Real-time Processing Method and System for Charging Data Applied to a Charging Information Platform

Abstract

The present invention discloses a real-time processing method and system for charging data applied to a charging information platform, which relates to the technical field of data processing, and includes the following steps: constructing a topological structure of a charging pile network; calculating the weights of the edges between child nodes and parent nodes, and calculating the weights of the edges between parent nodes; selecting a starting parent node to construct a sub-network, expanding the sub-network based on the weights of the edges between each parent node, dividing the charging pile network into several sub-networks, and determining the central parent node of each sub-network; collecting voltage and current data between parent nodes in real time, and calculating the volatility, determining the optimal path based on the topological structure of the charging pile network, during charging, collecting charging data, calculating the mean value of the charging data using a sliding window, if the mean value of the charging data exceeds the fluctuation threshold, reselecting the optimal path, so as to realize the real-time and efficient processing of the charging data, and improve the data processing ability and response speed of the charging information platform.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and specifically to a method and system for real-time processing of charging data applied to a charging information platform. Background Art

[0002] With the rapid development of the electric vehicle industry, the construction and improvement of charging infrastructure have become key factors to support the popularization of electric vehicles. As the main facility for energy replenishment of electric vehicles, the distribution, operation efficiency, and data processing ability of charging piles directly affect the charging experience of electric vehicle users and the stable operation of the power grid.

[0003] However, current technologies mainly rely on wireless or wired methods for data transmission, but do not fully consider the optimization requirements of the network structure when using power line communication technology (PLC) for data transmission. As an innovative data transmission means, the potential of power line communication technology lies in its ability to use the existing power infrastructure for data transmission. However, in practical applications, how to reasonably optimize the network structure according to the characteristics of PLC technology to maximize data transmission efficiency and stability is still a major gap in current technologies.

[0004] Secondly, when using power line communication technology for data transmission, the fluctuations of current and voltage become an influential factor that cannot be ignored. Such fluctuations may not only interfere with the normal transmission of data, resulting in data loss or errors, but also reduce the reliability and stability of data transmission. Summary of the Invention

[0005] (1) Technical Problems to be Solved

[0006] Aiming at the deficiencies of the existing technology, the present invention provides a method and system for real-time processing of charging data applied to a charging information platform. By means of constructing the topological structure of charging piles and the power network, calculating edge weights, dividing sub-networks, and real-time optimizing the data transmission path, etc., the real-time and efficient processing of charging data is realized, the data processing ability and response speed of the charging information platform are improved, and a better charging service is provided for electric vehicle users.

[0007] (2) Technical Solutions

[0008] To achieve the above object, the present invention is realized through the following technical solutions: A method for real-time processing of charging data applied to a charging information platform, including the following steps:

[0009] Collect the geographical location information of all charging piles, including longitude and latitude coordinates, the number of the charging pile, and the information of the convergence point accessing the power grid, and construct the topological structure of the charging pile network;

[0010] Collect historical charging data, calculate the weight of the edge between a child node and its parent node based on the charging amount of the child node and the physical distance between the child node and the parent node, calculate the weight of the edge between parent nodes based on the charging amount of the parent nodes and the physical distance between the parent nodes, and update the weight of the corresponding edge in the topological structure of the charging pile network;

[0011] Select a starting parent node to construct a sub-network, expand the sub-network based on the weight of the edge between each parent node, divide the charging pile network into several sub-networks, check whether there are parent nodes that have not been added to any sub-network, and determine the central parent node of each sub-network;

[0012] Collect voltage and current data between parent nodes in real time and calculate the volatility. Based on the topological structure of the charging pile network, determine the optimal path. During charging, collect charging data, calculate the mean value of the charging data using a sliding window. If the mean value of the charging data exceeds the volatility threshold, reselect the optimal path.

[0013] Furthermore, construct the topological structure of the charging pile network, specifically including:

[0014] Define each charging pile as a child node and assign a unique identifier to each child node. Define the convergence point where the charging pile is connected to the power grid as the parent node and assign a unique identifier to each parent node;

[0015] Determine the edge between the child node and the parent node according to the connection relationship between the charging pile and the power grid convergence point, determine the edge between parent nodes according to the connection relationship between the power grid convergence points, and each edge contains directional information;

[0016] Initialize an empty graph structure, which is used to store nodes and edges. Add the child nodes and parent nodes to the graph as the vertex set of the graph; according to the connection relationship, add the edges to the graph to connect the corresponding child nodes and parent nodes.

[0017] Furthermore, calculate the weight of the edge between the child node and the parent node based on the charging amount of the child node and the physical distance between the child node and the parent node: where ω s,p represents the weight of the edge between the child node s and its parent node p, E s represents the historical average power consumption of the child node s, D s,p represents the physical distance between the child node s and its parent node p, and children(p) represents the set of all child nodes of the parent node p;

[0018] Furthermore, calculate the weight of the edge between parent nodes based on the charging amount of the parent nodes and the physical distance between the parent nodes: where ω p1,p2Denote the weight of the edge between parent nodes p1 and p2 as E p1 and E p2 respectively denote the historical average power consumption of parent nodes p1 and p2 as D p1,p2 Denote the physical distance between parent nodes p1 and p2, and parent_pairs represents the set of all parent node pairs.

[0019] Furthermore, divide the charging pile network into several sub-networks, specifically including:

[0020] S1: Create an empty list to store the divided sub-networks, and initialize a variable to track the identifier of the current sub-network;

[0021] S2: Randomly select a parent node as the starting point of the current sub-network, and add this parent node and its directly connected child nodes and edges to the current sub-network;

[0022] S3: For each parent node in the current sub-network, check the weight of the edge between it and other parent nodes. If the weight of the edge between the current parent node and other parent nodes in the current sub-network is lower than the preset weight threshold, then add this parent node and its directly connected child nodes and edges to the current sub-network until no new parent nodes and child nodes can be added to the current sub-network;

[0023] S4: Traverse all parent nodes to check if there are parent nodes that have not been added to any sub-network. If so, repeat the steps of S2 - S3 to add this parent node and its child nodes and edges to a new sub-network.

[0024] Furthermore, determine the central parent node of each sub-network, specifically including:

[0025] For each divided sub-network, calculate the closeness centrality index of all parent nodes, and select the parent node with the highest closeness centrality index as the central parent node of this sub-network. The formula for calculating the closeness centrality index: where Cc(p) represents the closeness centrality index of node p, N represents the total number of nodes in the sub-network, and d(p,q) represents the shortest path length between node p and node q.

[0026] Furthermore, determine the optimal path based on the charging pile network topology, specifically including:

[0027] According to the voltage and current data between each pair of parent nodes, calculate the volatility of the parent node pairs. Based on the constructed charging pile network topology, generate the data transmission paths between all parent nodes. For each candidate path, calculate the stability index of this path according to the volatility of all parent node pairs on this path: where μ represents the stability index, σi It represents the volatility of the i-th parent node pair of the path, K represents the number of parent node pairs of the path, and the path with the highest stability index is selected as the optimal path.

[0028] Furthermore, reselect the optimal path, which specifically includes:

[0029] When the electric vehicle is connected to the charging pile and starts charging, the charging data during the charging process is collected in real time through the sensors built in the charging pile. The charging data is sorted according to the time stamp to construct a charging data sequence, and the charging data is transmitted to the charging information platform through the optimal path using power line communication technology.

[0030] Furthermore, set the size and step of the sliding window. Starting from the starting position of the charging data sequence, create the first sliding window, accumulate the charging data within the sliding window to obtain the accumulated sum, calculate the ratio of the accumulated sum to the window size to obtain the mean value of the charging data within the sliding window. If the mean value of the charging data within the sliding window exceeds the pre-set fluctuation threshold, re-determine the optimal path.

[0031] A real-time charging data processing system applied to the charging information platform includes:

[0032] A network topology construction module that collects the geographical location information of all charging piles, including longitude and latitude coordinates, the numbers of the charging piles, and the information of the convergence points accessing the power grid, and constructs the topological structure of the charging pile network;

[0033] A topological structure analysis module that collects historical charging data, calculates the weights of the edges between the child nodes and the parent nodes through the charging amounts of the child nodes and the physical distances between the child nodes and the parent nodes, calculates the weights of the edges between the parent nodes through the charging amounts of the parent nodes and the physical distances between the parent nodes, and updates the weights of the corresponding edges in the topological structure of the charging pile network;

[0034] A sub-network division module that selects a starting parent node to construct a sub-network, expands the sub-network based on the weights of the edges between each parent node, divides the charging pile network into several sub-networks, checks whether there are parent nodes that have not been added to any sub-network, and determines the central parent node of each sub-network;

[0035] A data processing module that collects the voltage and current data between the parent nodes in real time and calculates the volatility, determines the optimal path based on the topological structure of the charging pile network. During charging, it collects the charging data, calculates the mean value of the charging data using the sliding window. If the mean value of the charging data exceeds the fluctuation threshold, reselect the optimal path.

[0036] (III) Beneficial effects

[0037] The present invention provides a real-time charging data processing method and system applied to a charging information platform, having the following beneficial effects:

[0038] (1) By constructing the topological structure of the charging pile network, the connection relationship between the charging piles and the grid convergence points, as well as the energy flow direction, can be clearly displayed. This helps to optimize the data transmission path, reduce delays and losses during data transmission. Utilizing the characteristics of the topological structure, fast routing and forwarding of data can be achieved, improving the real-time performance and reliability of data transmission.

[0039] (2) By collecting and processing historical charging data, the weights of each edge (connection) in the charging pile network are calculated and updated. The calculation of the weights takes into account the charging amount and physical distance, which helps to identify the critical paths for data transmission in the network. By optimizing data transmission on these critical paths, the transmission efficiency of the entire network can be improved.

[0040] (3) By dividing the entire charging pile network into multiple sub-networks, the data processing volume within each sub-network is significantly reduced, which helps to reduce the complexity of data processing and improve the efficiency of data processing. At the same time, due to the limited number of nodes within the sub-network, the data transmission delay and error rate will also be reduced accordingly. The division of the sub-networks is carried out according to factors such as the charging characteristics and physical distance between the parent nodes, ensuring strong correlation between the nodes within each sub-network, which helps to reduce interference and noise during data transmission and improve the reliability of data transmission.

[0041] (4) By real-time collecting the voltage and current data between the parent nodes and calculating the volatility, changes in the grid state can be detected in a timely manner. Based on the charging pile network topological structure and the volatility of the parent nodes, the stability index of the path is calculated, thereby selecting the optimal data transmission path, optimizing the network structure of power communication, and ensuring the stability and reliability of data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 It is a schematic diagram of the steps of the real-time charging data processing method applied to the charging information platform of the present invention;

[0043] Figure 2 It is a schematic diagram of the network structure applied to the charging information platform of the present invention;

[0044] Figure 3 It is a schematic diagram of the structure of the real-time charging data processing system applied to the charging information platform of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0045] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] Please refer to Figure 1 - Figure 2 , the present invention provides a real-time charging data processing method applied to a charging information platform, including the following steps:

[0047] Step 1: Collect the geographical location information of all charging piles, including longitude and latitude coordinates, the numbers of the charging piles, and the information of the convergence points (i.e., parent node information) accessing the power grid, and construct the topological structure of the charging pile network;

[0048] The said Step 1 includes the following steps:

[0049] Step 101: Collect the geographical location information of all charging piles, including longitude and latitude coordinates, the numbers of the charging piles, and the information of the convergence points (i.e., parent node information) accessing the power grid;

[0050] Step 102: Define each charging pile as a child node, and assign a unique identifier (such as the charging pile number) to each child node. Define the convergence point of the charging pile accessing the power grid as the parent node, and also assign a unique identifier to each parent node;

[0051] Step 103: Determine the edges (connections) between the child nodes and the parent nodes according to the connection relationship between the charging piles and the power grid convergence points, and determine the edges between the parent nodes according to the connection relationship between the power grid convergence points. Each edge contains directional information (such as pointing from the child node to the parent node or from the parent node to the child node or from the parent node to the parent node) to represent the direction of energy flow;

[0052] It should be noted that the power grid convergence point refers to the centralized point where the charging pile accesses the power grid. Usually, it is the output end of a substation or a distribution substation. As the power source of multiple charging piles, it has the function of distributing electric energy to each charging pile. In the topological structure of the charging pile network, the power grid convergence point is abstracted as the parent node and is connected to the child nodes (charging piles) through edges;

[0053] The connection relationship between the child node (charging pile) and the parent node (power grid convergence point) represents the specific path for the charging pile to access the power grid. In the topological structure, this connection relationship is represented by a directed edge, and the direction of the edge is from the child node to the parent node, indicating that the electric energy flows from the power grid convergence point to the charging pile;

[0054] There may also be connection relationships between power grid convergence points, which are usually for achieving redundant backup of the power grid, load balancing, or optimizing power distribution. In the topological structure of the charging pile network, the connection relationships between power grid convergence points are also represented by directed edges, and the directions of these edges may change according to the actual operating conditions of the power grid, indicating the flow direction of electric energy between different power grid convergence points;

[0055] When determining the directional information of the edges, it needs to be determined according to the actual operating conditions of the power grid. For example, under normal circumstances, electric energy flows from the power grid convergence point to the charging pile, so the direction of the edge is from the parent node to the child node. However, in some special cases (such as power grid faults or maintenance), the electric energy may need to flow back from other power grid convergence points or charging piles, and at this time, the direction of the edge may change;

[0056] Step 104: Using the basic concepts of graph theory, initialize an empty graph structure that can store nodes and edges, and add the child nodes and parent nodes defined in Step 102 to the graph as the vertex set of the graph;

[0057] It should be noted that according to the connection relationships and weights (the initial weight is set to distance) between the charging piles and the power grid convergence points, an adjacency matrix or an adjacency list is constructed to represent the topological structure of the charging pile network. The adjacency matrix is a two-dimensional array, and the elements in it represent the connection relationships and weights between nodes; the adjacency list is a list of lists, and each list contains other nodes directly connected to the corresponding node and their weights.

[0058] Step 105: According to the connection relationships defined in Step 103, add edges to the graph to connect the corresponding child nodes and parent nodes. Functions in the graph theory library or framework can be used to simplify the process of adding edges;

[0059] Step 106: Verify the correctness of the graph structure to ensure that there are no isolated nodes or unconnected edges. According to the actual situation, make necessary adjustments to the graph structure, such as correcting incorrect connection relationships or adding missing edges. When all nodes and edges are correctly added to the graph and the graph structure is verified and adjusted, the construction of the topological structure of the charging pile network is completed.

[0060] When in use, combine the content of Steps 101 to 106:

[0061] By constructing the topological structure of the charging pile network, the connection relationships between the charging piles and the power grid convergence points, as well as the energy flow direction, can be clearly shown. This helps to optimize the data transmission path, reduce delays and losses during data transmission. Utilizing the characteristics of the topological structure, fast routing and forwarding of data can be achieved, improving the real-time performance and reliability of data transmission.

[0062] Step 2: Collect historical charging data, calculate the weights of the edges between child nodes and parent nodes based on the charging amounts of child nodes and the physical distances between child nodes and parent nodes, calculate the weights of the edges between parent nodes based on the charging amounts of parent nodes and the physical distances between parent nodes, and update the weights of the corresponding edges in the topological structure of the charging pile network;

[0063] The second step includes the following steps:

[0064] Step 201: Collect historical charging data, including historical charging amount data of each child node (charging pile), historical charging amount data of each parent node (grid convergence point), physical distance data between child nodes and parent nodes, and physical distance data between parent nodes;

[0065] It should be noted that the charging amount data is the charging amount of each charging pile (child node) recorded at time intervals (such as daily, weekly, monthly), and the total charging amount received or distributed by each grid convergence point (parent node). The charging amount data of the parent node needs to be calculated by summarizing the charging amount data of its child nodes; the physical distance between the child node and the parent node is usually static and can be obtained through a geographic information system (GIS) or actual measurement. Similarly, the mutual distance between parent nodes is also obtained in a similar way;

[0066] Step 202: Calculate the weight of the edge between the child node and the parent node based on the charging amount of the child node and the physical distance between the child node and the parent node: where ω s,p represents the weight of the edge between the child node s and its parent node p, E s represents the historical average power consumption of the child node s, D s,p represents the physical distance between the child node s and its parent node p, and children(p) represents the set of all child nodes of the parent node p;

[0067] Step 203: Calculate the weight of the edge between parent nodes based on the charging amount of the parent node and the physical distance between parent nodes: where ω p1,p2 represents the weight of the edge between the parent nodes p1 and p2, E p1 and E p2 respectively represent the historical average power consumption of the parent nodes p1 and p2, D p1,p2 represents the physical distance between the parent nodes p1 and p2, and parent_pairs represents the set of all parent node pairs;

[0068] Step 204: Use the weights calculated in Step 202 and Step 203 to update the weights of the corresponding edges in the graph structure constructed in Step 1.

[0069] Note that it is necessary to ensure that the weights of each edge (whether it is the edge from the child node to the parent node or the edge between the parent nodes) are correctly set. After updating the weights, it is necessary to verify the correctness of the graph structure to ensure that there are no isolated nodes or edges with incorrect weights, which can be accomplished by traversing the graph structure and checking the weight values of each edge.

[0070] When in use, combine the content of steps 201 to 204:

[0071] By collecting and processing historical charging data, calculate and update the weights of each edge (connection) in the charging pile network. The calculation of the weights takes into account the charging amount and physical distance, which helps to identify the critical paths for data transmission in the network. By optimizing the data transmission on these critical paths, the transmission efficiency of the entire network can be improved.

[0072] Step 3: Select a starting parent node to construct a sub-network, expand the sub-network based on the weights of the edges between each parent node, divide the charging pile network into several sub-networks, check whether there are parent nodes that have not been added to any sub-network, and determine the central parent node of each sub-network;

[0073] The said step 3 includes the following steps:

[0074] Step 301: Create an empty list or data structure to store the divided sub-networks, and initialize a variable to track the number or identifier of the current sub-network;

[0075] Step 302: Randomly or according to strategies such as charging amount size, geographical location distribution, etc., select a parent node as the starting point of the current sub-network. For example, select the parent node with the largest charging amount or the parent node with the most central geographical location as the starting point, and add this parent node and its directly connected child nodes and edges to the current sub-network;

[0076] Step 303: For each parent node in the current sub-network, check the weights of the edges between it and other parent nodes. If the weight of the edge between the current parent node and other parent nodes in the current sub-network is lower than the preset weight threshold (indicating that their charging characteristics are similar or the physical distance is relatively close), then add this parent node and its directly connected child nodes and edges to the current sub-network, and repeat this process until no new parent nodes and child nodes can be added to the current sub-network;

[0077] Note that the preset weight threshold plays an important role in the sub-network division process. If the threshold is set too high, it may lead to too many sub-networks and too small a scale; if the threshold is set too low, it may lead to too few sub-networks and too large a scale. Therefore, it needs to be adjusted according to specific requirements and data characteristics;

[0078] Step 304: Traverse all parent nodes to check if there are any parent nodes that have not been added to any sub-network. If so, repeat Steps 302 to 303 to add the parent node, its child nodes, and edges to the new sub-network.

[0079] It should be noted that when the division of sub-networks is completed, check the result of the sub-network division to ensure that each parent node and child node belongs to only one sub-network, and there are no isolated nodes or sub-networks. If any problems are found, the sub-network division needs to be adjusted.

[0080] Step 305: For each divided sub-network, calculate the centrality index of all parent nodes, and select the parent node with the highest centrality index as the central parent node of the sub-network. The centrality index includes degree centrality, closeness centrality, betweenness centrality, etc. For example, select the closeness centrality index as the centrality index. The calculation formula for the closeness centrality index is:

[0081] where Cc(p) represents the closeness centrality index of node p, N represents the total number of nodes in the sub-network, and d(p,q) represents the shortest path length between node p and node q.

[0082] When in use, combine the content of Steps 301 to 305:

[0083] By dividing the entire charging pile network into multiple sub-networks, the data processing volume within each sub-network is significantly reduced, which helps to reduce the complexity of data processing and improve the efficiency of data processing. At the same time, due to the limited number of nodes within the sub-network, the data transmission delay and error rate will also be reduced accordingly. The division of sub-networks is based on factors such as the charging characteristics and physical distance between parent nodes, ensuring strong correlation between nodes within each sub-network, which helps to reduce interference and noise during data transmission and improve the reliability of data transmission.

[0084] Step Four: Collect the voltage and current data between parent nodes in real time and calculate the volatility. Based on the charging pile network topology structure, determine the optimal path. During charging, collect the charging data and use a sliding window to calculate the average value of the charging data. If the average value of the charging data exceeds the volatility threshold, reselect the optimal path.

[0085] The said Step Four includes the following steps:

[0086] Step 401: Through sensors or monitoring devices installed in the power grid, collect the voltage and current data between each parent node (power grid convergence point) in real time. According to the voltage and current data between each pair of parent nodes, calculate the volatility of the parent node pair: where σ p1,p2 represents the volatility of the parent node pair, V p1,p2 (t) and Ip1,p2 (t) represents the voltage and current between the parent node p1 and the parent node p2 at time t. and respectively represent the mean values of voltage and current, and M represents the number of data points.

[0087] Step 402: Based on the charging pile network topology structure constructed in Step 1, generate the data transmission paths between all parent nodes. For each candidate path, calculate the stability index of the path according to the volatility of all parent node pairs on the path: where μ represents the stability index, and σ i represents the volatility of the i-th parent node pair of the path, and K represents the number of parent node pairs of the path. Select the path with the highest stability index as the optimal path.

[0088] Step 403: When the electric vehicle is connected to the charging pile and starts charging, use the sensors built into the charging pile to collect the charging data during the charging process in real time, including but not limited to current, voltage, charging duration, charging power, and charging amount. Sort the charging data according to the time stamp to construct a charging data sequence. The charging data sequence includes but not limited to current data sequence, voltage data sequence, and charging power data sequence. Use the power line communication technology to transmit the charging data to the charging information platform through the optimal path.

[0089] Step 404: Set the size of the sliding window, that is, the amount of data processed each time, and set the step size of the sliding window, that is, the distance the window moves each time. The window size and the step size of the sliding window are set according to the data processing requirements. For example, set the step size to 1, indicating that each time one data point is moved, or other values can also be set.

[0090] Step 405: Starting from the starting position of the charging data sequence, create the first sliding window, accumulate the charging data within the sliding window to obtain the accumulated sum, calculate the ratio of the accumulated sum to the window size to obtain the mean value of the charging data within the sliding window. If the mean value of the charging data within the sliding window exceeds the pre-set fluctuation threshold, repeat Step 401 to Step 402 to re-determine the optimal path.

[0091] It should be noted that the mean value calculation is performed for a specific charging data (such as current, voltage, etc.). In practical applications, the mean values of different charging data need to be calculated separately, and corresponding comparisons and analyses need to be carried out. The setting of the fluctuation threshold needs to be adjusted according to the actual application scenario and data characteristics to ensure that the operation of re-selecting the optimal path can be triggered in a timely manner when the data is abnormal.

[0092] When in use, combine the content of Step 401 to Step 405:

[0093] By collecting voltage and current data between parent nodes in real time and calculating the volatility, the change of the power grid state can be detected in time. Based on the charging pile network topology and the volatility of parent nodes, the stability index of the path is calculated, so as to select the optimal data transmission path, optimize the network structure of power communication, and ensure the stability and reliability of data transmission.

[0094] Please refer to Figure 3 , the present invention also provides a real-time charging data processing system applied to a charging information platform, including: a network topology construction module, a topology structure analysis module, a sub-network division module, and a data processing module; wherein,

[0095] The network topology construction module collects the geographical location information of all charging piles, including longitude and latitude coordinates, the numbers of charging piles, and the information of the convergence points accessing the power grid, and constructs the topology structure of the charging pile network;

[0096] The topology structure analysis module collects historical charging data, calculates the weights of the edges between child nodes and parent nodes through the charging amounts of child nodes and the physical distances between child nodes and parent nodes, calculates the weights of the edges between parent nodes through the charging amounts of parent nodes and the physical distances between parent nodes, and updates the weights of the corresponding edges in the topology structure of the charging pile network;

[0097] The sub-network division module selects a starting parent node to construct a sub-network, expands the sub-network based on the weights of the edges between each parent node, divides the charging pile network into several sub-networks, checks whether there are parent nodes that have not been added to any sub-network, and determines the central parent node of each sub-network;

[0098] The data processing module collects voltage and current data between parent nodes in real time and calculates the volatility, determines the optimal path based on the charging pile network topology. During charging, it collects charging data, calculates the average value of charging data using a sliding window. If the average value of charging data exceeds the fluctuation threshold, it reselects the optimal path.

[0099] In the application, several formulas involved are calculated by taking their numerical values after dimensionless, and the formula is obtained by software simulation of collecting a large amount of data to get a formula closest to the actual situation. The coefficients in the formula are set by those skilled in the art according to the actual situation.

[0100] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution.

[0101] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed over multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] As described above, the above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A real-time charging data processing method applied to a charging information platform, characterized in that: Including the following steps: Collect the geographical location information of all charging piles, including longitude and latitude coordinates, the number of the charging pile, and the information of the convergence point accessing the power grid, and construct the topological structure of the charging pile network; Collect historical charging data, calculate the weight of the edge between the child node and the parent node through the charging amount of the child node and the physical distance between the child node and the parent node, calculate the weight of the edge between the parent nodes through the charging amount of the parent node and the physical distance between the parent nodes, and update the weight of the corresponding edge in the topological structure of the charging pile network; Select a starting parent node to construct a sub-network, expand the sub-network based on the weight of the edge between each parent node, divide the charging pile network into several sub-networks, check whether there are parent nodes that have not been added to any sub-network, and determine the central parent node of each sub-network; Collect the voltage and current data between the parent nodes in real time and calculate the volatility: Among them, σ p1,p2 represents the volatility of the parent node pair, V p1,p2 (t) and I p1,p2 (t) represent the voltage and current between the parent nodes p1 and p2 at time t, and respectively represent the mean values of the voltage and current, and M represents the number of data points; Based on the constructed charging pile network topology, generate the data transmission paths between all parent nodes. For each candidate path, calculate the stability index of the path according to the volatility of all parent node pairs on the path: where μ represents the stability index, and σ i represents the volatility of the i-th parent node pair of the path, K represents the number of parent node pairs of the path, and select the path with the highest stability index as the optimal path; During charging, collect the charging data, calculate the average value of the charging data using a sliding window. If the average value of the charging data exceeds the fluctuation threshold, reselect the optimal path.

2. The real-time charging data processing method applied to a charging information platform according to claim 1, wherein: Construct the topological structure of the charging pile network, specifically including: Define each charging pile as a child node and assign a unique identifier to each child node. Define the convergence point where the charging pile accesses the power grid as the parent node and assign a unique identifier to each parent node; Determine the edge between the child node and the parent node according to the connection relationship between the charging pile and the power grid convergence point, determine the edge between the parent nodes according to the connection relationship between the power grid convergence points, and each edge contains directional information; Initialize an empty graph structure, which is used to store nodes and edges. Add the child nodes and parent nodes to the graph as the vertex set of the graph; according to the connection relationship, add the edges to the graph to connect the corresponding child nodes and parent nodes.

3. The real-time charging data processing method applied to the charging information platform according to claim 1, characterized in that: Calculate the weight of the edge between a child node and its parent node based on the charge of the child node and the physical distance between the child node and the parent node: where ω s,p represents the weight of the edge between child node s and its parent node p, E s represents the historical average power consumption of child node s, D s,p represents the physical distance between child node s and its parent node p, and children(p) represents the set of all child nodes of parent node p.

4. The real-time charging data processing method applied to the charging information platform according to claim 3, characterized in that: Calculate the weights of the edges between parent nodes based on the charging amounts of the parent nodes and the physical distances between the parent nodes: Among them, ω p1,p2 represents the weight of the edge between parent nodes p1 and p2, E p1 and E p2 respectively represent the historical average power consumption of parent nodes p1 and p2, D p1,p2 represents the physical distance between parent nodes p1 and p2, and parent_pairs represents the set of all parent node pairs.

5. The real-time charging data processing method applied to a charging information platform according to claim 1, characterized in that: Divide the charging pile network into several sub-networks, specifically including: S1: Create an empty list to store the divided sub-networks, and initialize a variable to track the identifier of the current sub-network; S2: Randomly select a parent node as the starting point of the current sub-network, and add the parent node and its directly connected child nodes and edges to the current sub-network; S3: For each parent node in the current sub-network, check the weight of the edge between it and other parent nodes. If the weight of the edge between the current parent node and other parent nodes in the current sub-network is lower than the preset weight threshold, add the parent node and its directly connected child nodes and edges to the current sub-network until no new parent nodes and child nodes can be added to the current sub-network; S4: Traverse all parent nodes, check whether there are parent nodes that have not been added to any sub-network. If so, repeat the steps of S2~S3 to add the parent node and its child nodes and edges to a new sub-network.

6. The real-time charging data processing method applied to a charging information platform according to claim 5, characterized in that: Determine the central parent node of each sub-network, specifically including: For each partitioned sub-network, calculate the closeness centrality index of all parent nodes, and select the parent node with the highest closeness centrality index as the central parent node of the sub-network. The formula for calculating the closeness centrality index is: where Cc(p) represents the closeness centrality index of node p, N represents the total number of nodes in the sub-network, and d(p,q) represents the shortest path length between node p and node q.

7. The real-time charging data processing method applied to a charging information platform according to claim 6, characterized in that: Reselect the optimal path, specifically including: When an electric vehicle is connected to a charging pile and starts charging, charging data during the charging process is collected in real time through sensors built into the charging pile. The charging data is sorted according to the timestamp to construct a charging data sequence, and the charging data is transmitted to the charging information platform through the optimal path using power line communication technology.

8. The real-time charging data processing method applied to a charging information platform according to claim 7, characterized in that: Set the size and step of the sliding window. Starting from the starting position of the charging data sequence, create the first sliding window, accumulate the charging data within the sliding window to obtain the accumulated sum, calculate the ratio of the accumulated sum to the window size to obtain the average value of the charging data within the sliding window. If the average value of the charging data within the sliding window exceeds the preset fluctuation threshold, re-determine the optimal path.

9. A real-time charging data processing system applied to a charging information platform for implementing the method according to any one of claims 1 to 8, characterized in that: Including: A network topology construction module that collects the geographical location information of all charging piles, including longitude and latitude coordinates, the numbers of the charging piles, and the information of the convergence points accessing the power grid, and constructs the topological structure of the charging pile network; A topological structure analysis module that collects historical charging data, calculates the weights of the edges between child nodes and parent nodes through the charging amounts of the child nodes and the physical distances between the child nodes and the parent nodes, calculates the weights of the edges between the parent nodes through the charging amounts of the parent nodes and the physical distances between the parent nodes, and updates the weights of the corresponding edges in the topological structure of the charging pile network; A sub-network division module that selects a starting parent node to construct a sub-network, expands the sub-network based on the weights of the edges between each parent node, divides the charging pile network into several sub-networks, checks whether there are parent nodes that have not been added to any sub-network, and determines the central parent node of each sub-network; A data processing module that collects voltage and current data between parent nodes in real time and calculates the volatility: Among them, σ p1,p2 represents the volatility of the parent node pair, V p1,p2 (t) and I p1,p2 (t) represent the voltage and current between the parent nodes p1 and p2 at time t, and represent the mean values of the voltage and current respectively, and M represents the number of data points; Based on the constructed charging pile network topology structure, generate the data transmission paths between all parent nodes. For each candidate path, calculate the stability index of the path according to the volatility of all parent node pairs on the path: where μ represents the stability index, and σ i represents the volatility of the i-th parent node pair of the path, K represents the number of parent node pairs of the path, and select the path with the highest stability index as the optimal path; during charging, collect charging data, calculate the mean value of the charging data using a sliding window, and if the mean value of the charging data exceeds the fluctuation threshold, reselect the optimal path.

Images