Charging data real-time processing method and system applied to charging information platform

Abstract

The invention discloses a charging data real-time processing method and system applied to a charging information platform, and relates to the technical field of data processing, and the method comprises the following steps: constructing a topological structure of a charging pile network; calculating the weight of the edge between the child node and the father node, and calculating the weight of the edge between the father nodes; selecting an initial father node to construct a sub-network, expanding the sub-network based on the weight of the edge between the father nodes, dividing the charging pile network into a plurality of sub-networks, and determining a central father node of each sub-network; the method comprises the following steps: collecting voltage and current data between father nodes in real time, calculating a fluctuation rate, determining an optimal path based on a charging pile network topology structure, collecting charging data during charging, calculating a charging data mean value by using a sliding window, and if the charging data mean value exceeds a fluctuation threshold value, reselecting the optimal path to realize real-time efficient processing of the charging data. And the data processing capability and the response speed of the charging information platform are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a real-time processing method and system for 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 has become a key factor in supporting the popularization of electric vehicles. As the main facility for energy replenishment of electric vehicles, the distribution, operating efficiency and data processing capabilities 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 need to optimize the network structure when using power line communication technology (PLC) for data transmission. As an innovative means of data transmission, power line communication technology has the potential to use existing power infrastructure for data transmission, but in actual applications, how to reasonably optimize the network structure based on the characteristics of PLC technology to maximize data transmission efficiency and stability is still a major gap in current technology.

[0004] Secondly, when using power line communication technology for data transmission, the fluctuation of current and voltage becomes an influencing factor that cannot be ignored. This fluctuation may not only interfere with the normal transmission of data, causing data loss or errors, but also reduce the reliability and stability of data transmission. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies in the prior art, the present invention provides a real-time processing method and system for charging data applied to a charging information platform. By constructing the topological structure of charging piles and power networks, calculating edge weights, dividing subnetworks, and optimizing data transmission paths in real time, the present invention realizes real-time and efficient processing of charging data, improves the data processing capability and response speed of the charging information platform, and provides electric vehicle users with better charging services.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: a real-time processing method of charging data applied to a charging information platform, comprising the following steps:

[0009] Collect the geographic location information of all charging piles, including latitude and longitude coordinates, charging pile numbers, and grid access point information, and build the topology of the charging pile network;

[0010] Collect historical charging data, calculate the weight of the edge between the child node and the parent node through the charge 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 charge 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;

[0011] Select a starting parent node to build a subnetwork, expand the subnetwork based on the weight of the edge between each parent node, divide the charging pile network into several subnetworks, check whether there is a parent node that has not been added to any subnetwork, and determine the central parent node of each subnetwork;

[0012] The voltage and current data between parent nodes are collected in real time, and the fluctuation rate is calculated. The optimal path is determined based on the topological structure of the charging pile network. When charging, the charging data is collected and the average charging data is calculated using a sliding window. If the average charging data exceeds the fluctuation threshold, the optimal path is reselected.

[0013] Furthermore, the topological structure of the charging pile network is constructed, 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 a parent node and assign a unique identifier to each parent node.

[0015] According to the connection relationship between the charging pile and the grid convergence point, the edge between the child node and the parent node is determined. According to the connection relationship between the grid convergence points, the edge between the parent nodes is determined. Each edge contains directional information.

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

[0017] Furthermore, the weight of the edge between the child node and the parent node is calculated by the charge of the child node and the physical distance between the child node and the parent node: Among them, 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 child node s, D s,p Represents the physical distance between a 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, the weights of the edges between parent nodes are calculated based on the charge of the parent nodes and the physical distance between the parent nodes: Among them, ω p1,p2represents the weight of the edge between parent nodes p1 and p2, E p1 and E p2 Represent the historical average power consumption of parent nodes p1 and p2, respectively, p1,p2 Represents the physical distance between parent nodes p1 and p2, and parent_pairs represents the set of all parent node pairs.

[0019] Furthermore, the charging pile network is divided into several sub-networks, 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 the 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, 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.

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

[0024] Furthermore, the central parent node of each sub-network is determined, 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 the sub-network. The calculation formula of the closeness centrality index is: Among them, Cc(p) represents the closeness centrality index of node p, N represents the total number of nodes in the subnetwork, and d(p,q) represents the shortest path length between node p and node q.

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

[0027] According to the voltage and current data between each parent node pair, the fluctuation rate of the parent node pair is calculated. Based on the constructed charging pile network topology, the data transmission path between all parent nodes is generated. For each candidate path, the stability index of the path is calculated according to the fluctuation rate of all parent node pairs on the path: Among them, μ represents the stability index, σi 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, the optimal path is reselected, which specifically includes:

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

[0030] Furthermore, the size and step size of the sliding window are set, and the first sliding window is created from the starting position of the charging data sequence. The charging data in the sliding window is accumulated to obtain the cumulative sum, and the ratio of the cumulative sum to the window size is calculated to obtain the mean of the charging data in the sliding window. If the mean of the charging data in the sliding window exceeds a preset fluctuation threshold, the optimal path is re-determined.

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

[0032] The network topology building module collects the geographical location information of all charging piles, including the longitude and latitude coordinates, the number of charging piles, and the information of the convergence point of access to the power grid, and builds the topological structure of the charging pile network;

[0033] The topology analysis module collects historical charging data, calculates the weight of the edge between the child node and the parent node through the charge amount of the child node and the physical distance between the child node and the parent node, calculates the weight of the edge between the parent nodes through the charge amount of the parent node and the physical distance between the parent nodes, and updates the weight of the corresponding edge in the topology of the charging pile network;

[0034] The subnetwork partitioning module selects a starting parent node to build a subnetwork, expands the subnetwork based on the weight of the edge between each parent node, divides the charging pile network into several subnetworks, checks whether there are parent nodes that have not been added to any subnetwork, and determines the central parent node of each subnetwork;

[0035] The data processing module collects the voltage and current data between parent nodes in real time, calculates the fluctuation rate, and determines the optimal path based on the topological structure of the charging pile network. When charging, it collects charging data and uses a sliding window to calculate the average charging data. If the average charging data exceeds the fluctuation threshold, the optimal path is reselected.

[0036] (III) Beneficial effects

[0037] The present invention provides a real-time processing method and system for charging data applied to a charging information platform, which has 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 aggregation point, as well as the direction of energy flow, can be clearly displayed. This helps to optimize the data transmission path and reduce data delay and loss during transmission. By utilizing the characteristics of the topological structure, data can be quickly routed and forwarded, improving the real-time 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 weight calculation takes into account the charge amount and physical distance, which helps to identify the key paths for transmitting data in the network. By optimizing data transmission on these key 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 amount of data processing in 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 in the sub-network, the delay and error rate of data transmission 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 that there is a strong correlation between the nodes in each sub-network, which helps to reduce interference and noise in the data transmission process and improve the reliability of data transmission.

[0041] (4) By collecting voltage and current data between parent nodes in real time and calculating the fluctuation rate, changes in the power grid status can be discovered in a timely manner. Based on the charging pile network topology and the fluctuation rate of the parent nodes, the stability index of the path is calculated to select the optimal data transmission path, thereby 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 method for real-time processing of charging data applied to a charging information platform of the present invention;

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

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

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] See also Figure 1-Figure 2 The present invention provides a real-time processing method for charging data applied to a charging information platform, comprising the following steps:

[0047] Step 1: Collect the geographic location information of all charging piles, including latitude and longitude coordinates, charging pile numbers, and grid access point information, and build the topological structure of the charging pile network;

[0048] The step 1 comprises the following steps:

[0049] Step 101: Collecting geographic location information of all charging piles, including latitude and longitude coordinates, charging pile numbers, and grid access point information (i.e., parent node information);

[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 where the charging pile is connected to the power grid as a parent node, and also assign a unique identifier to each parent node;

[0051] Step 103: According to the connection relationship between the charging pile and the grid aggregation point, determine the edge (connection) between the child node and the parent node, and according to the connection relationship between the grid aggregation points, determine the edge between the parent nodes, each edge contains directional information (such as from the child node to the parent node or the parent node to the child node or the parent node to the parent node) to indicate the direction of energy flow;

[0052] It should be noted that the grid convergence point refers to the centralized point where charging piles are connected to the grid, usually the output end of a substation or distribution station. 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 grid convergence point is abstracted as a parent node, which is connected to the child node (charging pile) through an edge.

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

[0054] There may also be connections between grid aggregation points, which is usually to achieve redundant backup of the grid, load balancing or optimize power distribution. In the topological structure of the charging pile network, the connection relationship between grid aggregation points is also represented by directed edges. The direction of these edges may change according to the actual operation of the grid, indicating the flow direction of power between different grid aggregation points.

[0055] When determining the directionality information of the edge, it is necessary to determine it based on the actual operation of the power grid. For example, under normal circumstances, electric energy flows from the power grid aggregation point to the charging pile, so the direction of the edge points from the parent node to the child node. However, in some special cases (such as power grid failure or maintenance), electric energy may need to flow back from other power grid aggregation points or charging piles, and the direction of the edge may change at this time;

[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 a vertex set of the graph;

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

[0058] Step 105: According to the connection relationship defined in step 103, add edges to the graph to connect the corresponding child nodes and parent nodes. The function 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 has been verified and adjusted, the topological structure of the charging pile network is completed.

[0060] When using, combine the contents of step 101 to step 106:

[0061] By constructing the topological structure of the charging pile network, the connection relationship between the charging piles and the grid aggregation point, as well as the direction of energy flow, can be clearly displayed. This helps to optimize the data transmission path and reduce data delays and losses during transmission. By utilizing the characteristics of the topological structure, rapid data routing and forwarding can be achieved, improving the real-time and reliability of data transmission.

[0062] Step 2: Collect historical charging data, calculate the weight of the edge between the child node and the parent node through the charging capacity 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 capacity 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;

[0063] The step 2 comprises the following steps:

[0064] Step 201: Collect historical charging data, including historical charging data of each child node (charging pile), historical charging 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 data records the charging capacity of each charging pile (child node) at time intervals (such as days, weeks, and months), as well as the total charging capacity received or distributed by each grid aggregation point (parent node). The charging data of the parent node needs to be aggregated and calculated based on the charging 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 charge of the child node and the physical distance between the child node and the parent node: Among them, 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 child node s, D s,p Represents the physical distance between a 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 the parent nodes by the charge amount of the parent nodes and the physical distance 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 Represent the historical average power consumption of parent nodes p1 and p2, respectively, p1,p2 Represents the physical distance between parent nodes p1 and p2, and parent_pairs represents the set of all parent node pairs;

[0068] Step 204: Using the weights calculated in steps 202 and 203, update the weights of the corresponding edges in the graph structure constructed in step 1.

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

[0070] When used, combine the contents of step 201 to step 204:

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

[0072] Step 3: Select a starting parent node to build a subnetwork, expand the subnetwork based on the weight of the edge between each parent node, divide the charging pile network into several subnetworks, check whether there is a parent node that has not been added to any subnetwork, and determine the central parent node of each subnetwork;

[0073] The step three comprises 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 a strategy such as the charge amount, 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 charge amount or the parent node with the most central geographical location as the starting point, and add the 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 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 (indicating that the charging characteristics of the two are similar or the physical distance is close), then add the 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] It should be noted 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 scale; if the threshold is set too low, it may lead to too few sub-networks and too large scale. Therefore, it needs to be adjusted according to specific needs and data characteristics;

[0078] Step 304: traverse all parent nodes and check whether there is a parent node that has not been added to any sub-network. If so, repeat steps 302 to 303 to add the parent node and its child nodes and edges to the new sub-network.

[0079] It should be noted that when the sub-network division is completed, check the results 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 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 of 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 subnetwork, and d(p,q) represents the shortest path length between node p and node q;

[0082] When used, combine the contents of step 301 to step 305:

[0083] By dividing the entire charging pile network into multiple sub-networks, the amount of data processing in 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 in the sub-network, the delay and error rate of data transmission 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 that there is a strong correlation between the nodes in each sub-network, which helps to reduce interference and noise during data transmission and improve the reliability of data transmission.

[0084] Step 4: Collect voltage and current data between parent nodes in real time and calculate the fluctuation rate. Determine the optimal path based on the topological structure of the charging pile network. When charging, collect charging data and use a sliding window to calculate the average of the charging data. If the average of the charging data exceeds the fluctuation threshold, reselect the optimal path.

[0085] The step 4 comprises the following steps:

[0086] Step 401: The voltage and current data between each parent node (grid aggregation point) are collected in real time through sensors or monitoring equipment installed in the power grid. The fluctuation rate of the parent node pair is calculated based on the voltage and current data between each parent node pair: Among them, 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 Represent the mean values ​​of voltage and current respectively, and M represents the number of data points;

[0087] Step 402: Based on the charging pile network topology constructed in step 1, generate 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: Among them, μ represents the stability index, 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 the path with the highest stability index is selected as the optimal path;

[0088] Step 403: When the electric vehicle is connected to the charging pile and starts charging, the charging data in the charging process is collected in real time through the built-in sensor of the charging pile, including but not limited to current, voltage, charging time, charging power and charging amount, and the charging data is sorted according to the timestamp to construct a charging data sequence, which includes but not limited to a current data sequence, a voltage data sequence and a charging power data sequence, and the charging data is transmitted to the charging information platform through the optimal path using power line communication technology;

[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 data processing requirements. For example, setting the step size to 1 means moving one data point each time. It can also be set to other values.

[0090] Step 405: Create the first sliding window from the starting position of the charging data sequence, accumulate the charging data in the sliding window to obtain the cumulative sum, calculate the ratio of the cumulative sum to the window size, and obtain the mean value of the charging data in the sliding window. If the mean value of the charging data in the sliding window exceeds a preset fluctuation threshold, repeat steps 401 to 402 to redetermine the optimal path.

[0091] It should be noted that the mean calculation is performed for a specific charging data (such as current, voltage, etc.). In actual applications, it is necessary to calculate the mean of different charging data separately, and make corresponding comparisons and analyses. 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 reselecting the optimal path can be triggered in time when the data is abnormal;

[0092] When used, combine the contents of step 401 to step 405:

[0093] By collecting voltage and current data between parent nodes in real time and calculating the fluctuation rate, changes in the power grid status can be discovered in a timely manner. Based on the charging pile network topology and the fluctuation rate of the parent node, the stability index of the path is calculated to select the optimal data transmission path, optimize the network structure of power communication, and ensure the stability and reliability of data transmission.

[0094] See also 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 building module collects the geographical location information of all charging piles, including the longitude and latitude coordinates, the number of charging piles, and the information of the convergence point of access to the power grid, and builds the topological structure of the charging pile network;

[0096] The topology analysis module collects historical charging data, calculates the weight of the edge between the child node and the parent node through the charge amount of the child node and the physical distance between the child node and the parent node, calculates the weight of the edge between the parent nodes through the charge amount of the parent node and the physical distance between the parent nodes, and updates the weight of the corresponding edge in the topology of the charging pile network;

[0097] The subnetwork partitioning module selects a starting parent node to build a subnetwork, expands the subnetwork based on the weight of the edge between each parent node, divides the charging pile network into several subnetworks, checks whether there are parent nodes that have not been added to any subnetwork, and determines the central parent node of each subnetwork;

[0098] The data processing module collects the voltage and current data between parent nodes in real time, calculates the fluctuation rate, and determines the optimal path based on the topological structure of the charging pile network. When charging, it collects charging data and uses a sliding window to calculate the average charging data. If the average charging data exceeds the fluctuation threshold, the optimal path is reselected.

[0099] In the application, the several formulas involved are all calculated by removing dimensions and taking their numerical values. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The coefficients in the formula are set by technicians in this field according to actual conditions.

[0100] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in combination with electronic hardware, computer software and electronic hardware. Whether these functions are performed 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, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0102] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered by the protection scope of the present application.

Claims

1. A real-time processing method for charging data applied to a charging information platform, characterized in that: The following steps are involved: Collect the geographic location information of all charging piles, including latitude and longitude coordinates, charging pile numbers, and grid access point information, and build the topology 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 charge 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 charge 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 build a subnetwork, expand the subnetwork based on the weight of the edge between each parent node, divide the charging pile network into several subnetworks, check whether there is a parent node that has not been added to any subnetwork, and determine the central parent node of each subnetwork; The voltage and current data between parent nodes are collected in real time, and the fluctuation rate is calculated. The optimal path is determined based on the topological structure of the charging pile network. When charging, the charging data is collected and the average charging data is calculated using a sliding window. If the average charging data exceeds the fluctuation threshold, the optimal path is reselected.

2. The real-time processing method for charging data applied to a charging information platform according to claim 1 is characterized in that: Construct the topology of the charging pile network, 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 is connected to the power grid as a parent node and assign a unique identifier to each parent node. According to the connection relationship between the charging pile and the grid convergence point, the edge between the child node and the parent node is determined. According to the connection relationship between the grid convergence points, the edge between the parent nodes is determined. Each edge contains directional information. Initialize an empty graph structure, which is used to store nodes and edges. Add child nodes and parent nodes to the graph as the vertex set of the graph. Add edges to the graph according to the connection relationship to connect the corresponding child nodes and parent nodes.

3. The real-time processing method for charging data applied to a charging information platform according to claim 1 is characterized in that: The weight of the edge between the child node and the parent node is calculated by the charge of the child node and the physical distance between the child node and the parent node: Among them, 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 child node s, D s,p Represents the physical distance between a child node s and its parent node p, and children(p) represents the set of all child nodes of the parent node p.

4. The real-time processing method for charging data applied to a charging information platform according to claim 3 is characterized in that: The weights of the edges between parent nodes are calculated by the charge of the parent nodes and the physical distance 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 Represent the historical average power consumption of parent nodes p1 and p2, respectively, 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 processing method for charging data applied to a charging information platform according to claim 1 is characterized in that: The charging pile network is divided into several sub-networks, 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 and check whether there is a parent node that has not been added to any sub-network. If so, repeat steps S2 to S3 to add the parent node and its child nodes and edges to the new sub-network.

6. The real-time processing method for charging data applied to a charging information platform according to claim 5 is characterized in that: Determine the central parent node of each sub-network, including: 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 the sub-network. The calculation formula of the closeness centrality index is: Among them, Cc(p) represents the closeness centrality index of node p, N represents the total number of nodes in the subnetwork, and d(p,q) represents the shortest path length between node p and node q.

7. The real-time processing method for charging data applied to a charging information platform according to claim 1 is characterized in that: Determine the optimal path based on the charging pile network topology, including: According to the voltage and current data between each parent node pair, the fluctuation rate of the parent node pair is calculated. Based on the constructed charging pile network topology, the data transmission path between all parent nodes is generated. For each candidate path, the stability index of the path is calculated according to the fluctuation rate of all parent node pairs on the path: Among them, μ represents the stability index, 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 the path with the highest stability index is selected as the optimal path.

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

9. The real-time processing method for charging data applied to a charging information platform according to claim 8 is characterized in that: Set the size and step size of the sliding window, create the first sliding window from the starting position of the charging data sequence, accumulate the charging data in the sliding window to obtain the cumulative sum, calculate the ratio of the cumulative sum to the window size, and obtain the mean of the charging data in the sliding window. If the mean of the charging data in the sliding window exceeds the preset fluctuation threshold, redetermine the optimal path.

10. A charging data real-time processing system applied to a charging information platform, used to implement the method according to any one of claims 1 to 9, characterized in that: include: The network topology building module collects the geographical location information of all charging piles, including the longitude and latitude coordinates, the number of charging piles, and the information of the convergence point of access to the power grid, and builds the topological structure of the charging pile network; The topology analysis module collects historical charging data, calculates the weight of the edge between the child node and the parent node through the charge amount of the child node and the physical distance between the child node and the parent node, calculates the weight of the edge between the parent nodes through the charge amount of the parent node and the physical distance between the parent nodes, and updates the weight of the corresponding edge in the topology of the charging pile network; The subnetwork partitioning module selects a starting parent node to build a subnetwork, expands the subnetwork based on the weight of the edge between each parent node, divides the charging pile network into several subnetworks, checks whether there are parent nodes that have not been added to any subnetwork, and determines the central parent node of each subnetwork; The data processing module collects the voltage and current data between parent nodes in real time, calculates the fluctuation rate, and determines the optimal path based on the topological structure of the charging pile network. When charging, it collects charging data and uses a sliding window to calculate the average charging data. If the average charging data exceeds the fluctuation threshold, the optimal path is reselected.

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