Multi-user shared charging pile management method and device

Abstract

The invention relates to the technical field of charging piles, and provides a multi-user shared charging pile management method and equipment, and the method comprises the steps: obtaining the charging demand data of each user, and generating a charging scheduling and distribution scheme based on the charging demand data; and generating a charging control sequence and a preliminary control parameter based on the distribution scheme, optimizing the charging control sequence by using the preliminary control parameter to obtain user feedback, and optimizing the charging scheduling and distribution scheme based on the user feedback to obtain a shared charging pile management strategy. Through the combination of performance evaluation and user feedback, the scheduling and load distribution strategy is iteratively optimized, the utilization rate of the charging pile is improved, the waiting time of the user is shortened, the resource waste is reduced, and the problems of low scheduling efficiency, non-uniform resource distribution and insufficient dynamic adjustment capability in the face of complex charging requirements of multiple users are solved.

Description

Technical Field

[0001] This application relates to the technical field of charging piles, and in particular to a management method and equipment for multi-user shared charging piles. Background Technology

[0002] In recent years, with the rapid popularization of electric vehicles, the demand for charging stations has increased dramatically. Shared charging stations, as an innovative model for optimizing resource allocation, are gradually being promoted in cities and communities. Through shared charging stations, existing charging resources can be effectively utilized to provide convenient charging services for multiple users, alleviating the problem of insufficient charging infrastructure and promoting the development of the new energy industry.

[0003] Among relevant technical approaches, charging pile management methods typically rely on fixed, predetermined scheduling strategies or simple real-time scheduling algorithms. These methods allow management systems to allocate and schedule charging pile resources. For example, a reservation-based charging model can allocate charging piles according to the user's reserved time, or handle user charging requests on a first-come, first-served basis. These methods achieve basic utilization of charging resources, improve the charging experience for some users, reduce waiting time, and avoid resource waste.

[0004] While the above-mentioned technical solutions can improve the utilization rate of charging piles and optimize the user's charging experience through scheduled or real-time allocation, they still suffer from problems such as low scheduling efficiency, uneven resource allocation, and insufficient dynamic adjustment capabilities when facing the complex charging needs of multiple users. This results in long waiting times for users or poor utilization of charging resources. Summary of the Invention

[0005] To address the issues of low scheduling efficiency, uneven resource allocation, and insufficient dynamic adjustment capabilities when facing complex charging demands from multiple users, this application provides a method and equipment for managing multi-user shared charging piles.

[0006] This invention provides a method for managing multi-user shared charging piles, comprising: acquiring charging demand data for each user; generating corresponding user charging demands based on the charging demand data; performing dynamic clustering processing on the user charging demands to obtain cluster groups; generating charging scheduling and power distribution plans based on the cluster groups; allocating power distribution loads to the charging scheduling using the power distribution plans to generate an allocation scheme; generating a charging control sequence and preliminary control parameters based on the allocation scheme; optimizing the charging control sequence using the preliminary control parameters to obtain charging control parameters; generating a control scheme based on the charging control parameters; performing performance evaluation on the control scheme to obtain a charging performance evaluation; and optimizing the charging scheduling and the allocation scheme using the charging performance evaluation to obtain a shared charging pile management strategy.

[0007] As a preferred embodiment, the steps of acquiring charging demand data for each user, generating corresponding user charging demands based on the charging demand data, and performing dynamic clustering processing on the user charging demands to obtain cluster groups include: acquiring charging demand data for each user, wherein the charging demand data includes historical charging records, real-time charging requests, and grid load status; generating a charging behavior dataset using the historical charging records and the real-time charging requests, and constructing user charging demands based on the charging behavior dataset; generating a load feature dataset based on the grid load status, and constructing load impact based on the load feature dataset; performing fusion calculation on the user charging demands and the load impact to obtain demand-load correlation information; extracting charging feature vectors based on the demand-load correlation information, and classifying the user charging demands of the real-time charging requests using the charging feature vectors to obtain user classification results; constructing user charging demands based on the user classification results, extracting similarity features from the user charging demands, and using the similarity features to perform secondary segmentation of the user group to obtain cluster groups.

[0008] As a preferred embodiment, the steps of fusing the user charging demand and the load impact to obtain demand-load correlation information, extracting charging feature vectors based on the demand-load correlation information, and classifying the user charging demand of the real-time charging request using the charging feature vectors to obtain user classification results include: performing weighted fusion of the user charging demand and the load impact to obtain demand-load correlation information; extracting features from the demand-load correlation information based on principal component analysis to obtain charging feature vectors; classifying the user charging demand of the real-time charging request using the charging feature vectors to obtain charging type and demand category; and performing clustering processing using the charging type and demand category to obtain user classification results.

[0009] As a preferred embodiment, the steps of generating charging schedules and power distribution plans based on the clustering grouping, allocating power loads to the charging schedule using the power distribution plan, and generating an allocation scheme include: generating a charging task list based on the clustering grouping, constructing charging schedule constraints using the charging task list; calculating user waiting time sequences based on the charging schedule constraints and preset charging task time window waiting data, optimizing charging task priorities using the waiting time sequences to obtain a charging schedule; acquiring current charging pile status information, generating a preliminary power distribution sequence using the charging schedule and the charging pile status information, calculating load distribution data for each charging pile based on the charging schedule and the preliminary power distribution sequence; calculating load balancing parameters based on the load distribution data and current grid load status information, calculating power distribution demand constraint information based on the load distribution data and the load balancing parameters, and generating a power distribution plan based on the power distribution demand constraint information; dynamically allocating loads to the charging schedule using the power distribution plan to obtain load allocation information, and adjusting the power supply strategy of the charging piles based on the load allocation information to obtain an allocation scheme.

[0010] As a preferred embodiment, the steps of calculating load balancing parameters based on the load distribution data and the current power grid load status information, calculating distribution demand constraint information based on the load distribution data and the load balancing parameters, and generating a distribution plan based on the distribution demand constraint information include: calculating a load distribution balance coefficient based on the load distribution data and the current power grid load status information; generating load balancing parameters based on the load distribution coefficient; optimizing the load distribution data using the load balancing parameters to obtain optimized load distribution data; generating distribution demand constraint information based on the optimized load distribution data; and combining the distribution demand constraint information with the charging task information in the charging task list to generate a distribution plan.

[0011] As a preferred embodiment, the steps of generating a charging control sequence and preliminary control parameters based on the allocation scheme, optimizing the charging control sequence using the preliminary control parameters to obtain charging control parameters, and generating a control scheme based on the charging control parameters include: calculating charging pile operating status data based on the allocation scheme, generating a charging control sequence and preliminary control parameters based on the charging pile operating status data, generating current adjustment parameters and voltage adjustment parameters based on the preliminary control parameters, and optimizing the charging control sequence using the current adjustment parameters and voltage adjustment parameters to obtain charging control parameters; calculating real-time charging feedback data based on the charging control parameters, generating a charging status adjustment strategy based on the real-time charging feedback data and the load allocation information, adjusting the charging voltage and current based on the charging status adjustment strategy to obtain an optimized charging control result; updating the charging pile operating status data using the optimized charging control result, calculating the control error based on the updated charging pile operating status data, and optimizing the charging status adjustment strategy using the control error to obtain a control scheme.

[0012] As a preferred embodiment, the steps of evaluating the control scheme to obtain a charging performance evaluation, and optimizing the charging scheduling and allocation scheme using the charging performance evaluation to obtain a shared charging pile management strategy include: calculating charging completion time, energy consumption, and grid load fluctuation data using the control scheme, and generating a charging performance evaluation based on the charging completion time, energy consumption, and grid load fluctuation data; calculating charging stability indicators and user experience indicators through the charging performance evaluation, and generating a charging behavior feature dataset based on the charging stability indicators and the user experience indicators; constructing user feedback through the charging behavior feature dataset, calculating user demand change trends based on the user feedback, and optimizing the charging scheduling and allocation scheme using the user demand change trends to obtain a shared charging pile management strategy.

[0013] This application also provides a multi-user shared charging pile management device, comprising: an acquisition module, configured to acquire charging demand data of each user, generate corresponding user charging demands based on the charging demand data, and perform dynamic clustering processing on the user charging demands to obtain cluster groups; an allocation module, configured to generate charging scheduling and power distribution plans according to the cluster groups, allocate power distribution loads to the charging scheduling using the power distribution plans, and generate an allocation scheme; an analysis module, configured to generate charging control sequences and preliminary control parameters based on the allocation scheme, optimize the charging control sequences using the preliminary control parameters to obtain charging control parameters, and generate a control scheme based on the charging control parameters; and a generation module, configured to perform performance evaluation on the control scheme to obtain a charging performance evaluation, and optimize the charging scheduling and the allocation scheme using the charging performance evaluation to obtain a shared charging pile management strategy.

[0014] Compared with existing technologies, this application has the following advantages: high scheduling efficiency and high flexibility. By acquiring user charging demand data, generating user charging demands, and performing dynamic clustering processing, it can refine the classification of user demands and improve the accuracy of resource allocation; by combining charging scheduling with power distribution planning, it can generate allocation schemes, thereby improving the utilization efficiency of power resources; by analyzing and dynamically adjusting charging control parameters, it can effectively enhance the system's ability to respond to emergencies; by combining performance evaluation with user feedback, iterative optimization of scheduling and load allocation strategies can be performed, enabling the system to continuously improve the management performance of shared charging piles, increase the utilization rate of charging piles, shorten user waiting time, reduce resource waste, and address the problems of low scheduling efficiency, uneven resource allocation, and insufficient dynamic adjustment capabilities that still exist when facing complex charging demands from multiple users. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0017] Figure 1 This is a flowchart illustrating the multi-user shared charging pile management method provided in an embodiment of the present invention; Figure 2 This is a schematic block diagram of the structure of the multi-user shared charging pile management device provided in the embodiment of the present invention.

[0018] Explanation of reference numerals in the attached figures: 10. Multi-user shared charging pile management equipment; 11. Acquisition module; 12. Allocation module; 13. Analysis module; 14. Generation module. Detailed Implementation

[0019] 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, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0021] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0022] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0023] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0024] Example 1: like Figure 1 As shown, this application provides a multi-user shared charging pile management method, including steps S100 to S400.

[0025] Step S100: Obtain charging demand data for each user, generate corresponding user charging demands based on the charging demand data, and perform dynamic clustering processing on the user charging demands to obtain cluster groups.

[0026] In this step, acquiring each user's charging demand data can be achieved in various ways, such as through real-time charging requests submitted by user terminal devices or historical electricity usage records. Specifically, when generating user charging demands using charging demand data, modeling can be performed using dimensions such as power demand, charging duration, and time priority to generate a graphical model that comprehensively reflects the characteristics of user demand. Dynamic clustering of user charging demands is then performed using clustering algorithms, such as K-means or density-based clustering algorithms, to group users with similar charging demands into the same cluster, thus forming cluster groups.

[0027] For example, in a community, if multiple users have a high demand for charging at night and their power needs are similar, dynamic clustering methods can be used to group these users into the same group, which facilitates subsequent scheduling optimization.

[0028] Step S200: Generate charging scheduling and power distribution plans based on clustering and grouping, and use the power distribution plans to allocate power distribution loads for charging scheduling to generate an allocation scheme.

[0029] In this step, when generating charging schedules based on clustering, the charging order of users needs to be arranged according to factors such as user priority, charging time period, and charging pile availability within the clustering group. Specifically, the charging schedule is then used to allocate power distribution loads based on the power distribution capacity constraints in the power distribution plan, generating an allocation scheme that complies with grid load limitations.

[0030] For example, in a power distribution plan, if the upper limit of the power distribution capacity for a certain period is 50 kilowatts, and the total demand of multiple users in a cluster is 70 kilowatts, then the allocation scheme can prioritize meeting the charging needs of high-priority users and arrange some low-priority users to other time periods.

[0031] Step S300: Generate a charging control sequence and preliminary control parameters based on the allocation scheme, optimize the charging control sequence using the preliminary control parameters to obtain the charging control parameters, and generate a control scheme based on the charging control parameters.

[0032] In this step, when generating the charging control sequence and preliminary control parameters using the allocation scheme, it is necessary to generate working instructions for each charging pile according to the scheduling arrangement in the scheme, such as start time and charging power. Specifically, by collecting the operating status of the charging piles and user feedback in real time, the charging control sequence is analyzed using the charging control parameters to dynamically adjust the charging plan and optimize the system control performance.

[0033] For example, when a charging station fails to operate as planned due to an unexpected malfunction, the load can be redistributed through status feedback analysis, and relevant users can be notified to update their charging times, thereby reducing the impact on user services.

[0034] Step S400: Evaluate the performance of the control scheme to obtain a charging performance evaluation. Optimize the charging scheduling and allocation scheme using the charging performance evaluation to obtain a shared charging pile management strategy.

[0035] In this step, performance evaluation generates a charging performance assessment by analyzing indicators such as user waiting time, charging completion rate, and power distribution capacity utilization efficiency. Specifically, user feedback is used to combine user charging behavior with system scheduling strategies to adjust and optimize charging scheduling and allocation schemes, ultimately forming a shared charging pile management strategy.

[0036] For example, when user feedback shows that charging demand is concentrated in certain time periods, the charging schedule can be adjusted and optimized to shift some users' charging schedules to periods with lower load, thereby improving the overall service efficiency of the system.

[0037] In this embodiment, charging demand data for each user is acquired, user charging needs are generated based on this data, and dynamic clustering is performed on these user charging needs to form cluster groups. Next, charging scheduling and power distribution plans are generated based on the cluster groups. The power distribution plans are then used to allocate power load for the charging scheduling, thereby generating an allocation scheme. Then, charging control sequences and charging control parameters are generated based on the allocation scheme. The charging control sequences are optimized using preliminary control parameters to obtain a control scheme. Finally, the control scheme is evaluated to generate a charging performance assessment, and user feedback is generated based on this assessment. User feedback is used to optimize the charging scheduling and allocation scheme, thus forming a shared charging pile management strategy. This effectively improves the resource management capabilities of multi-user shared charging piles. Dynamic clustering can more accurately identify and group user charging demand data, thereby achieving more reasonable resource allocation. By combining charging scheduling and power distribution plans, the power load allocation process is optimized, thereby improving the overall system operating efficiency. By leveraging state feedback analysis and user feedback, management strategies can be adaptively optimized, which not only improves the utilization efficiency of charging piles but also significantly reduces user waiting time, while reducing resource waste. This addresses the problems of low scheduling efficiency, uneven resource allocation, and insufficient dynamic adjustment capabilities that still exist when facing complex charging needs from multiple users.

[0038] Example 2: In step S100, charging demand data for each user is obtained, including historical charging records, real-time charging requests, and grid load status.

[0039] By analyzing users' historical charging records, their charging habits and preferences can be extracted, specifically including typical charging time periods, average charging duration, and charging power demand range. Real-time charging requests can capture users' current instantaneous charging needs, specifically including the location of the requested charging station, remaining battery power, and target charging capacity. Grid load status can monitor the current grid operation, specifically including parameters such as load distribution, voltage fluctuations, and regional power supply capacity. This charging demand data is collected through a unified interface and stored in the data processing module to ensure the accuracy of subsequent analysis.

[0040] For example, a user's historical charging records show that they typically charge between 8:00 PM and 10:00 PM, with an average charging time of 2 hours and a target charge level of 70%. One evening at 9:30 PM, the user submitted a real-time charging request, indicating their battery had 30% remaining and they needed to find a charging station to bring it to 70%. Simultaneously, the system checks the grid load status to determine if there is surplus power capacity in the area during this time period, allowing it to provide the user with a stable charging service.

[0041] A charging behavior dataset is generated using historical charging records and real-time charging requests, and user charging needs are constructed based on the charging behavior dataset.

[0042] By combining historical charging records with real-time charging requests, a charging behavior dataset can be formed. This dataset contains the charging demand characteristics of user groups and their dynamic changes over time. Specifically, data mining techniques are used to extract high-frequency features such as time priority, spatial distribution, and power demand. Through normalization and matrix representation, this data is used to construct user charging demands. The rows of this matrix represent different users, and the columns represent various charging demand characteristics.

[0043] For example, suppose a dataset contains five users whose charging behavior dataset records their respective time period preferences (e.g., peak or off-peak hours), spatial range (home charging station or public charging pile), and power demand (5 kW, 7 kW, or 10 kW). Using this characteristic data, constructing user charging demand can make it easier for the system to analyze the demand patterns of each user at a global level.

[0044] A load characteristic dataset is generated based on the power grid load status, and the load impact is constructed based on the load characteristic dataset.

[0045] By collecting and analyzing the power grid load status in real time, a load characteristic dataset can be generated. This dataset contains information such as the load distribution, peak capacity, and remaining available capacity of the power grid in different time periods and regions. Specifically, a data stratified sampling method is used to model high-frequency fluctuations and low-frequency trends separately, thereby constructing load impact to quantify the dynamic impact of user charging behavior on the power grid load.

[0046] For example, in the power grid monitoring of a certain region, the load characteristic dataset shows that the load reaches 95% of the capacity during the evening peak hours, while the load is only 60% during the off-peak hours. Based on these characteristic data, constructing load impact can provide a scientific basis for the system to judge the feasibility and potential impact of allocating charging tasks at different times.

[0047] The user charging demand and load impact are fused and calculated to obtain demand-load correlation information. Based on the demand-load correlation information, charging feature vectors are extracted, and the user charging demand of real-time charging requests is classified using the charging feature vectors to obtain user classification results.

[0048] By weighted fusion of user charging demand and load impact, demand-load correlation information can be calculated. This matrix comprehensively considers the matching degree between user demand and grid load. Specifically, correlation rules are used to extract the interaction features between the two to generate charging feature vectors, thereby enabling rapid classification of user charging demands. For example, through machine learning algorithms (such as support vector machines or clustering algorithms), users with real-time charging requests can be divided into different categories, such as high-priority users and flexibly scheduled users, in order to optimize resource allocation.

[0049] For example, a user may belong to a high-priority category, whose charging demand is immediate and has a high target capacity, while other users have more flexible needs and can be scheduled during off-peak hours. By classifying demand load correlation information and charging feature vectors, the system can achieve efficient management and coordination of multi-user demands.

[0050] Based on the user classification results, user charging needs are constructed, similarity features are extracted from user charging needs, and user groups are further divided using similarity features to obtain cluster groups.

[0051] By constructing user charging needs based on user classification results, the correlation between user needs can be intuitively displayed. Specifically, graph theory analysis is used to extract similarity features from the graph, and the user groups are further divided using similarity features to obtain more refined clustering groups, which are used for subsequent charging scheduling and resource allocation optimization.

[0052] For example, if certain user needs have a high degree of similarity, such as consistent time periods, similar target battery levels, and similar charging station locations, they can be grouped into a cluster and prioritized for optimization during subsequent scheduling to achieve efficient utilization of charging resources.

[0053] The steps of performing weighted fusion calculations on user charging demand and load impact to obtain demand-load correlation information, extracting charging feature vectors based on demand-load correlation information, and using charging feature vectors to classify user charging demand in real-time charging requests to obtain user classification results include: performing weighted fusion on user charging demand and load impact to obtain demand-load correlation information.

[0054] By weighted fusion of user charging demand and load impact, the fusion results can be refined using their respective data characteristics. Specifically, weight values ​​are assigned based on user charging behavior characteristics in user charging demand and grid operation characteristic parameters in load impact, and matrix operations are used for cross-calculation to generate demand-load correlation information. This demand-load correlation information can comprehensively reflect the degree of correlation between user charging demand and grid load, providing a reliable data foundation for subsequent analysis.

[0055] For example, suppose that the charging demand of a certain user group is concentrated between 18:00 and 22:00 in the evening, and the load impact of the power grid shows that the load capacity is high during this period. By weighted fusion results, it can be identified that the user group's demand load correlation is high during this period, so as to further refine the analysis of the scope of the power grid load impact.

[0056] Principal component analysis is used to extract features from the demand load correlation information to obtain charging feature vectors. These feature vectors are then used to classify user charging demands for real-time charging requests, resulting in charging type and demand category.

[0057] Principal component analysis (PCA) can effectively reduce the dimensionality of demand-load correlation information. Specifically, by utilizing the main data characteristics in the demand-load correlation information, key features that have the greatest impact on charging demand, such as user charging priority and grid load sensitivity, are extracted to generate charging feature vectors. These charging feature vectors can then be used to classify users' real-time charging requests into charging types (fast charging, standard charging, etc.) and demand categories (high priority, low priority, etc.) using classification algorithms (such as KNN or decision trees), thereby completing the classification task.

[0058] For example, a user's real-time charging request might be analyzed as a fast charging request with high priority, while another user's request might be identified as a standard charging request with low priority. By using the charging feature vector classification function, different user needs can be efficiently categorized, providing a reference for subsequent charging resource allocation.

[0059] Clustering is performed using charging type and demand category to obtain user classification results.

[0060] By clustering users based on charging type and demand category, users with similar needs can be grouped together. Specifically, based on the similarity index of charging type and demand category, clustering algorithms (such as K-means or hierarchical clustering) are used to process user data to obtain user classification results. These user classification results can help the system optimize charging scheduling strategies and resource allocation schemes, improving the operational efficiency of multi-user shared charging stations.

[0061] For example, a clustering result might show that one group of users belongs to the fast-charging, high-priority demand group, and charging station resources can be allocated to this group first. Meanwhile, another group of users belongs to the standard-charging, low-priority demand group, and their charging can be scheduled for off-peak hours. Through precise segmentation based on clustering results, more efficient utilization of charging resources can be achieved.

[0062] In step S200, a charging task list is generated based on clustering, and charging scheduling constraints are constructed using the charging task list.

[0063] By generating a charging task list based on clustering, the user charging demand information in each group can be arranged in an orderly manner. Specifically, charging tasks are assigned according to the user's charging priority, target power, charging time period, and charging pile location, generating an independent charging task record for each user. Charging scheduling constraints are then constructed using the generated charging task list as a foundation, combined with parameters such as the charging task's time window, equipment capacity limitations, and grid load constraints, to form a constraint matrix that guides scheduling optimization.

[0064] For example, if users in a certain cluster group charge their devices between 8:00 PM and 10:00 PM, and the target battery level is around 70%, then the start time, end time, and target battery level of each user are recorded in the charging task list; the constructed charging scheduling constraints include the task time window within that period and the available capacity constraints of each charging pile.

[0065] Based on the charging scheduling constraints and the pre-set charging task time window waiting data, the user's waiting time sequence is calculated, and the charging task priority is optimized using the waiting time sequence to obtain the charging schedule.

[0066] By utilizing charging scheduling constraints and user charging task time windows, the estimated waiting time for each user is calculated. Specifically, a waiting time sequence is formed based on the difference between the task start time and the charging pile status. The charging tasks are then prioritized based on these waiting times to minimize the waiting time for high-priority users. Finally, an optimized charging schedule is generated to guide subsequent resource allocation.

[0067] For example, if a user is a high-priority user with a shorter scheduled charging time window, while another user is a low-priority user with a longer time window, then through optimized charging scheduling, charging stations can be allocated to the high-priority user first, ensuring that they can complete charging within the scheduled time.

[0068] Obtain the current status information of charging piles, generate a preliminary power distribution sequence using the charging schedule and charging pile status information, and calculate the load distribution data of each charging pile based on the charging schedule and preliminary power distribution sequence.

[0069] By monitoring the status information of charging piles in real time (including working status, remaining available capacity and operating power), and combining it with optimized charging scheduling, a preliminary power distribution sequence can be generated. Specifically, user tasks are assigned to each charging pile, and its total load data is calculated. Based on the allocation results, the load distribution data of each charging pile is further refined, laying the foundation for subsequent load balancing optimization.

[0070] For example, if a charging station has been assigned three charging tasks within a certain period of time, with a total load of 15 kilowatts, its load distribution data can be further refined to the actual power demand and allocation strategy in each time period.

[0071] Load balancing parameters are calculated based on load distribution data and current power grid load status information. Distribution demand constraint information is calculated based on load distribution data and load balancing parameters. Distribution plan is generated based on distribution demand constraint information.

[0072] By comparing and analyzing load distribution data with real-time grid load status, the deviation between the current load and the target load can be quantified. Specifically, load balancing parameters are calculated using relevant models. These parameters are then used to constrain power distribution demand, forming power distribution demand constraint information. Finally, an optimized power distribution plan is generated, providing a basis for subsequent dynamic load allocation.

[0073] For example, if the current grid load in a certain area is close to its peak capacity, the new power distribution tasks in this area can be appropriately limited by calculating load balancing parameters; the power distribution plan optimizes the power allocation for charging tasks while ensuring grid stability.

[0074] By using the power distribution plan to dynamically allocate the load for charging scheduling, load allocation information is obtained, and the power supply strategy of the charging piles is adjusted based on the load allocation information to obtain the allocation scheme.

[0075] By dynamically allocating tasks in charging scheduling through power distribution planning, the grid load can be balanced while meeting users' charging needs. Specifically, the load allocation results are recorded as load allocation information. This matrix describes in detail the power usage of each charging pile in a specific time period. By combining the load allocation information with the power supply strategy of the charging piles, the overall allocation scheme can be optimized by dynamically adjusting the charging power and time sequence.

[0076] For example, if three charging piles in a certain area are assigned to different users, and the power distribution plan shows that the charging power of two high-priority users needs to be prioritized, then the load allocation information records the specific allocation data; ultimately, by optimizing the power supply strategy, the system can be made efficient while maintaining stable power supply.

[0077] The steps of calculating load balancing parameters based on load distribution data and current power grid load status information, calculating distribution demand constraint information based on load distribution data and load balancing parameters, and generating a distribution plan based on distribution demand constraint information include: calculating the load distribution balance coefficient based on load distribution data and current power grid load status information, and generating load balancing parameters based on the load distribution balance coefficient.

[0078] By statistically modeling load distribution data and the current power grid load status, the load distribution balance coefficient is calculated to quantify the load balance status. Specifically, the load balance of each region is assessed according to the power grid operation standards, and the overall load balance parameters are generated through a weighted method, laying the foundation for optimizing power distribution.

[0079] For example, if the load distribution in a certain area shows multiple peak and off-peak periods, the pressure distribution of the power grid at different times can be obtained through quantitative analysis of the balance coefficient, and the load balancing parameters can be adjusted accordingly.

[0080] The load distribution data is optimized using load balancing parameters to obtain optimized load distribution data.

[0081] By optimizing load distribution data using load balancing parameters, load distribution fluctuations can be smoothed out. Specifically, linear programming algorithms are used to redistribute tasks during high-load periods, reduce peak load, ensure stable grid operation, and generate optimized load distribution data.

[0082] For example, if load distribution data shows that the load is too high during a certain period, after optimization, some charging tasks can be rescheduled to off-peak periods. The optimization results show that the load distribution is more balanced, effectively reducing the pressure on the power grid.

[0083] Based on the load distribution optimization data, power distribution demand constraint information is generated. This power distribution demand constraint information is then combined with the charging task information in the charging task list to generate a power distribution plan.

[0084] When calculating the power grid load allocation strategy using load distribution optimization data, it is necessary to comprehensively consider the compatibility between power grid capacity and user demand. Specifically, the optimal power grid allocation scheme is generated using optimization algorithms, thereby generating power distribution demand constraint information. Combined with the charging task information in the charging task list, the power distribution demand is matched with the user tasks one by one, and finally an optimized power distribution plan is generated.

[0085] For example, the calculation results of the power grid load allocation strategy in a certain area show that charging power needs to be limited during the evening peak hours and can be appropriately increased during the off-peak hours. By combining user needs, the power distribution plan can optimize the overall allocation without affecting the user experience.

[0086] In step S300, the charging pile working status data is calculated based on the allocation scheme, and a charging control sequence and preliminary control parameters are generated based on the charging pile working status data. Current adjustment parameters and voltage adjustment parameters are generated based on the preliminary control parameters, and the charging control sequence is optimized using the current adjustment parameters and voltage adjustment parameters to obtain the charging control parameters.

[0087] By analyzing the allocation scheme, load data for each charging pile within a specified time period can be extracted, thereby calculating the charging pile's operating status data, including current operating power, remaining load capacity, and charging completion time. Based on the charging pile's operating status data, a charging control sequence is generated to plan the execution tasks for each charging pile, and the basic ranges of charging current and voltage are set through preliminary control parameters. Subsequently, the preliminary control parameters are further optimized using the matching degree between the charging pile load and user demand characteristics, generating current adjustment parameters and voltage adjustment parameters. Finally, the charging control sequence is optimized to precisely control the operating status of each charging pile, resulting in complete charging control parameters.

[0088] For example, if the operating status data of a charging pile shows that its current remaining capacity is 5 kW, and it is expected to provide 3 kW of charging power to a target user, then the charging control sequence plans the start time and operating power of the charging task. Based on this task, the initial control parameters are set with a current range of 15-20 amps, and through optimized calculations, the current adjustment parameter is set to 18 amps and the voltage adjustment parameter to 220 volts, thereby ensuring the stability and efficiency of the charging task.

[0089] Real-time charging feedback data is calculated based on charging control parameters. A charging state adjustment strategy is generated based on the real-time charging feedback data and load distribution information. The charging voltage and current are adjusted based on the charging state adjustment strategy to obtain optimized charging control results.

[0090] By monitoring the actual operating data of charging piles, the system calculates the changes in current, voltage, and power during the charging process in real time and uses this data as real-time charging feedback data. Combining this feedback data with user demands and assigned tasks recorded in the load allocation information, a charging status adjustment strategy is generated. Specifically, the voltage and current parameters of the charging piles are adjusted based on deviations in the feedback data to cope with unexpected situations, such as temporary increases in user charging demand or grid fluctuations. Through this adjustment process, optimized charging control results are obtained, effectively improving the system's responsiveness and stability.

[0091] For example, if a user temporarily adjusts their target power demand to 120% of the initial charging plan, and the real-time charging feedback data shows that the current current does not meet the demand, the charging voltage is adjusted to 240 volts and the current to 22 amps through the charging status adjustment strategy to ensure that the user completes the charging task within the scheduled time.

[0092] The optimized charging control results are used to update the charging pile operating status data, and the control error is calculated based on the updated charging pile operating status data. The control error is then used to optimize the charging status adjustment strategy to obtain the control scheme.

[0093] The optimized charging control results are used to update the charging pile's operating status data in real time, including parameters such as its current load capacity, actual output power, and remaining running time. This data is then compared with the initial plan, and the control error is calculated to quantify the deviation between the actual execution and the target task. Specifically, the control error is used to correct the charging status adjustment strategy, ensuring the accuracy and reliability of the strategy, and iteratively generating a more precise control scheme, thereby improving overall performance.

[0094] For example, if the actual operating power of a charging pile is 5% lower than the set power during a charging task, the original adjustment strategy was found to have failed to fully match the load demand through the calculation of control error. The optimized control scheme adds a dynamic response mechanism, which improves the power supply accuracy of subsequent tasks and further reduces power waste and resource consumption during the charging process.

[0095] In step S400, the charging completion time, energy consumption, and grid load fluctuation data are calculated using the control scheme, and a charging performance evaluation is generated based on the charging completion time, energy consumption, and grid load fluctuation data.

[0096] By monitoring key parameters during the charging process in real time, such as charging current, voltage changes, and running time, specifically, the charging pile's data recording module calculates the charging completion time for each user; energy consumption is calculated by integrating voltage and current data, and grid load fluctuations are calculated based on the difference in grid power output at different times. Based on this core data, a charging performance assessment is generated, including charging efficiency, energy utilization rate, and grid load balance, to evaluate the overall operational effectiveness of the charging system.

[0097] For example, a charging station completed charging for three users during a peak evening period, with each user's actual charging time being 40 minutes, 60 minutes, and 80 minutes respectively; energy consumption data recorded as 8 kWh, 10 kWh, and 12 kWh per user; grid load fluctuation parameters showed that the load gradually decreased from 90% to 60% during this period. This data ultimately formed a charging performance assessment, providing data support for system optimization.

[0098] Charging stability and user experience metrics are calculated through charging performance evaluation, and a charging behavior feature dataset is generated based on these metrics.

[0099] By comprehensively analyzing charging performance, specifically, a charging stability index is calculated using the standard deviation of charging completion time to reflect the system's time consistency in handling different user needs; and a user experience index is calculated by analyzing user waiting time and charging efficiency to assess the convenience and satisfaction users experience. These indicators are further organized into a charging behavior feature dataset, which includes information such as user behavior patterns, the operating characteristics of the charging system, and the time-of-day distribution of grid pressure.

[0100] For example, the charging stability index results for a certain area show that the standard deviation of user charging completion time is 5 minutes, indicating that the system has good response consistency during this period; the user experience index shows that the average waiting time has been shortened to less than 10 minutes, and the charging efficiency has been improved to 95%. These data are integrated into the charging behavior feature dataset for easy use in subsequent system optimization.

[0101] By constructing user feedback through a charging behavior feature dataset, calculating the trend of user demand changes based on user feedback, and optimizing charging scheduling and allocation schemes using the trend of user demand changes, a shared charging pile management strategy is obtained.

[0102] By extracting user charging preferences and usage patterns from a charging behavior feature dataset, a user feedback model is constructed. Specifically, rows in the matrix represent users, and columns represent their behavioral characteristics, such as usage frequency, preferred charging time periods, and target power demand. Combined with user feedback, time series analysis is used to calculate the dynamic trends of user demand and predict peak usage periods and demand fluctuations. These prediction results are then used to iteratively optimize charging scheduling and allocation schemes, achieving dynamic and efficient management of charging pile resources.

[0103] For example, user feedback from a community showed that a group of users charged more frequently on weekends than on weekdays, with peak hours concentrated between 7:00 PM and 9:00 PM. Based on demand trend predictions, the system adjusted the charging schedule, prioritizing the allocation of additional charging pile resources to this time period, and rescheduling low-priority users to complete charging during off-peak hours, ultimately generating a more efficient shared charging pile management strategy.

[0104] In this implementation, by acquiring each user's charging demand data and combining it with historical charging records, real-time charging requests, and grid load status, the system can construct user charging demand and load impact, and generate demand-load correlation information through weighted fusion. Key charging feature vectors are extracted through principal component analysis to classify user demands and construct user charging requirements. User groups are further segmented using graph similarity features to obtain optimized clustering groups. A charging task list is generated based on the clustering groups, and charging scheduling constraints are constructed. Charging schedules are generated through waiting time series and priority optimization. Combined with real-time charging pile status information and load distribution data, a power distribution plan and allocation scheme are generated to ensure reasonable resource allocation and load balance. The system also calculates and generates charging control parameters through dynamic optimization of charging control sequences and current and voltage regulation parameters, achieving precise control and status adjustment. During this process, real-time charging feedback data further optimizes the control scheme, effectively improving the system's responsiveness and operational stability. Furthermore, by analyzing charging completion time, energy consumption, and grid load fluctuation data, the system generates evaluation parameters and, combined with user feedback and demand change trends, optimizes the charging scheduling and allocation scheme, forming a shared charging pile management strategy. This embodiment significantly improves the resource utilization efficiency of charging piles, user experience, and grid load management capabilities, and comprehensively optimizes the management performance of multi-user shared charging piles.

[0105] Example 3: like Figure 2 As shown, this application also provides a multi-user shared charging pile management device 10, including an acquisition module 11, an allocation module 12, an analysis module 13, and a generation module 14.

[0106] The acquisition module 11 is mainly used to acquire the charging demand data of each user, generate corresponding user charging demands based on the charging demand data, and perform dynamic clustering processing on the user charging demands to obtain cluster groups.

[0107] The allocation module 12 is mainly used to generate charging scheduling and power distribution plans based on clustering and grouping, and to use the power distribution plan to allocate power distribution loads to the charging scheduling and generate allocation schemes.

[0108] Analysis module 13 is mainly used to generate charging control sequence and preliminary control parameters based on the allocation scheme, optimize the charging control sequence using the preliminary control parameters to obtain charging control parameters, and generate a control scheme based on the charging control parameters.

[0109] The generation module 14 is mainly used to evaluate the performance of the control scheme, obtain the charging performance evaluation, optimize the charging scheduling and allocation scheme using the charging performance evaluation, and obtain the shared charging pile management strategy.

[0110] In this embodiment, efficient management of multi-user shared charging piles is achieved through multi-module collaboration. The acquisition module 11 collects charging demand data from each user, constructs user charging needs, and generates cluster groups using dynamic clustering methods, thereby accurately grouping user charging needs. The allocation module 12 generates charging scheduling and power distribution plans based on the cluster groups, and allocates power load through the power distribution plans to form an allocation scheme, ensuring reasonable allocation of charging resources and dynamic balance of grid load. The analysis module 13 generates charging control sequences and charging control parameters based on the allocation scheme, and dynamically optimizes the charging control scheme through state feedback analysis of the charging control sequences, ensuring the stability and responsiveness of charging tasks. The generation module 14 performs performance evaluation on the control scheme, analyzes charging completion time, energy consumption, and grid load fluctuations, generates a charging performance evaluation, and further constructs user feedback. Based on the feedback matrix, it optimizes the charging scheduling and allocation scheme, ultimately obtaining an iteratively optimized shared charging pile management strategy. Through the equipment in this embodiment, the utilization efficiency and management level of charging pile resources can be significantly improved, while reducing user waiting time, improving user experience, and effectively reducing grid load fluctuations, achieving continuous optimization of system performance.

[0111] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0112] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for managing multi-user shared charging piles, characterized in that, include: Obtain charging demand data for each user, generate corresponding user charging demands based on the charging demand data, and perform dynamic clustering processing on the user charging demands to obtain cluster groups; Based on the clustering grouping, a charging schedule and power distribution plan are generated. The power distribution plan is used to allocate power load for the charging schedule and generate an allocation scheme. Based on the allocation scheme, a charging control sequence and preliminary control parameters are generated. The charging control sequence is then optimized using the preliminary control parameters to obtain the charging control parameters. Finally, a control scheme is generated based on the charging control parameters. The control scheme is evaluated to obtain a charging performance evaluation. The charging performance evaluation is then used to optimize the charging scheduling and allocation scheme to obtain a shared charging pile management strategy.

2. The multi-user shared charging pile management method according to claim 1, characterized in that, The steps of acquiring charging demand data for each user, generating corresponding user charging demands based on the charging demand data, and dynamically clustering the user charging demands to obtain cluster groups include: Acquire charging demand data for each user, wherein the charging demand data includes historical charging records, real-time charging requests, and grid load status; A charging behavior dataset is generated using the historical charging records and the real-time charging requests, and user charging needs are constructed based on the charging behavior dataset. A load feature dataset is generated based on the power grid load status, and the load impact is constructed based on the load feature dataset; The user charging demand and the load impact are fused and calculated to obtain demand-load correlation information. Based on the demand-load correlation information, a charging feature vector is extracted, and the user charging demand of the real-time charging request is classified using the charging feature vector to obtain the user classification result. Based on the user classification results, user charging needs are constructed, similarity features are extracted from the user charging needs, and the user groups are further divided using the similarity features to obtain cluster groups.

3. The multi-user shared charging pile management method according to claim 2, characterized in that, The steps of fusing the user's charging demand with the load impact to obtain demand-load correlation information, extracting charging feature vectors based on the demand-load correlation information, and using the charging feature vectors to classify the user's charging demand in the real-time charging request to obtain the user classification result include: The user charging demand and load impact are weighted and fused to obtain demand-load correlation information; Based on principal component analysis, feature extraction is performed on the demand load correlation information to obtain a charging feature vector. The charging feature vector is then used to classify the user charging demand of the real-time charging request to obtain the charging type and demand category. Clustering is performed using the charging type and the demand category to obtain user classification results.

4. The multi-user shared charging pile management method according to claim 1, characterized in that, The steps of generating charging schedules and power distribution plans based on the clustering grouping, and using the power distribution plans to allocate power loads to the charging schedules to generate an allocation scheme include: A charging task list is generated based on the clustering grouping, and charging scheduling constraints are constructed using the charging task list. The user's waiting time sequence is calculated based on the charging scheduling constraints and the preset charging task time window waiting data. The charging task priority is optimized using the waiting time sequence to obtain the charging schedule. Obtain the current charging pile status information, generate a preliminary power distribution sequence using the charging schedule and the charging pile status information, and calculate the load distribution data of each charging pile based on the charging schedule and the preliminary power distribution sequence; Based on the load distribution data and the current power grid load status information, load balancing parameters are calculated; based on the load distribution data and the load balancing parameters, distribution demand constraint information is calculated; and based on the distribution demand constraint information, a distribution plan is generated. The power distribution plan is used to dynamically allocate the load of the charging schedule to obtain load allocation information, and the power supply strategy of the charging pile is adjusted based on the load allocation information to obtain an allocation scheme.

5. The multi-user shared charging pile management method according to claim 4, characterized in that, The steps of calculating load balancing parameters based on the load distribution data and the current power grid load status information, calculating distribution demand constraint information based on the load distribution data and the load balancing parameters, and generating a distribution plan based on the distribution demand constraint information include: Calculate the load distribution balance coefficient based on the load distribution data and the current power grid load status information, and generate load balancing parameters based on the load distribution balance coefficient; The load distribution data is optimized using the load balancing parameters to obtain optimized load distribution data; Based on the load distribution optimization data, power distribution demand constraint information is generated. The power distribution demand constraint information is then combined with the charging task information in the charging task list to generate a power distribution plan.

6. The multi-user shared charging pile management method according to claim 4, characterized in that, The steps of generating a charging control sequence and preliminary control parameters based on the allocation scheme, optimizing the charging control sequence using the preliminary control parameters to obtain charging control parameters, and generating a control scheme based on the charging control parameters include: The charging pile working status data is calculated based on the allocation scheme, and a charging control sequence and preliminary control parameters are generated based on the charging pile working status data. Current adjustment parameters and voltage adjustment parameters are generated based on the preliminary control parameters, and the charging control sequence is optimized using the current adjustment parameters and voltage adjustment parameters to obtain the charging control parameters. Real-time charging feedback data is calculated based on the charging control parameters. A charging state adjustment strategy is generated based on the real-time charging feedback data and the load allocation information. The charging voltage and current are adjusted based on the charging state adjustment strategy to obtain an optimized charging control result. The optimized charging control results are used to update the charging pile's operating status data, and the control error is calculated based on the updated charging pile operating status data. The control error is then used to optimize the charging status adjustment strategy to obtain a control scheme.

7. The multi-user shared charging pile management method according to claim 1, characterized in that, The steps of evaluating the control scheme to obtain a charging performance evaluation, and using the charging performance evaluation to optimize the charging scheduling and allocation scheme to obtain a shared charging pile management strategy include: The control scheme is used to calculate charging completion time, energy consumption, and grid load fluctuation data, and a charging performance evaluation is generated based on the charging completion time, energy consumption, and grid load fluctuation data. The charging performance evaluation calculates charging stability indicators and user experience indicators, and generates a charging behavior feature dataset based on the charging stability indicators and user experience indicators. User feedback is constructed using the charging behavior feature dataset, and the trend of user demand changes is calculated based on the user feedback. The charging scheduling and allocation scheme are then optimized using the trend of user demand changes to obtain a shared charging pile management strategy.

8. A multi-user shared charging pile management device, characterized in that, include: The acquisition module is used to acquire charging demand data for each user, generate corresponding user charging demands based on the charging demand data, and perform dynamic clustering processing on the user charging demands to obtain cluster groups. The allocation module is used to generate charging schedules and power distribution plans based on the clustering grouping, allocate power distribution loads to the charging schedules using the power distribution plans, and generate an allocation scheme. The analysis module is used to generate a charging control sequence and preliminary control parameters based on the allocation scheme, optimize the charging control sequence using the preliminary control parameters to obtain charging control parameters, and generate a control scheme based on the charging control parameters. The generation module is used to evaluate the performance of the control scheme, obtain a charging performance evaluation, and use the charging performance evaluation to optimize the charging scheduling and the allocation scheme to obtain a shared charging pile management strategy.

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