New energy automobile charging load balancing scheduling method

Abstract

The invention provides a new energy automobile charging load balancing scheduling method. The method comprises the steps that load data and user demand information are collected in real time through an intelligent sensor network, a comprehensive data set is constructed, real-time state mapping data is generated, peak overload and valley idle trends are analyzed through a prediction model, and a load distribution prediction result is determined; based on a two-way communication mechanism, user charging preferences are obtained, charging suggestions are pushed, a preliminary resource allocation scheme is generated, a genetic algorithm is applied to optimize power distribution, overload risks are minimized, and the resource utilization rate is maximized; load distribution stability is evaluated through a virtual simulation test, and an optimization scheme is iterated; load fluctuation is monitored in real time, operation parameters are dynamically updated, a control instruction is issued, a standby scheduling mechanism is triggered according to user feedback, and service distribution is continuously optimized. According to the invention, the intelligent management of the charging load of the electric vehicle is realized, the power grid resource configuration is effectively balanced, and the user satisfaction and the charging efficiency are improved.

Description

Technical Field

[0001] The invention relates to a new energy vehicle charging load balancing scheduling method. Background Art

[0002] With the rapid growth in the number of electric vehicles, the rational scheduling and management of charging facilities is not only a technical issue but also a critical component in ensuring energy security and social benefits. However, many current charging load management methods still have significant shortcomings. Traditional management methods often rely on static planning and lack the ability to adapt to real-time changes, making it difficult to cope with fluctuations in grid load and dynamic changes in user demand. This leads to overloading of charging facilities during peak hours and idle resources during off-peak hours, resulting in overall inefficiency.

[0003] The core challenges facing this field stem from several interrelated technical difficulties. The lack of an effective two-way communication mechanism between charging facilities and electric vehicles prevents the system from timely obtaining information on vehicle status and user needs, making it difficult to achieve accurate charging scheduling. Due to the lack of real-time data support, the system is unable to accurately predict changing trends in load distribution when faced with complex scenarios, further exacerbating the imbalance in resource allocation. More fundamentally, due to the lack of effective virtual simulation methods to test and optimize scheduling strategies, actual operations often require high trial and error costs, affecting the scientific nature and reliability of management. Summary of the Invention

[0004] The present invention proposes a new energy vehicle charging load balancing scheduling method. Based on an intelligent scheduling system using two-way communication and virtual simulation technology, it achieves dynamic balancing of charging loads and efficient utilization of resources, effectively balancing grid resource allocation.

[0005] The technical solution of the present invention is achieved as follows: A new energy vehicle charging load balancing scheduling method, the method comprising: Step 1: Deploy an intelligent sensor network to collect real-time load data and user demand information from electric vehicles and charging facilities. This data is then integrated to manage load fluctuations and dynamically change user demand. This data sets a comprehensive dataset containing charging power, time distribution, and vehicle status. This data generates real-time state mapping data of load fluctuations and demand changes for subsequent predictive analysis. Step 2: Based on the real-time state mapping data of load fluctuations and demand changes generated in Step 1, a pre-established forecasting model is applied to analyze trends of overload during peak hours and idleness during off-peak hours. Time series analysis is used to process the comprehensive data set to determine the load distribution forecast for the future period. This forecast is used to make decisions on resource allocation and user guidance. Step 3: Based on the load distribution prediction results determined in Step 2, an interactive system based on a two-way communication mechanism is constructed. The system obtains the user's charging preferences and vehicle power status from the electric vehicle through mobile terminals or on-board devices, and pushes charging time recommendations to the user. If the prediction results show that the overload risk during peak hours is higher than the preset threshold, the user is given priority to choose off-peak hours for charging. This generates a preliminary resource allocation balance plan for subsequent power allocation optimization. Step 4: Based on the preliminary resource allocation balance plan generated in Step 3, combined with the dynamic scheduling strategy, a genetic algorithm is applied to optimize the power distribution of charging facilities. The optimization goal is to minimize the overload risk during peak hours and maximize the resource utilization during off-peak hours. The load distribution ratio of each facility is adjusted to generate an optimized power distribution matrix for subsequent simulation tests. Step 5: Using virtual simulation testing technology, simulate the load distribution effect in a grid simulation model built based on actual grid parameters for the optimized power distribution matrix generated in Step 4. Analyze the voltage fluctuations and current overloads that may occur during the simulation process, and generate load distribution stability assessment data for subsequent solution adjustments. Step 6: Based on the load distribution stability assessment data generated in step 5, if the voltage fluctuation or current overload index in the assessment data exceeds the preset threshold, the power distribution matrix is ​​adjusted again, and the load distribution plan is re-optimized through iterative calculation to generate a final load distribution plan that meets the grid stability requirements for real-time monitoring and execution; Step 7: Based on the final load distribution plan generated in Step 6, the real-time data acquisition system continuously monitors the load fluctuation management effect. Based on the dynamic changes in user demand and the resource allocation balance goal, the operating parameters of the charging facilities are dynamically updated, including the power upper limit and time scheduling strategy. The real-time adjusted load management instructions are generated for the issuance of power control signals. Step 8: Based on the real-time adjusted load management instructions generated in Step 7, specific power control signals are issued to charging facilities to optimize user experience and ensure the continuity and responsiveness of charging services during each period. If the user satisfaction feedback through the interactive system falls below a preset threshold, the backup scheduling mechanism is triggered to prioritize the allocation of backup charging resources and generate an improved service allocation plan for long-term optimization and adjustment. Step 9: For the improved service allocation plan generated in Step 8, combine the feedback loop of user satisfaction and load data, and use the feedback data to update the parameters of the prediction model and the rules of the scheduling strategy to ensure that the load management instructions continue to match the dynamic changes in user demand, and generate a long-term load management optimization path for continuous system improvement and resource balancing.

[0006] The beneficial effects of the present invention are as follows: real-time collection of load data and user demand information through an intelligent sensor network, construction of a comprehensive data set and generation of real-time status mapping data, use of a predictive model to analyze peak overload and valley idle trends, and determination of load distribution prediction results; based on a two-way communication mechanism, obtaining user charging preferences and delivering charging recommendations, generating a preliminary resource allocation plan, and applying a genetic algorithm to optimize power distribution, minimizing overload risks and maximizing resource utilization; evaluating load distribution stability through virtual simulation testing and iterating optimization plans; real-time monitoring of load fluctuations, dynamic updating of operating parameters and issuing control instructions, triggering a backup scheduling mechanism based on user feedback, and continuously optimizing service distribution. The present invention achieves intelligent management of electric vehicle charging loads, effectively balancing grid resource allocation, and improving user satisfaction and charging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0008] Figure 1 It is a flow control diagram of the present invention. DETAILED DESCRIPTION

[0009] The following will clearly and completely describe the technical solutions of the present invention in conjunction with the embodiments of the present invention. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0010] Reference Figure 1 A method for balancing and scheduling charging loads of new energy vehicles, specifically comprising: Step 1: Deploy an intelligent sensor network to collect real-time load data and user demand information from electric vehicles and charging facilities. Integrate this data to manage load fluctuations and dynamically change user demand. Build a comprehensive dataset that includes charging power, time distribution, and vehicle status. Generate real-time status mapping data of load fluctuations and demand changes for subsequent predictive analysis.

[0011] Through the deployed sensor network, load data and user demand information are obtained from electric vehicles and charging facilities. The load data and user demand are initially integrated to generate an initial data set containing charging power and time distribution, obtaining a comprehensive record for subsequent processing. Based on the initial data set, a data cleaning tool is used to organize the vehicle status information. If the load data is detected to exceed the preset threshold, the time distribution is marked to determine the critical time period for load fluctuations. By correlating the marked time distribution with the vehicle status, a data mapping tool is used to establish a correspondence between the real-time status and the load fluctuations, generating mapping data, and determining the dynamic trend of demand changes. Based on the mapping data, a time series processing tool is used to extract features from the load fluctuations and demand changes, obtain feature vectors for predictive analysis, and obtain basic predictive data for subsequent applications.

[0012] Step 2: Based on the real-time status mapping data of load fluctuations and demand changes generated in Step 1, apply the pre-established forecasting model to analyze the trends of overload during peak hours and idleness during off-peak hours. Use time series analysis methods to process the comprehensive data set and determine the load distribution forecast results for a period of time in the future. This is used for decision-making on resource allocation and user guidance.

[0013] Based on the real-time state mapping data of load fluctuations and demand changes, a time series decomposition tool is used to process the comprehensive data set to separate trend components, periodic components, and random components. The characteristics of overload during peak hours and idleness during off-peak hours are classified and labeled to obtain a classified load feature data set, and the direction of load fluctuation trends in the future period is determined. The characteristic values ​​of peak hours and off-peak hours are extracted from the classified load feature data set, and the characteristic values ​​are smoothed using a sliding window tool to obtain a smoothed load fluctuation curve. It is determined whether there are abnormal fluctuation points exceeding a preset threshold in the load fluctuation curve. If the preset threshold is exceeded, the abnormal points are marked to obtain the marked load fluctuation distribution results. Based on the marked load fluctuation distribution results, a regression prediction tool is used to fit and calculate the load distribution in the future period to obtain fitted load distribution prediction data. The overload risk during peak hours and the idle risk during off-peak hours in the prediction data are determined, and a risk level distribution map is determined. According to the risk level distribution map, a resource scheduling simulation tool is used to simulate the distribution of the predicted load, obtain resource utilization data after the simulated allocation, and dynamically adjust the areas where the utilization is lower than the preset threshold to obtain an adjusted resource allocation plan.

[0014] Step 3: Based on the load distribution prediction results determined in Step 2, an interactive system based on a two-way communication mechanism is constructed. The user's charging preferences and vehicle power status are obtained from the electric vehicle through mobile terminals or on-board devices, and charging time recommendations are pushed to the user. If the prediction results show that the overload risk during peak hours is higher than the preset threshold, the user is given priority to choose to charge during off-peak hours, and a preliminary resource allocation balance plan is generated for subsequent power distribution optimization.

[0015] User preferences and vehicle power information are obtained from mobile terminals or onboard devices. Real-time data is collected through a pre-established communication interface, and the data is format-checked to obtain a standardized user input data set. Based on the user input data set combined with the load distribution prediction results, a determination is made as to whether the overload risk during peak hours is higher than a preset threshold. If it is higher than the preset threshold, a charging time recommendation is generated to guide the user to the off-peak period, and a preliminary time allocation plan is determined. Based on the preliminary time allocation plan, charging time recommendations are pushed to the user through a two-way communication mechanism, and user feedback is obtained to obtain an adjusted confirmation data set. Based on the confirmation data set and combined with resource allocation requirements, a secondary matching of the charging time and power distribution is performed. The matching results are stored using a general database tool to generate a final balancing plan.

[0016] Step 4: Based on the preliminary resource allocation balance plan generated in step 3, combined with the dynamic scheduling strategy, the genetic algorithm is applied to optimize the power distribution of charging facilities, with the optimization goals of minimizing the overload risk during peak hours and maximizing the resource utilization during off-peak hours. The load distribution ratio of each facility is adjusted to generate the optimized power distribution matrix for subsequent simulation tests.

[0017] Based on a pre-established resource allocation plan, the load ratio information for each charging facility during peak and off-peak periods is obtained from dynamic scheduling data. A genetic algorithm is used to adjust the initial power allocation based on the peak overload risk and off-peak utilization targets, resulting in a first-version power allocation matrix. By analyzing the first-version power allocation matrix, the load distribution data for each facility during different time periods is obtained. If the load ratio of a facility during peak hours exceeds a preset threshold, the power allocation for that facility is adjusted again to determine a second-version power allocation matrix. For this second-version power allocation matrix, information on underutilized facility loads is extracted from off-peak resource utilization data. Data comparison tools are used to optimize the power allocation during these off-peak periods, resulting in a third-version power allocation matrix. Based on this third-version power allocation matrix and in combination with risk control requirements, simulation testing tools are used to verify the adjusted power allocation to determine whether it meets the optimization objectives, generating the final power allocation matrix.

[0018] Step 5: For the optimized power distribution matrix generated in Step 4, use virtual simulation testing technology to simulate the load distribution effect in the power grid simulation model built based on actual power grid parameters, analyze the voltage fluctuations and current overload phenomena that may occur during the simulation process, and generate load distribution stability evaluation data for subsequent solution adjustments.

[0019] Based on a pre-established grid parameter database, actual grid data for the target area is obtained. A virtual simulation environment is constructed for this grid data, and the optimized power distribution matrix is ​​loaded into this environment. This simulation is then run through a simulation test tool to obtain preliminary simulation data for load distribution. Based on this preliminary simulation data, a voltage fluctuation detection tool is used to monitor the voltage values ​​of each node in real time. If the node voltage value exceeds a preset threshold range, the node fluctuation information is recorded. The current overload detection tool is then used to determine whether an overload is present and to determine the distribution details of abnormal nodes. Based on the distribution details of these abnormal nodes, the specific data required for load stability assessment is obtained. A data processing tool is then used to classify and compile statistics on the frequency and impact range of the voltage fluctuations and current overloads to obtain quantitative indicators of load distribution stability. Based on these quantitative indicators, a data visualization tool is used to generate distribution effect diagrams for areas with low load stability. Combined with historical records in the solution adjustment database, the power distribution parameter range that needs to be optimized is determined, and reference data for adjustment is output.

[0020] Step 6: Based on the load distribution stability evaluation data generated in step 5, if the voltage fluctuation or current overload index in the evaluation data exceeds the preset threshold, the power distribution matrix is ​​adjusted secondary, and the load distribution plan is re-optimized through iterative calculation to generate a final load distribution plan that meets the grid stability requirements for real-time monitoring and execution.

[0021] Extract voltage fluctuations and current overload indicators that exceed the preset threshold from the stability assessment results, generate a preliminary adjustment requirement list for the abnormal indicators, use a data comparison tool to determine the specific distribution location of the abnormal indicators, and obtain the node information of the abnormal load. According to the node information of the abnormal load, obtain the corresponding distribution parameters in the power matrix, use a matrix operation tool to make a secondary adjustment for the distribution parameters, and determine the preliminary optimization results of the adjusted power matrix through simulation operations. If there are still voltage fluctuations or current overload indicators exceeding the preset threshold in the preliminary optimization results, start the iterative calculation process, repeatedly adjust the distribution ratio of the power matrix through the cyclic comparison tool, and obtain an intermediate optimization solution that meets the grid stability requirements. Generate a final load distribution plan based on the intermediate optimization plan, use a real-time monitoring tool to collect data on the execution process of the final load distribution plan, record the dynamic changes during the execution process, and determine whether the continuity requirements of grid stability are met.

[0022] Step 7: Based on the final load distribution plan generated in step 6, the real-time data acquisition system is used to continuously monitor the load fluctuation management effect. Based on the dynamic changes in user demand and the resource allocation balance target, the operating parameters of the charging facilities are dynamically updated, including the power upper limit and time scheduling strategy, and real-time adjusted load management instructions are generated for the issuance of power control signals.

[0023] Based on load fluctuations, the system obtains charging facility operating data from a real-time monitoring interface. Based on changes in user demand reflected in the operating data, it determines whether the load exceeds a preset threshold. If so, it generates a preliminary adjustment signal to determine the load anomaly. Based on the preliminary adjustment signal and the balance objective of resource allocation, the system analyzes the operating parameters of the charging facility, applies pre-established power allocation rules, determines the adjustment range of the power upper limit, and obtains the adjusted power parameter value.

[0024] Indicates the power adjustment amount, Indicates the maximum allowable adjustment range. represents the adjustment coefficient, represents the predicted signal value, Indicates the current signal value, Indicates the maximum value of the signal, Indicates the final power parameter value, Indicates the adjusted power value, represents the lower power limit constraint, Represents a power upper limit constraint. Based on the adjusted power parameter value, a new time scheduling scheme is generated based on the specific time scheduling requirements. If the user demand changes dynamically within a specific time period, the operating time period of the charging facility is adjusted to obtain the updated time scheduling instruction. Based on the updated time scheduling instruction and the power parameter value, a final load management instruction is generated. A control signal is issued based on the actual operating status of the charging facility, and the successful transmission of the instruction is determined. Execution feedback data after the issuance is obtained.

[0025] Step 8: Based on the real-time adjusted load management instructions generated in step 7, specific power control signals are sent to the charging facilities to optimize the user experience and ensure the continuity and response speed of charging services in each time period. If the user satisfaction feedback through the interactive system is lower than the preset threshold, the backup scheduling mechanism is triggered to prioritize the allocation of backup charging resources and generate an improved service allocation plan for long-term optimization and adjustment.

[0026] Based on the need for real-time adjustment, the load management module obtains the current operating data and power allocation status of the charging facility. Based on this operating data and power allocation status, a determination is made as to whether service continuity and response speed requirements are met. If the preset response speed standards are not met, an adjusted power control signal is generated, and the specific content of the power control signal is determined. Instructions are issued to the charging facility using this power control signal to obtain operational feedback data from the charging facility after execution. Based on this operational feedback data, a determination is made as to whether the user experience optimization goal is met. If the operational feedback data indicates an interruption or delay, a backup scheduling mechanism is triggered to generate a backup resource deployment plan. Based on this backup resource deployment plan, a list of available backup charging resources is obtained from the resource allocation database. Based on this list and satisfaction feedback below a preset threshold, a determination is made as to which resources can respond quickly and a list of priority resources is determined. Based on this list of priority resources, an improved service allocation plan is generated. User satisfaction feedback data is obtained for this plan, and a determination is made as to whether the power control parameters need to be adjusted to generate optimized allocation instructions.

[0027] Step 9: For the improved service allocation plan generated in Step 8, combine the feedback loop of user satisfaction and load data, and use the feedback data to update the parameters of the prediction model and the rules of the scheduling strategy to ensure that the load management instructions continue to match the dynamic changes in user demand, and generate a long-term load management optimization path for continuous system improvement and resource balancing.

[0028] Real-time feedback information on user satisfaction and load data is obtained from the user interaction interface and background logs. For the feedback loop mechanism, the data is classified and stored in a pre-established database. By comparing the preset threshold range, it is determined whether the user satisfaction meets the expected standards to obtain a preliminary feedback evaluation result. Based on the preliminary feedback evaluation results, the current configuration data of the prediction parameters is obtained. Combined with the recorded information of the load data, the configuration content of the prediction parameters is adjusted to determine the adjusted parameter configuration scheme. The allocation logic of the scheduling rules is updated through the adjusted parameter configuration scheme. If the load data exceeds the preset threshold range, the resource allocation ratio is adjusted, and the updated scheduling rule combination is obtained to determine whether it matches the dynamically changing user needs. Based on the updated scheduling rule combination, an optimized path for load management is generated, and a database query tool is used to monitor the resource balance status. For load fluctuations in long-term operation, the final optimized path solution is determined.

[0029] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A new energy vehicle charging load balancing scheduling method, characterized in that: The method comprises: Step 1: Deploy an intelligent sensor network to collect real-time load data and user demand information from electric vehicles and charging facilities. This data is then integrated to manage load fluctuations and dynamically change user demand. This data sets a comprehensive dataset containing charging power, time distribution, and vehicle status. This data generates real-time state mapping data of load fluctuations and demand changes for subsequent predictive analysis. Step 2: Based on the real-time state mapping data of load fluctuations and demand changes generated in Step 1, a pre-established forecasting model is applied to analyze trends of overload during peak hours and idleness during off-peak hours. Time series analysis is used to process the comprehensive data set to determine the load distribution forecast for the future period. This forecast is used to make decisions on resource allocation and user guidance. Step 3: Based on the load distribution prediction results determined in Step 2, an interactive system based on a two-way communication mechanism is constructed. The system obtains the user's charging preferences and vehicle power status from the electric vehicle through mobile terminals or on-board devices, and pushes charging time recommendations to the user. If the prediction results show that the overload risk during peak hours is higher than the preset threshold, the user is given priority to choose off-peak hours for charging. This generates a preliminary resource allocation balance plan for subsequent power allocation optimization. Step 4: Based on the preliminary resource allocation balance plan generated in Step 3, combined with the dynamic scheduling strategy, a genetic algorithm is applied to optimize the power distribution of charging facilities. The optimization goal is to minimize the overload risk during peak hours and maximize the resource utilization during off-peak hours. The load distribution ratio of each facility is adjusted to generate an optimized power distribution matrix for subsequent simulation tests. Step 5: Using virtual simulation testing technology, simulate the load distribution effect in a grid simulation model built based on actual grid parameters for the optimized power distribution matrix generated in Step 4. Analyze the voltage fluctuations and current overloads that may occur during the simulation process, and generate load distribution stability assessment data for subsequent solution adjustments. Step 6: Based on the load distribution stability assessment data generated in step 5, if the voltage fluctuation or current overload index in the assessment data exceeds the preset threshold, the power distribution matrix is ​​adjusted again, and the load distribution plan is re-optimized through iterative calculation to generate a final load distribution plan that meets the grid stability requirements for real-time monitoring and execution; Step 7: Based on the final load distribution plan generated in Step 6, the real-time data acquisition system continuously monitors the load fluctuation management effect. Based on the dynamic changes in user demand and the resource allocation balance goal, the operating parameters of the charging facilities are dynamically updated, including the power upper limit and time scheduling strategy. The real-time adjusted load management instructions are generated for the issuance of power control signals. Step 8: Based on the real-time adjusted load management instructions generated in Step 7, specific power control signals are issued to charging facilities to optimize user experience and ensure the continuity and responsiveness of charging services during each period. If the user satisfaction feedback through the interactive system falls below a preset threshold, the backup scheduling mechanism is triggered to prioritize the allocation of backup charging resources and generate an improved service allocation plan for long-term optimization and adjustment. Step 9: For the improved service allocation plan generated in Step 8, combine the feedback loop of user satisfaction and load data, and use the feedback data to update the parameters of the prediction model and the rules of the scheduling strategy to ensure that the load management instructions continue to match the dynamic changes in user demand, and generate a long-term load management optimization path for continuous system improvement and resource balancing.

2. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 1 specifically includes: Obtaining load data and user demand information from electric vehicles and charging facilities through a deployed sensor network, performing preliminary integration of the load data and user demand to generate an initial data set including charging power and time distribution, and obtaining a comprehensive record for subsequent processing; Based on the initial data set, a data cleaning tool is used to organize the vehicle status information. If it is detected that the load data exceeds a preset threshold, the time distribution is marked to determine the critical time period of load fluctuation; By correlating the marked time distribution with the vehicle status, a data mapping tool is used to establish a corresponding relationship between the real-time status and the load fluctuation, and mapping data is generated to determine the dynamic trend of demand changes; With respect to the mapping data, a time series processing tool is used to extract features of the load fluctuation and the demand change, obtain feature vectors for prediction analysis, and obtain basic prediction data for subsequent applications.

3. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 2 specifically includes: Based on the real-time state mapping data of load fluctuations and demand changes, a time series decomposition tool is used to process the comprehensive data set to separate trend components, periodic components, and random components. The characteristics of overload during peak hours and idleness during off-peak hours are classified and labeled to obtain a classified load feature data set to determine the direction of load fluctuation trends in the future. Extracting characteristic values ​​of peak periods and valley periods from the classified load characteristic data set, smoothing the characteristic values ​​using a sliding window tool to obtain a smoothed load fluctuation curve, determining whether there are abnormal fluctuation points exceeding a preset threshold in the load fluctuation curve, and if so, marking the abnormal points to obtain a marked load fluctuation distribution result; Based on the marked load fluctuation distribution results, a regression prediction tool is used to perform fitting calculations on the load distribution in the future period, obtain fitted load distribution prediction data, determine the overload risk during peak hours and the idle risk during off-peak hours in the prediction data, and determine a risk level distribution map; According to the risk level distribution map, a resource scheduling simulation tool is used to simulate the distribution of the predicted load, obtain resource utilization data after the simulated allocation, and dynamically adjust the areas where the utilization is lower than the preset threshold to obtain an adjusted resource allocation plan.

4. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 3 specifically includes: Obtain user preferences and vehicle power information from a mobile terminal or vehicle-mounted device, collect real-time data through a pre-established communication interface, perform format verification on the data, and obtain a standardized user input data set; Based on the user input data set and the load distribution prediction results, determine whether the overload risk during the peak period is higher than a preset threshold. If higher than the preset threshold, generate a charging time recommendation to guide charging to the off-peak period and determine a preliminary time allocation plan; Based on the preliminary time period allocation plan, push charging time recommendations to users through a two-way communication mechanism, obtain feedback from the users, and obtain an adjusted confirmation data set; According to the confirmed data set and in combination with resource allocation requirements, the charging time and power distribution are secondary matched, and a general database tool is used to store the matching results to generate a final balancing solution.

5. The method for balancing and scheduling charging loads of new energy vehicles according to claim 1, characterized in that: The step 4 specifically includes: According to the pre-established resource allocation plan, the load ratio information of each charging facility during peak and off-peak hours is obtained from the dynamic scheduling data. Based on the peak overload risk and off-peak utilization goals, the genetic algorithm is used to adjust the initial power allocation to obtain the first version of the power allocation matrix; By analyzing the first version of the power allocation matrix, the distribution data of the load of each facility at different time periods is obtained. If the load ratio of a facility during the peak period exceeds a preset threshold, the power allocation of the facility is adjusted again to determine the second version of the power allocation matrix; For the second version of the power allocation matrix, underutilized facility load information is extracted from the resource utilization data during the off-peak period, and the power allocation during the off-peak period is optimized using a data comparison tool to obtain the third version of the power allocation matrix. According to the third version of the power allocation matrix and in combination with risk control requirements, the adjusted power allocation is verified through a simulation test tool to determine whether the optimization goal is met and to generate a final power allocation matrix.

6. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 5 specifically includes: Based on a pre-established power grid parameter database, actual power grid data of the target area is obtained, a virtual simulation environment is constructed for the power grid data, and the optimized power distribution matrix is ​​loaded into the environment. The matrix is ​​run through a simulation test tool to obtain preliminary simulation data of the load distribution; Based on the preliminary simulation data, a voltage fluctuation detection tool is used to monitor the voltage value of each node in real time. If the node voltage value exceeds a preset threshold range, the fluctuation information of the node is recorded. In combination with the current overload detection tool, it is determined whether there is an overload phenomenon and the distribution details of the abnormal nodes are determined; By analyzing the distribution details of the abnormal nodes, specific data required for load stability assessment is obtained. Data processing tools are used to classify and count the frequency and impact range of the voltage fluctuations and current overloads to obtain quantitative indicators of load distribution stability. Based on the quantitative indicators, for areas with low load stability, a data visualization tool is used to generate a distribution effect diagram, and combined with the historical records in the solution adjustment database, the power distribution parameter range that needs to be optimized is determined, and the adjustment reference data is output.

7. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 6 specifically includes: Extract voltage fluctuation and current overload indicators that exceed preset thresholds from the stability assessment results, generate a preliminary adjustment requirement list for these abnormal indicators, use data comparison tools to determine the specific distribution locations of these abnormal indicators, and obtain node information of abnormal loads; According to the node information of the abnormal load, the corresponding distribution parameters in the power matrix are obtained, the distribution parameters are adjusted twice using a matrix operation tool, and the preliminary optimization result of the power matrix after adjustment is determined through simulation operation; If the voltage fluctuation or current overload index still exceeds the preset threshold in the preliminary optimization result, an iterative calculation process is started to repeatedly adjust the distribution ratio of the power matrix through a cyclic comparison tool to obtain an intermediate optimization solution that meets the grid stability requirements; A final load distribution plan is generated according to the intermediate optimization plan, and a real-time monitoring tool is used to collect data on the execution process of the final load distribution plan. Dynamic changes during the execution process are recorded to determine whether the continuity requirements of power grid stability are met.

8. The method for balancing and scheduling charging loads of new energy vehicles according to claim 1, characterized in that: The step 7 specifically includes: Based on load fluctuations, the operating data of the charging facility is obtained from the real-time monitoring interface. Based on the changes in user demand reflected in the operating data, it is determined whether the load exceeds a preset threshold range. If so, a preliminary adjustment signal is generated to obtain a load abnormality determination result. By using the preliminary adjustment signal and combining it with the balance target of resource allocation, analyzing the operating parameters of the charging facility, using the pre-established power allocation rule, determining the adjustment range of the power upper limit, and obtaining the adjusted power parameter value, Indicates the power adjustment amount, Indicates the maximum allowable adjustment range. represents the adjustment coefficient, represents the predicted signal value, Indicates the current signal value, Indicates the maximum value of the signal, Indicates the final power parameter value, Indicates the adjusted power value, represents the lower power limit constraint, represents the power upper limit constraint; Generating a new time scheduling plan based on the adjusted power parameter value and the specific time scheduling requirements; if the user demand changes dynamically within a specific time period, adjusting the operating time period of the charging facility to obtain the updated time scheduling instructions; The final load management instruction is generated by the updated time scheduling instruction and the power parameter value, a control signal is issued according to the actual operating status of the charging facility, it is determined whether the instruction is successfully transmitted, and the execution feedback data after the instruction is issued is obtained.

9. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 8 specifically includes: Based on the need for real-time adjustment, the system obtains the current operating data and power distribution status of the charging facility from the load management module, determines whether the service continuity and response speed requirements are met based on the operating data and the power distribution status, and generates an adjusted power control signal if the preset response speed standard is not met, and determines the specific content of the power control signal; Sending instructions to the charging facility via the power control signal, obtaining operational feedback data from the charging facility after execution, and determining whether the operational feedback data meets the optimization goal of user experience. If the operational feedback data indicates an interruption or delay, triggering a backup scheduling mechanism to obtain a call plan for backup resources; According to the backup resource call plan, a list of available backup charging resources is obtained from a resource allocation database. Based on the list and the condition where the satisfaction feedback is below a preset threshold, it is determined which resources can respond quickly and the priority allocation resource list is determined. An improved service allocation plan is generated through the priority allocation resource list, user satisfaction feedback data is obtained for the plan, and it is determined whether the power control parameters need to be adjusted to obtain an optimized allocation instruction.

10. A new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that: The step 9 specifically includes: Obtain real-time feedback information on user satisfaction and load data from the user interaction interface and background logs. Based on the feedback loop mechanism, the data is classified and stored in a pre-established database. By comparing it with the preset threshold range, it is determined whether the user satisfaction meets the expected standards and a preliminary feedback evaluation result is obtained; According to the preliminary feedback evaluation results, current configuration data of the prediction parameters are obtained, and the configuration content of the prediction parameters is adjusted in combination with the recorded information of the load data to determine an adjusted parameter configuration scheme; The allocation logic of the scheduling rules is updated through the adjusted parameter configuration scheme. If the load data exceeds the preset threshold range, the resource allocation ratio is adjusted, and the updated scheduling rule combination is obtained to determine whether it matches the dynamically changing user needs; Based on the updated scheduling rule combination, an optimized path for load management is generated, a database query tool is used to monitor the resource balance state, and a final optimized path solution is determined for load fluctuations in long-term operation.

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