Electric vehicle charging pile cooperative management system based on intelligent city management

Abstract

This invention discloses a collaborative management system for electric vehicle charging piles based on intelligent urban management, specifically relating to the field of collaborative management technology for electric vehicle charging piles. The system includes the following steps: dynamically aggregating dispersed charging facilities into multiple collaborative management areas; a factor definition module calculating the resource elasticity coefficient and demand pressure coefficient for each area; constructing a collaboration degree matrix based on this; and a diagnostic analysis module parsing the matrix and outputting the state category of each area, indicating resource surplus, shortage, or collaborative criticality. Matching charging guidance and power regulation strategies are automatically generated based on the state category. This invention can accurately identify areas of supply-demand imbalance, guide investment optimization, and create new revenue streams such as frequency regulation services; it provides proactive early warning and intervention capabilities for cities and power grids, enhancing system safety resilience and the market competitiveness of virtual power plants; simultaneously, it promotes the standardization of cross-operator data interfaces and provides precise scenario planning for the commercialization of technologies such as V2G, accelerating industrial upgrading.

Description

Technical Field

[0001] This invention relates to the field of collaborative management technology for electric vehicle charging stations, and more specifically, to a collaborative management system for electric vehicle charging stations based on intelligent urban management. Background Technology

[0002] With the rapid increase in the number of electric vehicles, urban charging infrastructure is characterized by multi-operator construction and decentralized layout. Under the existing technological framework, the management and investment optimization of electric vehicle charging facilities face significant bottlenecks, which restrict the full release of their commercial value, system value, and industry progress potential.

[0003] From a business operation and investment perspective, the existing model relies heavily on local experience and static planning. Due to a lack of dynamic and quantitative insights into the "supply elasticity" and "demand pressure" within a city, operators struggle to accurately identify truly undervalued areas with tight supply and demand, as well as potentially over-competitive "saturated areas," leading to inefficient capital allocation and uncertainty in investment returns. Simultaneously, the potential of charging piles as distributed power assets has not been fully realized. Existing technologies struggle to achieve cross-operator resource aggregation and quantified flexibility, preventing them from participating in the power ancillary services market as a qualified regulatory resource and missing opportunities to transform idle capacity into new revenue streams.

[0004] From the perspective of power grid security and comprehensive urban governance, existing solutions are mostly reactive. Urban management lacks a system capable of issuing early warnings, similar to weather forecasting. Figure 1 This tool presents the dynamic coupling of charging "resource potential" and "demand pressure" in real-time, grid-like form. Therefore, it is difficult to proactively intervene before distribution network overload or traffic congestion occurs, and it cannot provide forward-looking data support for precise grid expansion and network reinforcement. Although the concept of virtual power plants has been proposed, existing technologies lack the ability to assess the "quality" (i.e., reliability and dispatchability) of aggregated resources, limiting their competitiveness in bidding and contract fulfillment in the electricity market. Therefore, this invention proposes a collaborative management system for electric vehicle charging piles based on intelligent urban management to address the above problems. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: The collaborative management system for electric vehicle charging stations based on intelligent urban management includes the following steps: The regional division module is used to dynamically aggregate charging facilities from different geographical locations and different operators to obtain multiple collaborative management areas based on power grid topology, traffic flow and historical charging behavior data. The factor definition module is used to calculate and define the resource elasticity coefficient reflecting the schedulable capacity of charging facilities and the demand pressure coefficient reflecting the intensity of real-time charging demand for each collaborative management area. The collaboration matrix module is used to construct a collaboration degree matrix that quantitatively represents the collaboration status of each collaborative management area based on the correspondence between the resource elasticity coefficient and the demand pressure coefficient. The diagnostic analysis module is used to parse the synergy matrix, diagnose and output the state category of each synergy management area, which is either resource surplus, resource shortage or synergy criticality. The strategy generation module is used to automatically generate charging guidance strategies and charging power control strategies that match the state of the collaborative management area.

[0006] In a preferred embodiment, the logic for dividing the collaborative management area is as follows: Identify a set of charging facilities with common power supply characteristics based on power grid topology; Acquire and integrate historical traffic flow data with historical charging behavior data; Analyze the spatiotemporal correlation between traffic connectivity intensity among charging facility clusters and charging demand; Based on the spatiotemporal correlation between traffic connectivity and charging demand, multiple charging facilities are aggregated, merged, or reorganized to generate multiple collaborative management areas.

[0007] In a preferred embodiment, analyzing the spatiotemporal correlation between traffic connectivity strength among charging facility clusters and charging demand specifically includes: Based on historical traffic flow data and road network data, a weighted graph with charging facility sets as nodes is constructed. The edge weight between any two nodes is calculated through the following steps: determine all effective paths between the two nodes, multiply the historical average travel time and historical average travel frequency on each path as the connectivity strength value of the path, and select the maximum connectivity strength value among all effective paths as the traffic connection strength between the two nodes. Based on historical charging behavior data, the charging load time series of each charging facility set within a preset period is extracted; the dynamic time warping distance between the charging load time series of any two charging facility sets is calculated, and the reciprocal of this distance is defined as the charging demand time correlation coefficient; at the same time, the proportion of charging requests originating from the same traffic area in the total requests of each set is statistically analyzed, and the Jaccard similarity coefficient of the two sets on this distribution is calculated as the charging demand spatial correlation coefficient. By combining the correlation coefficients of traffic connectivity intensity, charging demand time, and charging demand space, a correlation vector is obtained.

[0008] In a preferred embodiment, during dynamic aggregation, each charging facility set is used as an initial node, and an association vector is matched for each node to determine its association with other nodes. Based on the correlation vectors of all initial nodes, the Euclidean distance is used to calculate the comprehensive correlation distance between any two nodes; A cohesive hierarchical clustering method is used to iteratively merge node pairs whose overall correlation distance is less than a preset distance threshold to form clusters; Each resulting cluster is mapped to a collaborative management region.

[0009] In a preferred embodiment, the method for calculating the resource elasticity coefficient for each collaborative management area specifically includes: The system collects the rated power, currently available power, upper and lower limits of the adjustable power range, and historical average time delay of response commands for each charging facility within the collaborative management area. For each charging facility in the area, the ratio of the difference between the upper and lower limits of its power adjustment range to its rated power is calculated to obtain the individual power adjustment breadth; the reciprocal of the historical average time delay of its response command within the preset time window is calculated to obtain the individual response agility; the individual power adjustment breadth and the individual response agility are multiplied to obtain the individual elasticity component of the charging facility. The individual elasticity components of all charging facilities within the region are summed to obtain the regional aggregate elasticity baseline value. Assess the spatiotemporal distribution of the power available for charging facilities within the collaborative management area: Calculate the spatial distribution dispersion of the power available for each facility at the current moment, and the average amplitude of the fluctuation of the power available for each facility over time within a preset historical period. Add the spatial distribution dispersion and the average amplitude of the fluctuation over time, and then add one to obtain the power concentration resistance factor that characterizes the negative effects of the concentrated distribution. The resource elasticity coefficient of the collaboratively managed region is defined by dividing the regional aggregation elasticity base value by the power concentration resistance factor and then normalizing the resulting value.

[0010] In a preferred embodiment, the method for calculating the demand pressure coefficient for each collaborative management area specifically includes: Obtain the real-time requested power, the remaining battery power of the corresponding vehicle, and the estimated arrival time for each charging request within the collaborative management area; Based on real-time requested power and estimated arrival time, the time-series change of total requested power within a future preset period is calculated. The sum of the absolute values ​​of the changes in total requested power between adjacent time points within this period is calculated to obtain the total power fluctuation. The number of time points within this period where the total requested power exceeds the preset proportion of the region's historical average load is calculated to obtain the peak impact frequency. The total power fluctuation and the peak impact frequency are added together and then one is added to obtain the fluctuation intensity factor characterizing demand instability. For each charging request within the region, the urgency weight is calculated based on the remaining battery power of the vehicle. The real-time request power of each charging request is multiplied by its urgency weight and then summed to obtain the weighted total demand power for the region. The weighted total demand power of the region is divided by the fluctuation intensity factor, and the resulting value is normalized and defined as the demand pressure coefficient of the collaboratively managed region.

[0011] In a preferred embodiment, constructing a synergy degree matrix that quantifies the synergy state of a region specifically includes: A two-dimensional state plane is constructed with the resource elasticity coefficient as the vertical axis and the demand pressure coefficient as the horizontal axis; Each collaborative management area is mapped to a coordinate point on a two-dimensional state plane based on the specific values ​​of its resource elasticity coefficient and demand pressure coefficient. The two-dimensional state plane, all coordinate points, and their corresponding collaborative management area identifiers are collectively defined as the collaboration degree matrix.

[0012] In a preferred embodiment, the analytical synergy matrix specifically includes: Obtain the coordinates of each collaborative management area in the collaboration degree matrix; Compare the resource elasticity coefficient of the ordinate of each coordinate point with the preset elasticity threshold; Compare the horizontal axis pressure coefficient of each coordinate point with the preset pressure threshold. Based on the comparison results, the status category of the collaborative management area is determined as follows: when the resource elasticity coefficient is higher than the elasticity threshold and the demand pressure coefficient is lower than the pressure threshold, it is determined to be resource surplus; when the resource elasticity coefficient is lower than the elasticity threshold and the demand pressure coefficient is higher than the pressure threshold, it is determined to be resource shortage; and the rest are determined to be collaborative critical.

[0013] In a preferred embodiment, the strategy generation module has a built-in charging guidance strategy library and a charging power control strategy library. Based on the state category of the collaborative management area, it automatically matches and generates a charging guidance strategy and a charging power control strategy that match the state.

[0014] The technical effects and advantages of this invention are as follows: This invention dynamically aggregates dispersed charging facilities from different geographical locations and operators within a city into multiple collaborative management zones by integrating power grid topology, traffic flow, and historical charging behavior data. For each zone, it innovatively calculates resource elasticity coefficients and demand pressure coefficients, thereby constructing a collaboration matrix for precise status diagnosis. This technological approach fundamentally changes the traditional investment and operation model that relies on local experience and static planning. It enables investors and operators to clearly and quantitatively understand the true supply and demand situation in various areas across the entire city, accurately identifying resource-scarce areas with low resource elasticity coefficients and high demand pressure coefficients, as well as the opposite resource-scarce areas. This capability enables a leap from experience-driven to data-driven capital allocation, ensuring that construction and upgrade funds are prioritized for areas with the most prominent supply-demand contradictions and the greatest potential for investment returns, while avoiding ineffective competition in areas with already redundant supply. Furthermore, the system's identification of resource surplus areas not only points out the risk of investment saturation, but also reveals the potential of high-quality dispatchable resources that have not yet been fully utilized. Due to their high elasticity and low pressure characteristics, these areas are ideal aggregation units for future participation in ancillary service markets such as grid frequency regulation and reserve capacity. This opens up a business channel for operators to transform the utilization rate of idle equipment into new revenue, creating significant incremental economic value.

[0015] The synergy matrix and diagnostic results generated by this invention essentially constitute a dynamic, grid-based panoramic view of the coupling situation between urban charging resources and demand pressure. This unprecedented global visualization and quantitative insight capability provides a powerful tool for refined urban governance and proactive, safe operation of the power grid. Urban managers and power grid dispatching agencies can use this to identify areas with low resource elasticity and high demand pressure in advance. These areas are potential sources of distribution network overload risks and surrounding traffic congestion events. Through the early warning provided by this system, managers can proactively intervene and alleviate load before risks actually occur by using various flexible strategies such as floating electricity prices and real-time route guidance, transforming passive response into proactive prevention. At the same time, this accurate risk diagnostic data can also provide forward-looking and targeted decision-making basis for power grid companies' distribution network expansion, line renovation, and reinforcement projects, improving investment efficiency and system resilience. In addition, because the system can accurately measure the dispatchable potential of each collaboratively managed area, virtual power plants formed by the aggregation of multiple areas can participate in the electricity market by optimally combining bidding and dispatching based on resource quality, significantly improving the reliability of their response commands and market competitiveness.

[0016] The unified calculation method for cross-operator charging facility resource elasticity coefficients proposed in this invention, along with the accurate identification of V2G technology application scenarios based on a two-factor matrix, has profound significance for promoting overall technological progress and standardization in the industry. The calculation of resource elasticity coefficients requires the integration of real-time status, adjustability, and response performance data of charging facilities under different operators. This technical requirement directly forces various operating platforms to move towards compatibility and standardization in data interfaces, communication protocols, and even settlement methods, providing practical impetus for solving the long-standing data silo problem that has plagued industry development. Simultaneously, by analyzing the weak links on the power grid side, the average vehicle parking time, and the resource elasticity basis in various regions, the system can clearly identify areas with insufficient power grid support capacity but suitable for long-term parking. These areas are the priority scenarios where deploying vehicle-to-grid technology can generate the greatest economic and social benefits. This provides scientific planning guidance based on empirical data for the large-scale demonstration and commercialization of advanced technologies such as V2G, avoiding blind investment and accelerating the evolution of charging networks from single charging service infrastructure to intelligent two-way interactive resources supporting the construction of new power systems, thereby leading the entire industry towards a more efficient and collaborative direction. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the collaborative management system for electric vehicle charging piles based on intelligent urban management in this invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Figure 1 The following examples were obtained: Example 1: Under the current technological background, the management of urban electric vehicle charging networks faces structural challenges. Charging facilities are built and operated independently by different operators, resulting in a situation that is physically interconnected but fragmented in terms of management and data. This decentralization makes it difficult to conduct unified quantitative and coupled analysis of "supply-side adjustment capacity" and "real-time demand-side pressure" from a city-wide perspective. Existing solutions often only achieve local optimization or ex-post response, failing to proactively identify risk areas of supply-demand imbalance and potential areas of idle resources, and even more so failing to aggregate scattered charging piles into flexible resources that the power grid can recognize and dispatch. To systematically solve this core contradiction, namely overcoming the fundamental disconnect between decentralization and the need for global coordination, this technical solution aims to build an intelligent system that can penetrate operational boundaries, understand resource elasticity, sense demand pressure, and automatically generate control strategies. This provides a unified and indispensable technical logical starting point for the subsequent beneficial effects such as precise investment guidance, proactive safety early warning, and the promotion of industry standards.

[0020] This invention proposes a collaborative management system for electric vehicle charging stations based on intelligent urban management, comprising the following steps: The regional division module is used to dynamically aggregate charging facilities from different geographical locations and different operators to obtain multiple collaborative management areas based on power grid topology, traffic flow and historical charging behavior data. The factor definition module is used to calculate and define the resource elasticity coefficient reflecting the schedulable capacity of charging facilities and the demand pressure coefficient reflecting the intensity of real-time charging demand for each collaborative management area. The collaboration matrix module is used to construct a collaboration degree matrix that quantitatively represents the collaboration status of each collaborative management area based on the correspondence between the resource elasticity coefficient and the demand pressure coefficient. The diagnostic analysis module is used to parse the synergy matrix, diagnose and output the state category of each synergy management area, which is either resource surplus, resource shortage or synergy criticality. The strategy generation module is used to automatically generate charging guidance strategies and charging power control strategies that match the state of the collaborative management area.

[0021] In one specific implementation, the division of charging facility collaborative management areas begins with a deep understanding and analysis of the physical structure of the power grid. This process first obtains complete topological data, including substations, feeders, distribution transformers, and their connections, from the urban distribution network automation system, using this as the basic constraint framework for division. Specifically, based on this topological structure, all charging facilities directly powered by a specific 10kV feeder from the same 110kV substation or by the same public distribution transformer are identified. These facilities, sharing the same power supply circuit and capacity limit, have the strongest coupling relationship and safety boundary at the grid operation level, and are thus logically categorized into an initial set of charging facilities. For example, charging piles in five parking lots located in "Science and Technology Park Area A," after consulting the power grid asset GIS system, are confirmed to be powered entirely by "Xiaguang Substation Feeder No. 3." Therefore, regardless of which operator these charging piles belong to, they are all grouped into the same initial set, constituting a unit constrained by a unified power supply capacity.

[0022] To achieve management zoning that transcends grid constraints and better aligns with users' travel and charging habits, it's necessary to introduce traffic and historical behavior dimensions. The system synchronously acquires average traffic speed and flow data for road segments within historical periods from the city's traffic data center, and simultaneously extracts charging transaction records with precise timestamps and location tags from the charging operation platform within the same historical period. These multi-source data are fused after spatiotemporal alignment and cleaning, aiming to reveal the inherent connections between the various initial charging facility sets formed based on the grid topology in terms of user accessibility and demand patterns. The analysis is conducted for each pair of initial sets. On one hand, based on historical traffic flow and road networks, a comprehensive index of traffic intensity for all reasonable paths from the core location of set A to the core location of set B is calculated to quantify its traffic connectivity strength. On the other hand, the similarity of the historical charging load curves of the two sets is measured, and the spatial overlap of their service sources is analyzed, thereby obtaining a quantitative value of the temporal and spatial correlation of charging demand. This stage generates a feature vector characterizing the multidimensional correlation between any two initial sets.

[0023] The system uses each initial set of charging facilities as a basic node and the calculated multidimensional association feature vectors as edge attributes between nodes to construct a network graph characterizing the relationships between facility groups. Based on this network graph, a cohesive hierarchical clustering algorithm based on Euclidean distance is used to iteratively merge all nodes. The algorithm prioritizes clustering nodes with close traffic connections and highly similar spatiotemporal patterns of charging demand until the overall association distance between nodes within a cluster is less than a threshold set based on experience and multiple simulations, while the distance between clusters exceeds this threshold. This process achieves intelligent coupling and reorganization of the physical power supply boundary and the user behavior boundary.

[0024] Each stable cluster generated by the clustering algorithm is defined as a collaborative management area. Within this area, charging facilities not only share a common power grid supply, but more importantly, users within their service area exhibit high homogeneity and substitutability in terms of travel routes and charging habits. For example, the initial set powered by the "Xiaguang Substation Feeder No. 3" might be merged with another set powered by the adjacent "Biyun Substation Feeder No. 5," but closely connected by urban expressways and with overlapping residential areas, during the clustering process. The resulting "Kechuang-Biyun North District" collaborative management area is a refined operational unit that balances power grid constraints, road connectivity efficiency, and user demand characteristics, laying a reasonable spatial foundation for subsequent flexible assessment and collaborative scheduling.

[0025] The analysis of the spatiotemporal correlation between traffic connectivity strength and charging demand among charging facility sets includes: constructing a weighted graph with charging facility sets as nodes based on historical traffic flow data and road network data. The edge weight between any two nodes is calculated through the following steps: determining all effective paths between the two nodes, multiplying the historical average travel time and historical average travel frequency on each path as the connectivity strength value of the path, and selecting the maximum connectivity strength value among all effective paths as the traffic connectivity strength between the two nodes. Based on historical charging behavior data, the charging load time series of each charging facility set within a preset period is extracted; the dynamic time warping distance between the charging load time series of any two charging facility sets is calculated, and the reciprocal of this distance is defined as the charging demand time correlation coefficient; at the same time, the proportion of charging requests originating from the same traffic area in the total requests of each set is statistically analyzed, and the Jaccard similarity coefficient of the two sets on this distribution is calculated as the charging demand spatial correlation coefficient. By combining the correlation coefficients of traffic connectivity intensity, charging demand time, and charging demand space, a correlation vector is obtained.

[0026] In one specific implementation, the spatiotemporal correlation between traffic connectivity strength and charging demand among charging facility sets is analyzed. Based on historical traffic flow data from the past six months provided by the urban traffic management department, as well as road network data including road grade, number of lanes, and topology, each charging facility set defined by the power grid topology is abstracted as a node, constructing an initial fully connected weighted graph. For any two nodes in the graph, such as node A representing the power supply area of ​​the High-tech Zone Software Park and node B representing the power supply area of ​​the adjacent University Town, the weight of their edges, i.e., traffic connectivity strength, needs to be accurately calculated. This calculation process is completed by traversing all effective travel paths connecting representative locations of the two nodes. For each effective path, the average travel time and average daily travel frequency of each segment on the path during the morning rush hour on historical weekdays are extracted, and the two are multiplied to obtain a connectivity strength value reflecting the overall travel convenience of the path. Among all the connectivity strength values ​​of effective paths, the maximum value is selected as the final traffic connectivity strength between node A and node B. This design takes into account that users naturally tend to choose the path with the lowest overall travel cost, thus best reflecting the potential traffic flow connection.

[0027] To quantify the temporal correlation of charging demand, charging power records for all charging facilities across the same six-month historical period are obtained from the charging operation platform. These records are then used at one-hour intervals to generate daily charging load time series for each set. To compare the similarity of charging demand patterns between two sets, such as node A and node B, a dynamic time warping algorithm is used to calculate the distance between the two daily average load curves. This algorithm overcomes minor shifts in peak charging times (e.g., one set peaks at 18:00 while the other peaks at 18:30) by finding the optimal alignment path to calculate a cumulative distance value. The reciprocal of this dynamic time warped distance is then normalized to obtain a charging demand temporal correlation coefficient ranging from 0 to 1. A higher coefficient indicates a more synchronized trend in the charging activity periods of the two sets.

[0028] To define spatial correlation, the spatial origin of charging demand is analyzed. Based on the traffic zone division in urban planning, the historical charging request sources for each charging facility set are statistically analyzed. For example, 30% of charging requests for node A come from traffic zone A, and 25% come from traffic zone B; for node B, 40% of requests come from traffic zone A, and 10% come from traffic zone B. Treating the request sources of each set as a single set, the Jaccard similarity coefficient between the two source sets is calculated. This coefficient is the sum of the request volume percentages of the intersection of the two sets (common source zones), divided by the sum of the request volume percentages of the union of the two sets. This coefficient directly reflects the degree of overlap in the customer sources served by the two charging facility sets, serving as the spatial correlation coefficient of charging demand.

[0029] The calculated traffic connectivity strength, charging demand temporal correlation coefficient, and charging demand spatial correlation coefficient are sequentially combined into a three-dimensional correlation vector. For example, the correlation vector between node A and node B might be represented as (0.85, 0.76, 0.45), indicating that they have strong physical connectivity, highly similar charging demand temporal patterns, but moderate overlap in their customer base. This multi-dimensional vector provides accurate and comprehensive input for subsequent clustering and merging decisions based on quantitative indicators, overcoming the limitations of single-dimensional analysis.

[0030] In one specific implementation, after the correlation vector is calculated, a dynamic aggregation process based on quantitative indicators is initiated to form the final area for collaborative management. This process first defines each set of charging facilities initially defined by the power grid topology as an initial node, and assigns each pair of nodes the previously calculated three-dimensional correlation vector. For example, in a network containing ten initial nodes, node one and node two have a correlation vector V12, node one and node three have a vector V13, and so on, thus constructing a complete model for the entire network that quantitatively describes the multidimensional correlation between any two nodes.

[0031] Based on these correlation vectors, the comprehensive correlation distance between each pair of initial nodes is calculated as a quantitative basis for determining whether they should be aggregated into the same management area. Specifically, the Euclidean distance formula is used, treating the three components of a correlation vector as coordinate points in three-dimensional space, and calculating the straight-line distance between two points. For example, the correlation vector of node one is (0.85, 0.76, 0.45) compared to node two, and (0.30, 0.25, 0.80) compared to node three. Calculations show that node one and node two are closer in the three-dimensional correlation space composed of traffic connections, time patterns, and spatial sources, indicating higher overall similarity and potential for merging. This distance value comprehensively reflects the overall differences between the two charging facility sets in terms of accessibility, demand fluctuation patterns, and user composition.

[0032] A cohesive hierarchical clustering algorithm is used to iteratively merge all initial nodes. Initially, each node is treated as an independent cluster. In each iteration, the average composite correlation distance between all cluster pairs is calculated, and the pair with the smallest distance is identified. If this minimum distance is less than a pre-set distance threshold determined through historical data simulation and grid search optimization, these two clusters are merged into a new cluster. This process is repeated cyclically. For example, initially, nodes four and seven, which are very close, are merged. In the next iteration, this new cluster may be merged with node five, until the minimum average distance between any two clusters is greater than the preset threshold, at which point the clustering process terminates. This threshold setting aims to ensure that each cluster has high homogeneity within itself, while maintaining significant heterogeneity between clusters.

[0033] Each stable cluster formed after the agglomerative hierarchical clustering algorithm stops is formally mapped and defined as a collaborative management region. For example, the initial ten nodes, after the above calculations and iterations, may be aggregated into three independent clusters: the first cluster is formed by merging nodes one, two, and five, mainly covering the city's central business district and adjacent high-end residential areas; the second cluster contains nodes three, eight, and ten, corresponding to the university town and research institution area; and the third cluster consists of nodes four, six, seven, and nine, serving the periphery of large transportation hubs and logistics parks. Within each generated collaborative management region, charging facilities not only have a connection in terms of power grid supply, but also exhibit a high degree of consistency in user travel habits and demand characteristics, thus laying a logically rigorous spatial partitioning foundation for implementing unified and precise flexible assessment and collaborative scheduling strategies.

[0034] In one specific implementation, calculating the resource elasticity coefficient for a collaborative management area begins with a comprehensive collection and statistical analysis of the operating parameters of all charging facilities belonging to different operators within that area. The parameters to be statistically analyzed include the rated power of the charging facilities, the currently available power as monitored in real time, the upper and lower limits of the adjustable power range set by the equipment controller or operating platform, and the historical average time delay of the facility in responding to remote control commands, calculated based on historical records. For example, for a collaborative management area containing fifteen charging piles, it is necessary to obtain the rated power value on the nameplate of each pile, query its real-time load status to determine the currently unoccupied and available power for scheduling, read the highest and lowest allowed operating power values ​​set in its background configuration, and retrieve the average time taken to receive and execute power adjustment commands each time over the past 72 hours.

[0035] After obtaining the aforementioned basic data, the individual elasticity contribution of each charging facility within the region is quantitatively calculated. The individual power adjustment breadth of the facility is calculated, which is the difference between the upper and lower limits of the adjustable power range, divided by the facility's rated power. This ratio characterizes the theoretical adjustment range of the device's power output. Simultaneously, the individual response agility of the facility is calculated, which is a preset reference time constant divided by the historical average time delay of the facility's response command. This reference time constant can be set according to regional control requirements, for example, ten minutes. A higher individual response agility indicates a faster device response speed. Subsequently, the calculated individual power adjustment breadth and individual response agility are multiplied; the product is defined as the individual elasticity component of the charging facility. This component comprehensively reflects the basic elasticity qualities of a single device in terms of both adjustment capability and response speed.

[0036] After completing individual calculations for all charging facilities, regional aggregation is performed. The individual elasticity components of all charging facilities within the region are arithmetically summed, and the resulting cumulative value is the regional aggregated elasticity base value for the collaboratively managed region. This base value theoretically represents the simple sum of the individual elasticity potentials of all charging facilities within the region. However, to more accurately reflect the ease with which resources can be collaboratively scheduled in the spatiotemporal dimension, the distribution of resources must be further evaluated. Therefore, a power concentration resistance factor is calculated. This factor is calculated in two parts: first, assessing the spatial dispersion of the allocatable power values ​​of each charging facility within the region at the current moment, quantified using the ratio of standard deviation to mean (coefficient of variation), reflecting the uneven distribution of resources at spatial locations; second, assessing the average amplitude of the total allocatable power of all facilities within the region fluctuating over a preset historical period, such as the past 24 hours, reflecting the instability of resources over time. Finally, the spatial dispersion and the average amplitude of fluctuation over time are directly added together, and then one is added to ensure the denominator is positive, thus obtaining the power concentration resistance factor. The larger this factor is, the more concentrated and unstable the spatiotemporal distribution of resources is, and the greater the resistance to the overall elasticity.

[0037] Dividing the previously calculated regional aggregate resilience baseline value by the power concentration resistance factor yields a preliminary resilience evaluation value. To ensure comparability of coefficients between regions of different sizes, this preliminary evaluation value is processed by a normalization function designed based on historical maximum and minimum values ​​or statistical quantiles of all regions, mapping the output value to a standard interval between zero and one. The final value obtained after this normalization process is formally defined as the resource resilience coefficient of the collaboratively managed region. For example, a region with excellent equipment performance but whose power resources are entirely concentrated in a few sites and fluctuate drastically over time may have a high regional aggregate resilience baseline value, but its power concentration resistance factor is also very large. The final calculated resource resilience coefficient will be significantly reduced, thus accurately revealing that the actual availability of its resilience resources is severely constrained by its distribution pattern.

[0038] In one specific implementation, calculating the demand pressure coefficient for a collaboratively managed area first requires acquiring real-time charging demand information reported from various user terminals and vehicles. This information pertains to each charging request that is being initiated or about to take effect within the collaboratively managed area, specifically including the real-time requested power, the remaining battery charge status of the corresponding electric vehicle at the time the request was made, and the estimated arrival time based on the vehicle's current location and navigation route. For example, for a collaboratively managed area covering a large transportation hub and surrounding business district, dozens of charging requests may converge simultaneously during the evening peak hours. Each request carries a planned charging power, an urgent status indicating that the vehicle currently has only 15% battery charge remaining, and different time stamps indicating an estimated arrival time within 10 to 30 minutes. This real-time data constitutes the raw input for the pressure assessment.

[0039] A quantitative analysis of the dynamic instability of demand is conducted. Based on the real-time requested power and expected arrival time of all charging requests, these are aligned according to the expected arrival time to generate a time-series curve of the total requested power in the region at 15-minute intervals within a preset two-hour period. Analyzing this time-series curve involves two aspects: First, calculating the absolute value of the change in total requested power between adjacent time points and summing all these absolute values ​​to obtain the total power fluctuation reflecting the overall severity of demand fluctuations. Second, setting a pressure threshold, such as 120% of the average charging load of the same period over the past 30 working days in this collaboratively managed region, as a benchmark, and counting the number of time points on the total requested power time-series curve exceeding this benchmark within the next two hours, this number represents the peak impact frequency, reflecting the number of times demand impacts the system's basic carrying capacity. The calculated total power fluctuation is directly added to the peak impact frequency, with an increment to avoid mathematical anomalies. The result is defined as the fluctuation intensity factor characterizing demand instability. The higher the factor value, the more volatile and peak-impact-prone the future demand will be.

[0040] To assess the overall scale and urgency of the demand, for each individual charging request within the region, a demand urgency weight needs to be calculated based on the remaining battery power of the vehicle. This weight can be obtained through a predetermined non-linear function mapping; for example, when the remaining battery power is below 20%, the weight increases exponentially from 1.0, aiming to quantify the higher priority demand for charging resources from vehicles with low battery power. Alternatively, other calculation methods in existing technologies can be used to achieve the same purpose. Then, the real-time request power of each charging request is multiplied by its corresponding demand urgency weight to obtain the weighted request power for that request. Finally, the weighted request powers of all charging requests within the region are summed to obtain the region's weighted total demand power. This value not only reflects the total power scale of the demand but, more importantly, embeds the differences in demand quality arising from the varying degrees of battery urgency among users through weighting.

[0041] Dividing the regional weighted total demand power by the previously calculated fluctuation intensity factor yields a preliminary pressure assessment value. This division operation physically means that the overall weighted demand scale will be "reduced" or "amplified" by the intensity of demand fluctuations. A high but very stable total demand may produce similar pressure assessment values ​​as a slightly lower but drastically fluctuating total demand. To ensure comparability of coefficients across different regions and time scales, this preliminary assessment value needs to be normalized. Specifically, extreme value normalization can be performed using the maximum and minimum values ​​of the pressure assessment values ​​for all calculation moments within the collaborative management region's historical statistical period (e.g., the past month), mapping the output value to a standard range of zero to ten. The final value obtained after this processing is formally defined as the demand pressure coefficient of the collaborative management region at the current moment. For example, a region with a high total weighted demand power but extremely stable future demand forecasts (small volatility factor) may be assessed as having a moderate final demand pressure coefficient; while another region with a moderate total weighted demand power but forecasts showing that it will fluctuate sharply and surge multiple times in a short period of time (large volatility factor) may have a higher demand pressure coefficient, thus warning that it faces more complex scheduling challenges.

[0042] In one specific implementation, constructing a coordination degree matrix to quantitatively characterize the regional coordination state begins with establishing an analytical framework for unified evaluation and comparison. The core of this framework is a specially designed two-dimensional state plane, whose design follows clear physical meaning: the resource elasticity coefficient, representing the flexibility of charging resource supply, is used as the vertical axis, with values ​​increasing from zero, indicating that the schedulable potential of resources is increasing; simultaneously, the demand pressure coefficient, representing the intensity of real-time charging demand surges, is used as the horizontal axis, with values ​​increasing from zero to the right, indicating that the pressure exerted by demand on the system is increasing. Through the establishment of this orthogonal coordinate system, an intuitive and measurable location space is provided for the comprehensive state of each coordinated management region.

[0043] Each independent collaborative management region is precisely mapped onto this two-dimensional state plane based on its latest calculated indicator values. Specifically, the resource elasticity coefficient and demand pressure coefficient of each region are read, with the former used as the ordinate and the latter as the abscissa, thus determining a unique coordinate point on the plane. For example, a region with a resource elasticity coefficient of 0.85 and a demand pressure coefficient of 0.40 will have its coordinate point located in the upper right-left position of the plane; while another region with a resource elasticity coefficient of 0.30 and a demand pressure coefficient of 0.75 will have its coordinate point located in the lower right position. Each coordinate point is clearly labeled with its corresponding collaborative management region identifier, such as "Region A - Central Business District" or "Region D - Peripheral Industrial Park," thereby establishing a direct link from abstract data to concrete management entities. The two-dimensional state plane, all coordinate points, and their corresponding collaborative management region identifiers are collectively defined as a collaboration degree matrix.

[0044] As an extension of this embodiment, to more deeply characterize the relative relationships and overall distribution patterns between regions, after generating all coordinate points, a feature analysis based on statistical distribution can be further performed on the point set. For example, the geometric center position of all coordinate points on the two-dimensional state plane can be calculated, which represents the "average state" of the current matching of charging resources and demand in the entire city. At the same time, the clustering density of coordinate points in different quadrants can be analyzed, or their dispersion along the diagonal direction can be calculated. These derived indicators can reveal from a macroscopic perspective whether the system as a whole is biased towards abundant resources, tight supply and demand, or a differentiated state, providing situational awareness for upper-level decision-making. The constructed two-dimensional state plane, all clearly marked coordinate points on the plane and their corresponding collaborative management area identifiers, and the macroscopic distribution feature parameters obtained from the point set analysis are collectively encapsulated and defined as a structured data object, namely, the collaboration degree matrix. This matrix is ​​not a simple mathematical matrix, but a data complex integrating a visual coordinate system, individual state mapping, group distribution characteristics, and metadata. As a core input for subsequent diagnostic analysis, it enables operators or automated algorithms to gain a clear understanding of the real-time status of all areas, accurately locate abnormal areas (such as points clustered in the high-stress-low-elasticity quadrant), and provide quantitative evidence for understanding the overall level of collaboration.

[0045] In one specific implementation, parsing the collaboration degree matrix to diagnose the status category of each collaborative management area begins by extracting each recorded coordinate point and its associated collaborative management area identifier from the constructed collaboration degree matrix data object. The vertical coordinate value of each coordinate point is the resource elasticity coefficient of the corresponding area, and the horizontal coordinate value is the demand pressure coefficient of the corresponding area. This condenses the complex and multi-dimensional operating status of each area into a pair of values ​​that can be used for intuitive comparison. For example, if the coordinate point (0.65, 0.50) identified as "Area C - High-tech Park" is read from the matrix, it means that the current resource elasticity coefficient of this area is 0.65 and the demand pressure coefficient is 0.50.

[0046] The ordinate value of each coordinate point, i.e., the resource elasticity coefficient, is compared with a preset elasticity threshold. This threshold is not arbitrary; its core basis lies in statistical analysis of historical operating data and engineering judgment of the grid's capacity to accommodate and regulate resources. One specific method is to calculate the historical average or median of the resource elasticity coefficient for all collaboratively managed areas of the city over the past quarter, and use this statistical value as the benchmark elasticity threshold. This method ensures that the threshold reflects the overall normal level of system elasticity, making the judgment results historically consistent and comparable. Similarly, the abscissa value of each coordinate point, i.e., the demand pressure coefficient, is compared with a preset pressure threshold. This pressure threshold is mainly based on the constraints of safe operation of the distribution system and an empirical model of user satisfaction. For example, a pressure threshold is comprehensively determined by analyzing the critical range of pressure coefficients that lead to heavy overload of distribution transformers or a significant increase in average user queuing time in historical data.

[0047] Based on the results of the two comparisons above, a logically clear classification rule is implemented. For a specific collaborative management area, if its resource elasticity coefficient is higher than a preset elasticity threshold, while its demand pressure coefficient is lower than a preset pressure threshold, the area is judged to be in a state of resource surplus. This indicates that the supply of dispatchable charging resources in the area is relatively abundant, while the real-time demand intensity is relatively moderate, and there is potential for underutilization of resources. Conversely, if its resource elasticity coefficient is lower than the preset elasticity threshold, while its demand pressure coefficient is higher than the preset pressure threshold, the area is judged to be in a state of resource shortage. This warns that the dispatchable capacity of the area is insufficient, while the real-time demand shock is strong, making it the area with the highest operational risk and user queuing risk. All other situations besides the two explicit cases mentioned above, such as the resource elasticity coefficient and demand pressure coefficient being simultaneously higher or lower than the threshold, or one being higher than the threshold while the other is close to the threshold, are judged to be in a collaborative critical state. This state indicates that the supply and demand relationship in the area is on the edge of sensitive or dynamic equilibrium, requiring close monitoring but not necessarily immediate strong intervention.

[0048] After completing the traversal, comparison, and logical judgment of all collaborative management areas, a structured status diagnostic report is output. This report not only lists the status category of each area, such as "Area C - High-tech Park: Collaborative Critical" and "Area E - Transportation Hub: Resource Shortage," but also includes the specific differences between its coordinates and threshold values ​​for in-depth analysis. This diagnostic result directly reveals the weak links and advantageous areas in the urban charging network where the "supply elasticity" and "demand pressure" are mismatched, providing precise and quantitative decision-making input for subsequent targeted triggering of differentiated collaborative management strategies. Through the threshold setting method based on a combination of historical data statistics and engineering experience, the entire diagnostic process is free from subjective assumptions, possessing solid existing technical support and repeatable objectivity.

[0049] In one specific implementation, matching management strategies are automatically generated based on the status category of the collaborative management area. This relies on a pre-built and continuously optimized strategy knowledge base. This knowledge base is explicitly divided into a charging guidance strategy library and a charging power control strategy library. The charging guidance strategy library contains a series of strategy templates and their parameterized descriptions designed to influence users' choices of charging locations and times. For example, for a resource surplus state, pre-set entries in the strategy library might include "pushing users service fee discount information with a specific discount rate," "increasing the display priority of charging stations in this area in the map navigation interface," and "generating personalized recommended routes to this area." The charging power control strategy library contains a series of strategy templates that directly affect the operating parameters of charging facilities. For example, for a resource shortage state, pre-set entries might include "uniformly reducing the output power limit of all charging facilities in the area to 70% of the rated power," "automatically activating the power ramp-up mode for vehicles with high state of charge," or "activating a priority charging queue for vehicles with low battery levels."

[0050] Once the diagnostic analysis determines the status category of a collaborative management area, the strategy generation process begins. The system uses the determined status category, such as "resource surplus," "resource scarcity," or "collaborative criticality," as an index key to simultaneously query the charging guidance strategy library and the charging power control strategy library. The query operation aims to retrieve all strategy templates strongly correlated with the given status category and marked as currently available. For example, for "Area F - Ecological Residential Area," which is determined to have resource surplus, the system will retrieve templates such as "push service fee discount" and "increase navigation priority" from the guidance strategy library, and "remove power float restriction" from the control strategy library.

[0051] After retrieving the corresponding strategy template, the system instantiates and populates the variable parameters in the template based on the real-time context information of the collaborative management area, thereby generating specific, immediately executable strategy instructions. For example, when applying the "Push Service Fee Discount" template, the system calculates a specific discount rate (e.g., 20% off) based on the current time period and the area's historical utilization rate and enters it into the instruction; when applying the "Unified Reduction of Output Power Limit" template, it determines a specific reduction ratio (e.g., reducing to 65% of rated power) as the instruction parameter based on the real-time grid load and average vehicle waiting time in the area. This parameterization process ensures the adaptability and accuracy of the strategy.

[0052] The system categorizes and encapsulates the generated specific policy instruction sets. All charging guidance policy instructions involving user interaction and services, such as push messages containing promotional information and navigation routes, are assembled into an instruction package, ready to be sent to the user terminal application. All charging power regulation policy instructions involving device control, such as commands to adjust the power limit and enable special modes, are assembled into another instruction package, ready to be sent to the charging facility centralized controller or edge computing unit in the corresponding collaborative management area.

[0053] The above algorithms or formulas are all dimensionless and numerical calculations, and the results are obtained by software simulation based on a large amount of collected data to obtain the most recent real-world results. The preset parameters are set by those skilled in the art according to the actual situation.

[0054] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0055] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0056] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0057] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A collaborative management system for electric vehicle charging piles based on intelligent urban management, characterized in that, Includes the following steps: The region division module is used to dynamically aggregate multiple collaborative management regions based on power grid topology, traffic flow, and historical charging behavior data. The factor definition module is used to calculate and define the resource elasticity coefficient reflecting the schedulable capacity of charging facilities and the demand pressure coefficient reflecting the intensity of real-time charging demand for each collaborative management area. The collaboration matrix module is used to construct a collaboration degree matrix that quantitatively represents the collaboration status of each collaborative management area based on the correspondence between the resource elasticity coefficient and the demand pressure coefficient. The diagnostic analysis module is used to parse the synergy matrix, diagnose and output the state category of each synergy management area, which is either resource surplus, resource shortage or synergy criticality. The strategy generation module is used to automatically generate charging guidance strategies and charging power control strategies that match the state of the collaborative management area.

2. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 1, characterized in that, The logic for dividing collaborative management areas is as follows: Identify a set of charging facilities with common power supply characteristics based on power grid topology; Acquire and integrate historical traffic flow data with historical charging behavior data; Analyze the spatiotemporal correlation between traffic connectivity intensity among charging facility clusters and charging demand; Based on the spatiotemporal correlation between traffic connectivity and charging demand, multiple charging facilities are aggregated, merged, or reorganized to generate multiple collaborative management areas.

3. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 2, characterized in that, The analysis examines the spatiotemporal correlation between traffic connectivity intensity among charging facility clusters and charging demand, specifically including: Based on historical traffic flow data and road network data, a weighted graph with charging facility sets as nodes is constructed. The edge weight between any two nodes is calculated through the following steps: determine all effective paths between the two nodes, multiply the historical average travel time and historical average travel frequency on each path as the connectivity strength value of the path, and select the maximum connectivity strength value among all effective paths as the traffic connection strength between the two nodes. Based on historical charging behavior data, the charging load time series of each charging facility set within a preset period is extracted; the dynamic time warping distance between the charging load time series of any two charging facility sets is calculated, and the reciprocal of this distance is defined as the charging demand time correlation coefficient; at the same time, the proportion of charging requests originating from the same traffic area in the total requests of each set is statistically analyzed, and the Jaccard similarity coefficient of the two sets on this distribution is calculated as the charging demand spatial correlation coefficient. By combining the correlation coefficients of traffic connectivity intensity, charging demand time, and charging demand space, a correlation vector is obtained.

4. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 3, characterized in that, During dynamic aggregation, each set of charging facilities is used as an initial node, and an association vector is matched for each node to determine its relationship with other nodes. Based on the correlation vectors of all initial nodes, the Euclidean distance is used to calculate the comprehensive correlation distance between any two nodes; A cohesive hierarchical clustering method is used to iteratively merge node pairs whose overall correlation distance is less than a preset distance threshold to form clusters; Each resulting cluster is mapped to a collaborative management region.

5. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 4, characterized in that, The specific methods for calculating the resource elasticity coefficient for each collaborative management area include: The system collects the rated power, currently available power, upper and lower limits of the adjustable power range, and historical average time delay of response commands for each charging facility within the collaborative management area. For each charging facility in the area, the ratio of the difference between the upper and lower limits of its power adjustment range to its rated power is calculated to obtain the individual power adjustment breadth; the reciprocal of the historical average time delay of its response command within the preset time window is calculated to obtain the individual response agility; the individual power adjustment breadth and the individual response agility are multiplied to obtain the individual elasticity component of the charging facility. The individual elasticity components of all charging facilities within the region are summed to obtain the regional aggregate elasticity baseline value. Assess the spatiotemporal distribution of the power available for charging facilities within the collaborative management area: Calculate the spatial distribution dispersion of the power available for each facility at the current moment, and the average amplitude of the fluctuation of the power available for each facility over time within a preset historical period. Add the spatial distribution dispersion and the average amplitude of the fluctuation over time, and then add one to obtain the power concentration resistance factor that characterizes the negative effects of the concentrated distribution. The resource elasticity coefficient of the collaboratively managed region is defined by dividing the regional aggregation elasticity base value by the power concentration resistance factor and then normalizing the resulting value.

6. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 5, characterized in that, The specific methods for calculating the demand pressure coefficient for each collaborative management area include: Obtain the real-time requested power, the remaining battery power of the corresponding vehicle, and the estimated arrival time for each charging request within the collaborative management area; Based on real-time requested power and estimated arrival time, the time-series change of total requested power within a future preset period is calculated. The sum of the absolute values ​​of the changes in total requested power between adjacent time points within this period is calculated to obtain the total power fluctuation. The number of time points within this period where the total requested power exceeds the preset proportion of the region's historical average load is calculated to obtain the peak impact frequency. The total power fluctuation and the peak impact frequency are added together and then one is added to obtain the fluctuation intensity factor characterizing demand instability. For each charging request within the region, the urgency weight is calculated based on the remaining battery power of the vehicle. The real-time request power of each charging request is multiplied by its urgency weight and then summed to obtain the weighted total demand power for the region. The weighted total demand power of the region is divided by the fluctuation intensity factor, and the resulting value is normalized and defined as the demand pressure coefficient of the collaboratively managed region.

7. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 6, characterized in that, Constructing a synergy degree matrix that quantitatively represents the synergy state of a region specifically includes: A two-dimensional state plane is constructed with the resource elasticity coefficient as the vertical axis and the demand pressure coefficient as the horizontal axis; Each collaborative management area is mapped to a coordinate point on a two-dimensional state plane based on the specific values ​​of its resource elasticity coefficient and demand pressure coefficient. The two-dimensional state plane, all coordinate points, and their corresponding collaborative management area identifiers are collectively defined as the collaboration degree matrix.

8. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 7, characterized in that, The analytical synergy matrix specifically includes: Obtain the coordinates of each collaborative management area in the collaboration degree matrix; Compare the resource elasticity coefficient of the ordinate of each coordinate point with the preset elasticity threshold; Compare the horizontal axis pressure coefficient of each coordinate point with the preset pressure threshold. Based on the comparison results, the status category of the collaborative management area is determined as follows: when the resource elasticity coefficient is higher than the elasticity threshold and the demand pressure coefficient is lower than the pressure threshold, it is determined to be resource surplus; when the resource elasticity coefficient is lower than the elasticity threshold and the demand pressure coefficient is higher than the pressure threshold, it is determined to be resource shortage; and the rest are determined to be collaborative critical.

9. The electric vehicle charging pile collaborative management system based on intelligent urban management according to claim 8, characterized in that, The strategy generation module has a built-in charging guidance strategy library and charging power control strategy library. Based on the status category of the collaborative management area, it automatically matches and generates charging guidance strategies and charging power control strategies that match the status.

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