as fast charging or slow charging. The vehicle charging habit is the average charging duration and charging frequency of the vehicle. The time feature of each charging station, such as the peak charging time period, the geographical feature of each charging station, such as different charging station locations resulting in different main sources of users, and the consumption feature of the users of each charging station, such as the way of paying the average charging cost.

[0088] In the process of constructing the homogeneous graph neural network, each charging station is used as a node of the homogeneous graph of the homogeneous graph neural network, as Figure 3 shown Figure 3A structural schematic diagram of a homogeneous graph neural network provided by an embodiment of the present application. Each charging station is regarded as Node 1, Node 2, Node 3, etc. In this embodiment, the number of nodes depends on the time situation. The distance between each two charging stations is used as the edge of the homogeneous graph of the homogeneous graph neural network, and the distance between two charging stations can be obtained from the site positions of the two charging stations. For example, in actual situations, the distance between the target charging pile and the nearest charging pile is used as the length of the edge in the homogeneous graph neural network, and the homogeneous graph neural network is constructed based on this length.

[0089] By regarding each charging station as the node of the homogeneous graph of the homogeneous graph neural network and the distance between each two charging stations as the edge of the homogeneous graph of the homogeneous graph neural network, the relationship between each charging station can be shown more clearly, providing more accurate information for the operation and management of charging piles.

[0090] In one implementation manner of this embodiment, according to the power redundancy of the target area and the user portraits of the charging stations, the power consumption of the charging stations at future time nodes is predicted, and it is determined whether the charging stations are connected to the grid energy storage device according to the power consumption, including:

[0091] S410: Predict the power consumption of each charging station at a future time node through the user portrait of each charging station, and obtain the peak power data of each charging station according to the power consumption;

[0092] S420: Obtain the dynamic power usage data of the target area, and determine the power regulation data of each charging station according to the peak power data of each charging station and the dynamic power usage data of the target area;

[0093] S430: If the power regulation data of each charging station is greater than the power redundancy of the target area, determine that the charging station is connected to the grid energy storage device.

[0094] According to the power redundancy of the target area and the user portraits of the charging stations, predict the power consumption of the charging stations at future time nodes, and determine whether the charging stations are connected to the grid energy storage device based on the power consumption. Specifically, first, through the user portraits of each charging station, predict the power consumption of each charging station at future time nodes. Specifically, first, obtain the power consumption of each charging station through the user portraits of each charging station. A user portrait refers to a user information file constructed for a specific user group through means such as data analysis, market research, and user behavior. Using the user portrait, information about the target group can be accurately obtained. Input the obtained power consumption of each charging station into a pre-trained model. In this embodiment, the pre-trained model can be a neural network model. A neural network model is a mathematical model that simulates the structure and function of the human brain neural network and is used to process and analyze data. Input the power consumption of each charging station into the neural network model, and output the power consumption of each charging station at future time nodes. In this embodiment, the future time nodes can be determined according to the actual situation.

[0095] Secondly, obtain the dynamic power usage data of the target area, and determine the power regulation data of each charging station according to the peak power data of each charging station and the dynamic power usage data of the target area. Specifically, by obtaining the dynamic power usage data of the target area, determine the peak power data of each charging station according to the peak power data of each charging station and the dynamic power usage data of the target area. Compare the power regulation data of all charging stations with the power redundancy of the target area. If the power regulation data of all charging stations is greater than the power redundancy of the target area, determine that the charging station is connected to the grid energy storage device. For example, after determining the power regulation data of each charging station, add up the power regulation data to obtain the power regulation data of all charging stations. For example, the power regulation data of all charging stations is 5000 WH, and the power redundancy of the target area is 4000 WH. At this time, the power regulation data of all charging stations is greater than the power redundancy of the target area, and the power redundancy of the target area is insufficient, so it is necessary to connect to the grid energy storage device.

[0096] By judging whether the charging station is connected to the grid energy storage device according to the power consumption at future time nodes, the operation efficiency of the power system can be effectively improved at the system level, the reliability and stability of the power supply of the grid energy storage device can be improved, thereby effectively balancing the grid load and avoiding the shortage and waste of electric energy resources.

[0097] In one implementation manner of this embodiment, determining the power regulation data of each charging station according to the peak power data of each charging station and the dynamic power change data of the target area includes:

[0098] S510. Determine the dynamic power change data of the target area within the preset time interval where the peak power data of each charging station is located as the first dynamic data;

[0099] S520. Take the areas where the first dynamic data is less than the average value of the dynamic power change data of the target area as low - energy consumption areas;

[0100] S530. Obtain the power redundancy of the low - energy consumption areas during the peak power data time period of each charging station respectively;

[0101] S540. Regulate the power redundancy of the low - energy consumption areas into each charging station respectively, and determine the power regulation data of each charging station.

[0102] Determine the dynamic power change data of the target area within the preset time interval where the peak power data of each charging station is located as the first dynamic data. In this embodiment, the first dynamic data is for the peak power data stage of each charging station.

[0103] Determine the dynamic power change data of the target area within the preset time interval where the peak power data of each charging station is located as the first dynamic data. In this embodiment, the first dynamic data is the dynamic power change data of the target area in the peak power data stage of each charging station. By determining the dynamic power change data of the target area during the peak power data time period of each charging station.

[0104] Take the areas where the first dynamic data is less than the average value of the dynamic power change data of the target area as low - energy consumption areas. That is to say, during the peak power data time period of each charging station, judge the power consumption situation of the target area, and take the areas where the first dynamic data is less than the average value of the dynamic power change data of the target area as low - energy consumption areas. In this embodiment, the low - energy consumption areas are the areas where the first dynamic data is less than the average value of the dynamic power change data of the target area. By determining the location of the low - energy consumption areas, during the peak power consumption stage of the target charging pile, regulate the power of the low - energy consumption areas to the target charging pile that needs power supplement during the peak power consumption stage of the target charging pile.

[0105] After obtaining the locations of the low - energy consumption areas, obtain the power redundancy of the low - energy consumption areas during the peak power data time period of each charging station respectively, and regulate the power redundancy of the low - energy consumption areas into each charging station respectively, and determine the power regulation data of each charging station. Specifically, after determining the low - energy consumption areas, confirm the power redundancy of the low - energy consumption areas, and regulate the power redundancy of the low - energy consumption areas into each charging station that needs power supplement, so as to determine the power regulation data of each charging station.

[0106] By analyzing the peak power data of each charging station and the dynamic power change data of the target area, the power regulation data of each charging station can be determined, and the power consumption of each charging station can be determined, which helps to accurately supplement the power of each charging station and effectively avoid the waste of power resources.

[0107] In one implementation manner of this embodiment, the power consumption of each charging station at future time nodes is predicted through the user portrait of each charging station, including:

[0108] S610. Obtain the power consumption of each charging station through the user portrait of each charging station;

[0109] S620. Input the power consumption of each charging station into a pre-trained model, and predict the power consumption of each charging station at future time nodes.

[0110] Predicting the power consumption of each charging station at future time nodes through the user portrait of each charging station. Specifically, first, obtain the power consumption of each charging station through the user portrait of each charging station. The user portrait refers to a user information file constructed for a specific user group through means such as data analysis, market research, and user behavior. Using the user portrait, the information of the target group can be accurately obtained.

[0111] Input the obtained power consumption of each charging station into a pre-trained model. In this embodiment, the pre-trained model can be a neural network model. A neural network model is a mathematical model that simulates the structure and function of the human brain neural network and is used to process and analyze data. Input the power consumption of each charging station into the neural network model, and output the power consumption of each charging station at future time nodes. In this embodiment, the future time nodes can be determined according to the actual situation.

[0112] In this embodiment, the user portrait of each charging station includes the charging types and charging habits of the vehicles in each charging station. Specifically, the charging types include the charging powers required for different types of vehicles (such as pure electric vehicles, plug-in hybrid vehicles), and the charging habits include the charging frequencies and charging durations of each type of vehicle.

[0113] According to the charging power and charging frequency, obtain the power consumption of each charging station, including:

[0114] Cluster the charging types and charging durations of the vehicles in all charging stations to obtain the power demand set and charging duration set for each charging type;

[0115] Calculate the first average value of the power demand set and the second average value of the charging duration set;

[0116] Using the electricity consumption calculation formula, calculate the electricity consumption of different charging types at each charging station based on the first average value, the second average value, and the charging frequency.

[0117] The electricity consumption calculation formula includes:

[0118] ;

[0119] In the formula, E is the electricity consumption (unit: kWh), P is the average power demand of each charging type (unit: kW), obtained by clustering and averaging the powers of all charging types, and F is the charging frequency, representing the number of charging times within a specific time period.

[0120] Among them, the first average value represents the average charging power of each charging type (unit: kW), and the second average value represents the average charging duration of each charging type (unit: hour).

[0121] By clustering and analyzing the charging types and charging durations of vehicles, the charging habits of users can be understood more accurately, thereby providing data support for the operation and management of charging stations. At the same time, calculating the average values of power demand and charging duration can reduce the influence of individual outliers on the calculation of electricity consumption, thereby improving the calculation accuracy. The clustering method can identify the characteristics of different charging types, making the average value of each type more representative. By understanding the electricity consumption of different charging types, charging stations can allocate resources more effectively. The number or power settings of charging piles can be adjusted according to high-demand periods to meet user needs.

[0122] By obtaining the electricity consumption of each charging station at future time nodes through a pre-trained neural network model, for each charging station, the efficiency of electricity regulation can be improved, the accuracy of electricity regulation can be guaranteed, potential risks can be better identified, thereby optimizing the electricity regulation management process and improving the overall efficiency.

[0123] In one implementation manner of this embodiment, determine the allocation strategy between the target charging pile in the charging station and the grid energy storage device according to the device information, including:

[0124] S710. In response to the user's selection instruction for the target charging pile in the charging station, obtain the location of the target charging pile, and identify the grid energy storage device closest to the location of the target charging pile as the first energy storage device;

[0125] S720. Obtain the information of the first energy storage device;

[0126] S730. Compare the information of the first energy storage device with the information of the target charging pile to determine the allocation strategy between the target charging pile in the charging station and the grid energy storage device.

[0127] In response to a user's selection instruction for a target charging pile in a charging station, obtain the location of the target charging pile, and identify that the grid energy storage device closest to the location of the target charging pile is the first energy storage device. In this embodiment, the first energy storage device is a device for storing electric energy, such as a clean energy microgrid device. The charging station is a charging station that needs to be connected to a grid energy storage device, and the target charging pile is any charging pile in the charging station. That is to say, when the user selects a charging pile to charge in the target charging station, obtain the location of the target charging pile selected by the user, and identify that the grid energy storage device closest to the location of the target charging pile is the first energy storage device.

[0128] After identifying the grid energy storage device closest to the location of the target charging pile, obtain the information of the first energy storage device. In this embodiment, the information of the first energy storage device is the electric energy storage capacity and the device status of the first energy storage device. Compare the information of the first energy storage device with the information of the target charging pile to determine the allocation strategy between the target charging pile and the grid energy storage device in the charging station. Specifically, compare the required power of the target charging pile with the stored power of the first energy storage device to determine whether the stored power of the first energy storage device is greater than the required power of the target charging pile. When the stored power of the first energy storage device is greater than the required power of the target charging pile, determine the first matching degree between the target charging pile and the first energy storage device. Secondly, compare the geographical location information of the target charging pile with the geographical location information of the first energy storage device. If the geographical location of the target charging pile is less than a preset distance from the geographical location of the first energy storage device, determine the second matching degree between the target charging pile and the first energy storage device. Finally, by determining whether the grid energy storage device connected to the target charging pile is faulty, when the grid energy storage device connected to the target charging pile is in a fault-free state, determine the third matching degree between the target charging pile and the first energy storage device. When the first matching degree, the second matching degree, and the third matching degree all meet the corresponding preset standards, determine the allocation strategy between the target charging pile and the grid energy storage device in the charging station.

[0129] By determining the allocation strategy between the target charging pile and the grid energy storage device in the charging station, the target charging pile in the charging station can be powered in a timely manner, ensuring the stable operation of the power supply of the target charging station.

[0130] In one implementation manner of this embodiment, the information of the first energy storage device and the information of the charging pile include power regulation data, the location information of the grid energy storage device and the charging pile, and the fault information of the grid energy storage device. Comparing the information of the first energy storage device with the information of the target charging pile to determine the allocation strategy between the target charging pile and the grid energy storage device in the charging station includes:

[0131] S810. Obtain the dynamic power change data of the target charging pile, and determine the first regulation data of the target charging pile based on the dynamic power change data of the target charging pile;

[0132] S820. Compare the first regulation data of the target charging pile with the power redundancy of the first energy storage device. If the power redundancy is greater than the first regulation data, determine the first matching degree between the target charging pile and the first energy storage device;

[0133] S830. Compare the geographical location information of the target charging pile with the geographical location information of the first energy storage device. If the geographical location of the target charging pile and the geographical location of the first energy storage device are less than the preset distance, determine the second matching degree between the target charging pile and the first energy storage device;

[0134] S840. Determine whether the grid energy storage device connected to the target charging pile is faulty;

[0135] S850. If the grid energy storage device connected to the target charging pile is not faulty, obtain the power output state of the grid energy storage device. If the power output state of the grid energy storage device is a stable output state, determine the third matching degree between the target charging pile and the first energy storage device;

[0136] S860. If the first matching degree, the second matching degree, and the third matching degree all meet the corresponding preset standards, determine the allocation strategy between the target charging pile and the grid energy storage device in the charging station.

[0137] Obtain the dynamic power change data of the target charging pile, and determine the first regulation data of the target charging pile based on the dynamic power change data of the target charging pile. Specifically, through the dynamic power change data of the target charging pile, it is possible to determine the peak stage of power consumption and the trough stage of power consumption during the power change process of the target charging pile. When it is in the peak stage of power consumption, the power consumption of the target charging pile is greater than the regulated power energy of the target charging pile in the target area for the target site. At this time, determine the power energy required by the target charging pile, and use the power energy required by the target charging pile as the first regulation data of the target charging pile.

[0138] Compare the first regulation data of the target charging pile with the power redundancy of the first energy storage device. If the power redundancy is greater than the first regulation data, determine the first matching degree between the target charging pile and the first energy storage device. In this embodiment, the first regulation data refers to the amount of electrical energy required by the target charging pile when the power consumption of the target charging pile is greater than the regulated electrical energy of the target charging pile at the target site in the target area. For example, if the regulated electrical energy in the target area for the target site is 400 kWh and the regulated electrical energy is less than the electrical energy consumed by the target site, the electrical energy of the target site is insufficient at this time, and the amount of electrical energy required by the target charging pile. When the target charging pile determines the first energy storage device with the shortest distance, compare the electrical energy required by the target charging pile with the power redundancy of the first energy storage device. If the power redundancy is greater than the first regulation data, it means that the electrical energy of the first energy storage device can supply power to the target charging pile at this time, and determine the first regulation data of the target charging pile.

[0139] Compare the geographical location information of the target charging pile with the geographical location information of the first energy storage device. If the geographical location of the target charging pile and the geographical location of the first energy storage device are less than the preset distance, determine the second matching degree between the target charging pile and the first energy storage device. Specifically, first, obtain the geographical location information of the target charging pile, search at the location where the target charging pile is located, determine the first energy storage device with the shortest distance from the target charging pile, and determine the second matching degree between the target charging pile and the first energy storage device. For example, when searching at the location where the target charging pile is located, five energy storage devices are respectively searched, and the distances between the five energy storage devices and the target charging pile are 400 meters, 600 meters, 200 meters, 500 meters, and 300 meters. The grid energy storage device with a distance of 200 meters from the target charging pile is used as the first energy storage device.

[0140] After determining the second matching degree between the target charging pile and the first energy storage device, determine whether the grid energy storage device connected to the target charging pile is faulty, and determine the fault information of the target charging pile. When the fault information of the first energy storage device connected to the target charging pile is determined, it is determined that the first energy storage device has a fault and cannot supply power to the target charging pile stably. At this time, the third matching degree between the target charging pile and the first energy storage device cannot be determined. After identifying the first energy storage device and determining that there is no fault information, the first energy storage device can supply power to the target charging pile stably, and determine the third matching degree between the target charging pile and the first energy storage device. In this embodiment, the voltage fluctuation of the grid energy storage device stably input is within the range of ±5% of the rated value, indicating that the grid energy storage device can supply power to the target charging pile stably.

[0141] When the first matching degree, the second matching degree, and the third matching degree all meet the corresponding preset criteria, determine the allocation strategy between the target charging pile in the charging station and the grid energy storage device. Specifically, when the distance range between the target charging pile and the first energy storage device is the nearest range, determine the first matching degree between the target charging pile and the first energy storage device. When the first regulation data of the target charging pile is less than the power redundancy of the first energy storage device, determine the second matching degree between the target charging pile and the first energy storage device. For example, when the power redundancy of the first energy storage device closest to the target charging pile is 300 kWh, but the first regulation data of the target charging pile is 400 kWh, it indicates that the power redundancy of the first energy storage device is insufficient to supply power to the target charging pile stably. At this time, it is necessary to determine the grid energy storage device with the second closest distance to the target charging pile, and judge the first regulation data of the target charging pile and the power redundancy of the grid energy storage device with the second closest distance. When the first regulation data of the target charging pile is less than the power redundancy of the grid energy storage device with the second closest distance, determine the second matching degree between the target charging pile and the grid energy storage device with the second closest distance.

[0142] By comparing the first matching degree, the second matching degree, and the third matching degree between the target charging pile and the first energy storage device, the position and information of the first energy storage device can be obtained more accurately, ensuring that the first energy storage device can supply power to the target charging pile, making the power supply of the target charging pile stable, and improving the experience of users using the target charging pile.

[0143] Figure 2 The structure diagram of a charging pile operation management platform based on a cloud platform provided by an embodiment of the present application is shown, as Figure 2 shown, an embodiment of the present application further provides a charging pile operation management platform, including:

[0144] A power redundancy acquisition module 10, which acquires the power redundancy of the target area. The target area includes at least one sub-region, and each sub-region includes at least one charging station;

[0145] A historical charging information and station location acquisition module 20, which acquires the historical charging information and station location of each charging station in the target area within a preset time period;

[0146] A user portrait generation module 30, which generates a user portrait of each charging station by combining historical charging information and station location;

[0147] A grid energy storage device access module 40, which predicts the power consumption of the charging station at a future time node according to the power redundancy of the target area and the user portrait of the charging station, and determines whether the charging station accesses the grid energy storage device according to the power consumption;

[0148] The allocation strategy determination module 50, for one of the charging stations, if the charging station is connected to the grid energy storage device, determines the target charging pile in the charging station, obtains the device information of the target charging pile, and determines the allocation strategy between the target charging pile in the charging station and the grid energy storage device according to the device information;

[0149] The power demand prediction result generation module 60 generates the power demand prediction result of the target charging pile at a future time node according to the user profile of each charging station, the power redundancy of the target area, and the allocation strategy;

[0150] The power regulation strategy module 70 obtains the regulation strategy of the target charging pile in the charging station at a future time node according to the power demand prediction result.

[0151] The embodiment of the present application also provides an electronic device, including:

[0152] A memory configured to store instructions; and

[0153] A processor configured to call instructions from the memory and be able to implement the above-mentioned charging pile operation management method based on the cloud platform when executing the instructions.

[0154] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0155] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the specified functions in one Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or multiple blocks.

[0156] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction device that implements the functions specified in one process Figure 1 or more processes and / or blocks Figure 1 or more blocks specified in the block diagram.

[0157] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 or more processes and / or blocks Figure 1 or more blocks specified in the block diagram.

[0158] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0159] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.

[0160] Computer-readable media includes both permanent and non-permanent, removable and non-removable media and can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0161] It should also be noted that the term "comprise", "include" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, commodity or device comprising the element.

[0162] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A charging pile operation management method based on a cloud platform, characterized in that: include: Acquiring a power redundancy amount of a target area, wherein the target area includes at least one sub-area, and each sub-area includes at least one charging station; Obtaining historical charging information and station location of each charging station in the target area within a preset time period; Combining the historical charging information and the station location, generating a user profile for each charging station; Predicting the power consumption of each charging station at a future time node through the user portrait of each charging station, and obtaining the power data peak of each charging station according to the power consumption; Acquire the dynamic data of power usage in the target area, and determine the power regulation data of each charging station according to the peak value of the power data of each charging station and the dynamic data of power usage in the target area; If the power regulation data of each of the charging sites is greater than the power redundancy of the target area, determining that the charging site is connected to the grid energy storage device; For one of the charging sites, if the charging site is connected to a grid energy storage device, determine a target charging pile in the charging site, and obtain device information of the target charging pile; In response to a user's selection instruction for a target charging pile in the charging station, obtaining a location of the target charging pile, and identifying a grid energy storage device that is closest to the location of the target charging pile as a first energy storage device; Acquiring information of the first energy storage device; Comparing the information of the first energy storage device with the equipment information of the target charging pile, and determining an allocation strategy between the target charging pile in the charging station and the grid energy storage device; Generate a power demand forecast result of the target charging pile at a future time node according to the user profile of each charging station, the power redundancy of the target area and the allocation strategy; According to the power demand forecast result, a control strategy for the target charging pile in the charging station at a future time node is obtained.

2. The method according to claim 1, characterized in that The generating a user profile of each charging station by combining the historical charging information and the station location includes: Constructing a homogeneous graph of the charging stations according to the historical charging information and the station locations, wherein the homogeneous graph includes nodes and edges; Inputting the homogeneous graph into a pre-trained graph neural network model, and outputting a node vector of each charging station; A user portrait of each charging site is generated through the node vector, wherein the node vector corresponds to the user portrait one-to-one, and the user portrait of each charging site includes the charging type and charging habits of the vehicles in each charging site.

3. The method according to claim 2, characterized in that The nodes of the homogeneous graph are the historical charging information of each charging station, and the edges of the homogeneous graph are the distance between two charging stations.

4. The method according to claim 1, characterized in that: The step of determining the power regulation data of each charging site according to the power data peak value of each charging site and the power change dynamic data of the target area includes: Determining first dynamic data from the power change dynamic data of the target area, wherein the first dynamic data is the power change dynamic data within a preset time interval where the power data peak of each charging station is located; The area where the first dynamic data is smaller than the average of the dynamic data of electric energy change in the target area is regarded as a low-energy consumption area; Respectively obtaining the electric energy redundancy of the low-energy consumption area in the electric energy data peak time period of each charging station; The redundant power in the low-power consumption area is respectively regulated to each of the charging sites, and the power regulation data of each of the charging sites is determined.

5. The method according to claim 1, characterized in that The predicting of the power consumption of each charging station at a future time node based on the user profile of each charging station includes: Obtaining the power consumption of each charging station through the user portrait of each charging station; The power consumption of each charging station is input into the pre-trained model to predict the power consumption of each charging station at a future time node.

6. The method according to claim 1, characterized in that The information of the first energy storage device and the information of the charging pile include power regulation data, location information of the power grid energy storage device and the charging pile, and fault information of the power grid energy storage device. The information of the first energy storage device is compared with the information of the target charging pile to determine the allocation strategy between the target charging pile in the charging station and the power grid energy storage device, including: Acquire dynamic power change data of the target charging pile, and determine first control data of the target charging pile according to the dynamic power change data of the target charging pile; Comparing the first control data of the target charging pile with the electric energy redundancy of the first energy storage device, and if the electric energy redundancy is greater than the first control data, determining a first matching degree between the target charging pile and the first energy storage device; Comparing the geographical location information of the target charging pile with the geographical location information of the first energy storage device, and determining a second matching degree between the target charging pile and the first energy storage device if the geographical location of the target charging pile and the geographical location of the first energy storage device are less than a preset distance; Determining whether the grid energy storage device connected to the target charging pile is faulty; If the grid energy storage device connected to the target charging pile is not faulty, obtaining the power output state of the grid energy storage device, and if the power output state of the grid energy storage device is a stable output state, determining a third matching degree between the target charging pile and the first energy storage device; If the first matching degree, the second matching degree and the third matching degree all meet the corresponding preset standards, an allocation strategy between the target charging pile in the charging site and the grid energy storage device is determined.

7. A charging pile operation management platform, characterized in that: include: An electric energy redundancy acquisition module is used to acquire electric energy redundancy in a target area, wherein the target area includes at least one sub-area, and each sub-area includes at least one charging station; A historical charging information and station location acquisition module, which acquires the historical charging information and station location of each charging station in the target area within a preset time period; A user portrait generation module, combining the historical charging information and the station location to generate a user portrait for each charging station; A power data peak determination module predicts the power consumption of each charging station at a future time node through a user portrait of each charging station, and obtains the power data peak of each charging station according to the power consumption; Determine an electric energy regulation data module, obtain the dynamic data of electric energy changes in the target area, and determine the electric energy regulation data of each charging station according to the peak value of the electric energy data of each charging station and the dynamic data of electric energy changes in the target area; Determine a module for connecting to a power grid energy storage device, and if the power regulation data of each charging station is greater than the power redundancy of the target area, determine that the charging station is connected to the power grid energy storage device; A device information acquisition module, for one of the charging sites, if the charging site is connected to a grid energy storage device, determines a target charging pile in the charging site and acquires device information of the target charging pile; Determine a first energy storage device module, in response to a user's selection instruction for a target charging pile in the charging station, obtain the location of the target charging pile, and identify the grid energy storage device closest to the location of the target charging pile as the first energy storage device; Obtaining a first energy storage device information module to obtain information of the first energy storage device; Determine an allocation strategy module, compare the information of the first energy storage device with the equipment information of the target charging pile, and determine an allocation strategy between the target charging pile in the charging station and the grid energy storage device; A power demand forecast result generating module, which generates a power demand forecast result of the target charging pile at a future time node according to the user profile of each charging station, the power redundancy of the target area and the allocation strategy; The power control strategy module obtains the control strategy of the target charging pile in the charging station at a future time node according to the power demand prediction result.

8. A charging pile operation and management system based on a cloud platform, characterized in that: include: a memory configured to store instructions; as well as A processor is configured to call the instruction from the memory and to implement the charging pile operation management method based on a cloud platform according to any one of claims 1 to 6 when executing the instruction.

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