A new energy vehicle charging load balancing scheduling method

Abstract

The application provides a new electric vehicle charging load balancing scheduling method, which collects load data and user demand information in real time through an intelligent sensor network, constructs a comprehensive data set and generates real-time state mapping data, analyzes peak overload and trough idle trends using a prediction model to determine load distribution prediction results; based on a two-way communication mechanism, user charging preferences are obtained and charging suggestions are pushed to generate a preliminary resource allocation scheme, genetic algorithm is applied to optimize power distribution, minimize overload risk and maximize resource utilization; through virtual simulation test, load distribution stability is evaluated and the scheme is iteratively optimized; real-time monitoring of load fluctuations, dynamic updating of operating parameters and issuance of control instructions, triggering of standby scheduling mechanism according to user feedback, continuous optimization of service allocation. The application realizes intelligent management of electric vehicle charging load, effectively balances power grid resource allocation, and improves user satisfaction and charging efficiency.

Description

Technical Field

[0001] This invention relates to a method for load balancing scheduling of new energy vehicle charging. Background Technology

[0002] With the rapid growth in the number of electric vehicles, the rational scheduling and management of charging infrastructure is not only a technical issue, but also a crucial link 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, lacking the ability to adapt to real-time changes and struggling to cope with fluctuations in grid load and dynamic changes in user demand. This leads to overloaded charging facilities during peak hours and idle resources during off-peak hours, resulting in overall low efficiency.

[0003] The core challenges in this field stem from several interconnected technical problems. The lack of an effective two-way communication mechanism between charging infrastructure and electric vehicles prevents the system from obtaining timely information on vehicle status and user demand, hindering precise charging scheduling. Due to the lack of real-time data support, the system cannot accurately predict load distribution trends in complex scenarios, further exacerbating resource imbalances. At a deeper level, the absence of effective virtual simulation methods for testing and optimizing scheduling strategies often results in high trial-and-error costs in actual operation, impacting the scientific rigor and reliability of management. Summary of the Invention

[0004] This invention proposes a new energy vehicle charging load balancing scheduling method. Based on a smart scheduling system using two-way communication and virtual simulation technology, it achieves dynamic balance of charging load and efficient utilization of resources, effectively balancing the allocation of power grid resources.

[0005] The technical solution of this invention is implemented as follows:

[0006] A method for load balancing scheduling of new energy vehicle charging, the method comprising:

[0007] Step 1: By deploying a smart sensor network, load data and user demand information are collected in real time from electric vehicles and charging facilities. Data is integrated for load fluctuation management and dynamic changes in user demand to build a comprehensive dataset that includes charging power, time distribution and vehicle status. Real-time status mapping data of load fluctuation and demand changes is generated for subsequent predictive analysis.

[0008] Step 2: Based on the real-time status mapping data of load fluctuation and demand change generated in Step 1, apply the pre-established prediction model to analyze the trend of overload during peak hours and idle during off-peak hours, use time series analysis methods to process the comprehensive dataset, determine the load distribution prediction results for a period of time in the future, and use them for resource allocation and user guidance decision-making.

[0009] Step 3: Based on the load distribution prediction results determined in Step 2, construct an interactive system based on a two-way communication mechanism. Obtain user charging preferences and vehicle battery status from the electric vehicle through mobile terminals or in-vehicle devices, and push charging time suggestions to users. If the prediction results show that the overload risk during peak hours is higher than the preset threshold, then guide users to choose to charge during off-peak hours first, and generate a preliminary resource allocation balance plan for subsequent power allocation optimization.

[0010] Step 4: Based on the preliminary resource allocation balancing scheme generated in Step 3, and combined with the dynamic scheduling strategy, the genetic algorithm is applied to optimize the power allocation of charging facilities. The optimization objectives are to minimize the overload risk during peak hours and maximize the resource utilization rate during off-peak hours. The load allocation ratio of each facility is adjusted to generate an optimized power allocation matrix for subsequent simulation tests.

[0011] Step 5: For the optimized power allocation matrix generated in Step 4, use virtual simulation testing technology to simulate the load allocation effect in a 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, and generate load allocation stability evaluation data for subsequent scheme adjustments.

[0012] 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 a second time, and the load distribution scheme is re-optimized through iterative calculation to generate a final load distribution scheme that meets the grid stability requirements for real-time monitoring and execution.

[0013] Step 7: Based on the final load allocation scheme generated in Step 6, continuously monitor the load fluctuation management effect in conjunction with the real-time data acquisition system, dynamically update the operating parameters of the charging facilities, including the power limit and time scheduling strategy, in response to dynamic changes in user demand and resource allocation balance goals, and generate real-time adjusted load management instructions for the issuance of power control signals.

[0014] Step 8: Based on the real-time adjusted load management instructions generated in Step 7, send specific power control signals to the charging facilities. In order to optimize user experience, 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, trigger the backup scheduling mechanism, prioritize the allocation of backup charging resources, and generate an improved service allocation scheme for long-term optimization and adjustment.

[0015] Step 9: Based on the improved service allocation scheme generated in Step 8, and combined with the feedback loop of user satisfaction and load data, use the feedback data to update the parameters of the prediction model and the rules of the scheduling strategy, ensuring that the load management instructions continuously match the dynamic changes in user needs, and generating a load management optimization path for long-term operation, which is used for continuous system improvement and resource balancing.

[0016] The beneficial effects of this invention are as follows: It collects load data and user demand information in real time through an intelligent sensor network, constructs a comprehensive dataset, generates real-time state mapping data, and uses a predictive model to analyze peak overload and off-peak idle trends to determine load distribution prediction results; based on a two-way communication mechanism, it obtains user charging preferences and pushes charging suggestions, generates a preliminary resource allocation scheme, and applies a genetic algorithm to optimize power allocation, minimizing overload risk and maximizing resource utilization; it evaluates load allocation stability through virtual simulation testing and iteratively optimizes the scheme; it monitors load fluctuations in real time, dynamically updates operating parameters and issues control commands, triggers a backup scheduling mechanism based on user feedback, and continuously optimizes service allocation. This invention achieves intelligent management of electric vehicle charging load, effectively balances power grid resource allocation, and improves user satisfaction and charging efficiency. Attached Figure Description

[0017] 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.

[0018] Figure 1 This is the flow control diagram of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Reference Figure 1 A new energy vehicle charging load balancing scheduling method specifically includes:

[0021] Step 1: By deploying a smart sensor network, load data and user demand information are collected in real time from electric vehicles and charging facilities. Data is integrated for load fluctuation management and dynamic changes in user demand to build a comprehensive dataset that includes charging power, time distribution, and vehicle status. Real-time status mapping data of load fluctuation and demand changes is generated for subsequent predictive analysis.

[0022] By deploying a sensor network, load data and user demand information are acquired from electric vehicles and charging facilities. This load data and user demand are initially integrated to generate an initial dataset containing charging power and time distribution, resulting in a comprehensive record for subsequent processing. Based on this initial dataset, vehicle status information is processed using data cleaning tools. If the load data exceeds a preset threshold, the time distribution is marked to identify key time periods of load fluctuation. By associating the marked time distribution with vehicle status, a data mapping tool is used to construct the correspondence between real-time status and load fluctuations, generating mapping data to determine the dynamic trend of demand changes. For this mapping data, time series processing tools are used to extract features from load fluctuations and demand changes, obtaining feature vectors for predictive analysis, thus providing the predictive foundation data for subsequent applications.

[0023] Step 2: Based on the real-time status mapping data of load fluctuations and demand changes generated in Step 1, apply the pre-established prediction 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 dataset, and determine the load distribution prediction results for a future period of time for resource allocation and user guidance decision-making.

[0024] Based on real-time status mapping data of load fluctuations and demand changes, a time series decomposition tool is used to process the comprehensive dataset, separating trend components, periodic components, and random components. Overload during peak hours and idleness during off-peak hours are categorized and labeled to obtain a classified load feature dataset, determining the load fluctuation trend direction over a future period. Feature values ​​for peak and off-peak hours are extracted from the classified load feature dataset, and a sliding window tool is used to smooth these feature values, obtaining a smoothed load fluctuation curve. The load fluctuation curve is then checked for abnormal fluctuation points exceeding a preset threshold; if any exceed the threshold, these abnormal fluctuation points are marked, resulting in a marked load fluctuation distribution. Based on the marked load fluctuation distribution, a regression prediction tool is used to fit the load distribution over a future period, obtaining the fitted load distribution prediction data. The overload risk during peak hours and the idleness risk during off-peak hours in the predicted data are then assessed, determining a risk level distribution map. Based on the risk level distribution map, a resource scheduling simulation tool is used to simulate and allocate the predicted load distribution, obtain the resource utilization data after the simulated allocation, and dynamically adjust the resource configuration scheme for areas with utilization rates lower than a preset threshold.

[0025] Step 3: Based on the load distribution prediction results determined in Step 2, construct an interactive system based on a two-way communication mechanism. Obtain user charging preferences and vehicle battery status from the electric vehicle through mobile terminals or in-vehicle devices, and push charging time suggestions to users. If the prediction results show that the overload risk during peak hours is higher than the preset threshold, then guide users to choose to charge during off-peak hours first, and generate a preliminary resource allocation balance plan for subsequent power allocation optimization.

[0026] User preferences and vehicle battery information are acquired from mobile terminals or in-vehicle devices. Real-time data is collected through a pre-established communication interface, and the data undergoes format validation to obtain a standardized user input dataset. Based on the user input dataset and load distribution prediction results, it is determined whether the overload risk during peak hours exceeds a preset threshold. If it does, a charging time suggestion is generated to guide charging to off-peak hours, determining a preliminary time allocation scheme. For this preliminary time allocation scheme, charging time suggestions are pushed to users through a two-way communication mechanism to obtain user feedback, resulting in an adjusted confirmation dataset. Based on the confirmation dataset and resource allocation requirements, a secondary matching of charging time and power allocation is performed. The matching results are stored using a general database tool to generate a final balanced scheme.

[0027] Step 4: Based on the preliminary resource allocation balancing scheme generated in Step 3, and combined with the dynamic scheduling strategy, the genetic algorithm is applied to optimize the power allocation of charging facilities. The optimization objectives are to minimize the overload risk during peak hours and maximize the resource utilization rate during off-peak hours. The load allocation ratio of each facility is adjusted to generate an optimized power allocation matrix for subsequent simulation testing.

[0028] Based on a pre-established resource allocation scheme, the load ratio information of each charging facility during peak and off-peak hours is obtained from dynamic scheduling data. To address the overload risk during peak hours and the resource utilization rate during off-peak hours, a genetic algorithm is used to adjust the initial power allocation, resulting in a first version of the power allocation matrix. By analyzing the first version of the power allocation matrix, the load distribution data of each facility at different times is obtained. If the load ratio of a facility during peak hours exceeds a preset threshold, the power allocation of that facility is adjusted a second time, determining a 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 rate data during off-peak hours. A data comparison tool is used to optimize the power allocation during off-peak hours, resulting in a third version of the power allocation matrix. Based on the third version of the power allocation matrix and considering risk control requirements, simulation testing tools are used to verify the adjusted power allocation, determining whether it meets the optimization objectives, and generating the final power allocation matrix.

[0029] Step 5: For the optimized power allocation matrix generated in Step 4, use virtual simulation testing technology to simulate the load allocation effect in a 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, and generate load allocation stability assessment data for subsequent scheme adjustments.

[0030] Based on a pre-established power grid parameter database, actual power grid data for the target area is obtained. A virtual simulation environment is constructed using this data, and an optimized power allocation matrix is ​​loaded into the environment. The simulation is then run using simulation testing tools to obtain preliminary simulated load allocation data. For this preliminary simulation data, a voltage fluctuation detection tool is used to monitor the voltage values ​​of each node in real time. If a node's voltage value exceeds a preset threshold range, the fluctuation information of that node is recorded. Combined with a current overload detection tool, the presence of overload phenomena is determined, and the distribution details of abnormal nodes are identified. Based on the distribution details of these abnormal nodes, specific data required for load stability assessment is obtained. Data processing tools are used to classify and statistically analyze the frequency and impact range of voltage fluctuations and current overloads, obtaining quantitative indicators of load allocation stability. Based on these quantitative indicators, for areas with low load stability, a distribution effect map is generated using data visualization tools. Combined with historical records in the scheme adjustment database, the power allocation parameter range requiring optimization is determined, and adjustment reference data is output.

[0031] 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 a second time, and the load distribution scheme is re-optimized through iterative calculation to generate a final load distribution scheme that meets the grid stability requirements for real-time monitoring and execution.

[0032] Voltage fluctuations and current overload indicators exceeding preset thresholds are extracted from the stability assessment results. A preliminary adjustment requirement list is generated for these indicators. Data comparison tools are used to determine the specific distribution locations of these indicators, obtaining node information of abnormal loads. Based on the node information of the abnormal loads, corresponding allocation parameters in the power matrix are obtained. These allocation parameters are then adjusted a second time using matrix operation tools. Simulation calculations are used to determine the preliminary optimization results of the adjusted power matrix. If voltage fluctuations or current overload indicators still exceed preset thresholds in the preliminary optimization results, an iterative calculation process is initiated. The allocation ratio of the power matrix is ​​repeatedly adjusted using loop comparison tools to obtain an intermediate optimization scheme that meets the grid stability requirements. A final load allocation scheme is generated based on the intermediate optimization scheme. Real-time monitoring tools are used to collect data during the execution of the final load allocation scheme, recording dynamic changes during the execution process to determine whether it meets the continuity requirements of grid stability.

[0033] Step 7: Based on the final load allocation scheme generated in Step 6, and in conjunction with the real-time data acquisition system to continuously monitor the load fluctuation management effect, dynamically update the operating parameters of the charging facilities, including the power limit and time scheduling strategy, according to the dynamic changes in user demand and the resource allocation balance target, and generate real-time adjusted load management instructions for the issuance of power control signals.

[0034] Based on load fluctuations, operational data of the charging facilities is acquired from a real-time monitoring interface. The changes in user demand reflected in this data are used to determine if the load exceeds a preset threshold range. If it does, a preliminary adjustment signal is generated, resulting in a load anomaly assessment. Using this preliminary adjustment signal and the resource allocation balance target, the operating parameters of the charging facilities are analyzed. A pre-established power allocation rule is then used to determine the adjustment range of the power upper limit, and the adjusted power parameter values ​​are obtained.

[0035]

[0036] Indicates the amount of power adjustment. Indicates the maximum allowable adjustment range. Indicates the adjustment factor. Indicates the predicted signal value. Indicates the current signal value. Indicates the maximum value of the signal.

[0037]

[0038] This represents the final determined power parameter value. This indicates the adjusted power value. This indicates a lower limit constraint on power. This indicates a power upper limit constraint. Based on the adjusted power parameter values, a new time scheduling scheme is generated to meet specific time scheduling needs. If user demand dynamically changes within a specific time period, the operating hours of the charging facility are adjusted to obtain an updated time scheduling instruction. Using the updated time scheduling instruction and power parameter values, a final load management instruction is generated. Control signals are sent based on the actual operating status of the charging facility, and the success of instruction transmission is determined, obtaining execution feedback data after transmission.

[0039] Step 8: Based on the real-time adjusted load management instructions generated in Step 7, send specific power control signals to the charging facilities. In order to optimize user experience, 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, trigger the backup scheduling mechanism, prioritize the allocation of backup charging resources, and generate an improved service allocation scheme for long-term optimization and adjustment.

[0040] Based on real-time adjustment needs, the system obtains the current operating data and power allocation status of the charging facility from the load management module. It then determines whether the operating data and power allocation status meet the requirements for service continuity and response speed. If the preset response speed standard is not met, an adjusted power control signal is generated, and the specific content of the power control signal is determined. Using the power control signal, instructions are issued to the charging facility, and operational feedback data after execution is obtained. Based on the operational feedback data, it is determined whether it meets the user experience optimization goals. If the operational feedback data indicates an interruption or delay, a backup scheduling mechanism is triggered, resulting in a backup resource allocation plan. Based on the backup resource allocation plan, an available backup charging resource list is obtained from the resource allocation database. Considering the available backup charging resource list and the situation where satisfaction feedback is below a preset threshold, it is determined which resources can respond quickly, and a priority resource list is determined. Using the priority resource list, an improved service allocation plan is generated. Based on the improved service allocation plan, user satisfaction feedback data is obtained to determine whether the power control signal needs adjustment, resulting in an optimized allocation instruction.

[0041] Step 9: Based on the improved service allocation scheme generated in Step 8, and combined with the feedback loop of user satisfaction and load data, use the feedback data to update the parameters of the prediction model and the rules of the scheduling strategy, ensuring that the load management instructions continuously match the dynamic changes in user needs, and generating a load management optimization path for long-term operation, which is used for continuous system improvement and resource balancing.

[0042] Real-time feedback information on user satisfaction and load data is obtained from the user interface and backend logs. Load data is categorized and stored in a pre-established database based on this real-time feedback. By comparing the data with preset threshold ranges, it is determined whether the user satisfaction meets the expected standards, resulting in a preliminary feedback evaluation. Based on this preliminary evaluation, the current configuration data of the prediction parameters is obtained. Combined with the recorded load data, the configuration of the prediction parameters is adjusted to determine the adjusted parameter configuration scheme. Using the adjusted parameter configuration scheme, the allocation logic of the scheduling rules is updated. If the load data exceeds a preset threshold range, the resource allocation ratio is adjusted, resulting in an updated combination of scheduling rules. It is then determined whether this matches dynamically changing user needs. Based on the updated combination of scheduling rules, an optimized load management path is generated. Database query tools are used to monitor resource balance. For load fluctuations during long-term operation, the final optimized path scheme is determined.

[0043] 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 within the protection scope of the present invention.

Claims

1. A method for load balancing scheduling of new energy vehicle charging, characterized in that, The method includes: Step 1: By deploying a smart sensor network, load data and user demand information are collected in real time from electric vehicles and charging facilities. Data is integrated for load fluctuation management and dynamic changes in user demand to build a comprehensive dataset that includes charging power, time distribution and vehicle status. Real-time status mapping data of load fluctuation and demand changes is generated for subsequent predictive analysis. Step 1 specifically includes: acquiring load data and user demand information from electric vehicles and charging facilities through a deployed sensor network; initially integrating the load data and user demand to generate an initial dataset containing charging power and time distribution, obtaining a comprehensive record for subsequent processing; based on the initial dataset, using data cleaning tools to organize vehicle status information; if the load data exceeds a preset threshold, marking the time distribution to determine the key time periods of load fluctuation; by associating the marked time distribution with vehicle status, using data mapping tools to construct the correspondence between real-time status and load fluctuation, generating mapping data, and judging the dynamic trend of demand changes; for the mapping data, using time series processing tools to extract features from load fluctuations and demand changes, obtaining feature vectors for predictive analysis, and obtaining the prediction basis data for subsequent applications; Step 2: Based on the real-time status mapping data of load fluctuation and demand change generated in Step 1, apply the pre-established prediction model to analyze the trend of overload during peak hours and idle during off-peak hours, use time series analysis methods to process the comprehensive dataset, determine the load distribution prediction results for a period of time in the future, and use them for resource allocation and user guidance decision-making. Step 3: Based on the load distribution prediction results determined in Step 2, construct an interactive system based on a two-way communication mechanism. Obtain user charging preferences and vehicle battery status from the electric vehicle through mobile terminals or in-vehicle devices, and push charging time suggestions to users. If the prediction results show that the overload risk during peak hours is higher than the preset threshold, then guide users to choose to charge during off-peak hours first, and generate a preliminary resource allocation balance plan for subsequent power allocation optimization. Step 4: Based on the preliminary resource allocation balancing scheme generated in Step 3, and combined with the dynamic scheduling strategy, the genetic algorithm is applied to optimize the power allocation of charging facilities. The optimization objectives are to minimize the overload risk during peak hours and maximize the resource utilization rate during off-peak hours. The load allocation ratio of each facility is adjusted to generate an optimized power allocation matrix for subsequent simulation tests. Step 5: For the optimized power allocation matrix generated in Step 4, use virtual simulation testing technology to simulate the load allocation effect in a 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, and generate load allocation stability evaluation data for subsequent scheme 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 a second time, and the load distribution scheme is re-optimized through iterative calculation to generate a final load distribution scheme that meets the grid stability requirements for real-time monitoring and execution. Step 7: Based on the final load allocation scheme generated in Step 6, continuously monitor the load fluctuation management effect in conjunction with the real-time data acquisition system, dynamically update the operating parameters of the charging facilities, including the power limit and time scheduling strategy, in response to dynamic changes in user demand and resource allocation balance goals, and generate real-time adjusted load management instructions for the issuance of power control signals. Step 8: Based on the real-time adjusted load management instructions generated in Step 7, send specific power control signals to the charging facilities. In order to optimize user experience, 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, trigger the backup scheduling mechanism, prioritize the allocation of backup charging resources, and generate an improved service allocation scheme for long-term optimization and adjustment. Step 9: Based on the improved service allocation scheme generated in Step 8, and combined with the feedback loop of user satisfaction and load data, use the feedback data to update the parameters of the prediction model and the rules of the scheduling strategy, ensuring that the load management instructions continuously match the dynamic changes in user needs, and generating a load management optimization path for long-term operation, which is used for continuous system improvement and resource balancing.

2. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 2 specifically includes: Based on the real-time status mapping data of load fluctuations and demand changes, the comprehensive dataset is processed using a time series decomposition tool 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 the classified load feature dataset and determine the load fluctuation trend direction in the future. Feature values ​​for peak and off-peak periods are extracted from the classified load feature dataset. The feature values ​​are smoothed using a sliding window tool to obtain a smoothed load fluctuation curve. It is determined whether there are any abnormal fluctuation points in the load fluctuation curve that exceed a preset threshold. If they exceed the preset threshold, the abnormal fluctuation points are marked to obtain the marked load fluctuation distribution result. Based on the marked load fluctuation distribution results, a regression prediction tool is used to fit the load distribution over a future period to obtain the fitted load distribution prediction data. The overload risk during peak hours and the idle risk during off-peak hours in the prediction data are then determined to establish a risk level distribution map. Based on the risk level distribution map, a resource scheduling simulation tool is used to simulate and allocate the predicted load distribution, obtain the resource utilization data after the simulated allocation, and dynamically adjust the resource configuration scheme for areas with utilization rates lower than a preset threshold.

3. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 3 specifically includes: User preferences and vehicle battery information are obtained from mobile terminals or in-vehicle devices. Real-time data is collected through a pre-established communication interface, and the data is format-validated to obtain a standardized user input dataset. Based on the user input dataset and the load distribution prediction results, it is determined whether the overload risk during peak hours is higher than a preset threshold. If it is higher than the preset threshold, a charging time suggestion to guide the user to the off-peak hours is generated to determine a preliminary time allocation scheme. Based on the initial time slot allocation plan, charging time suggestions are pushed to users through a two-way communication mechanism to obtain user feedback and obtain an adjusted confirmation dataset. Based on the confirmed dataset and the resource allocation requirements, a secondary matching is performed on the charging time and power allocation. The matching results are stored using a general database tool to generate the final balancing scheme.

4. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 4 specifically includes: Based on the pre-established resource allocation scheme, the load ratio information of each charging facility during peak and off-peak hours is obtained from the dynamic scheduling data. In order to target the overload risk during peak hours and the resource utilization rate during off-peak hours, a genetic algorithm is used to adjust the initial power allocation and 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 are obtained. If the load ratio of a certain facility during the peak period exceeds a preset threshold, the power allocation of the facility is adjusted a second time to determine the second version of the power allocation matrix. For the second version of the power allocation matrix, the underutilized facility load information is extracted from the resource utilization data during off-peak hours, and the power allocation during the off-peak hours is optimized using a data comparison tool to obtain the third version of the power allocation matrix. Based on the third version of the power allocation matrix and in conjunction with risk control requirements, the adjusted power allocation is verified using simulation testing tools to determine whether it meets the optimization objective, and the final power allocation matrix is ​​generated.

5. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 5 specifically includes: Based on a pre-established power grid parameter database, actual power grid data for the target area is obtained from it. A virtual simulation environment is constructed based on the actual power grid data, and an optimized power allocation matrix is ​​loaded into the environment. The simulation is then run using simulation testing tools to obtain preliminary simulation data of load allocation. 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 voltage value of a node exceeds the preset threshold range, the fluctuation information of the node is recorded, and the current overload detection tool is used to determine whether there is an overload phenomenon and to determine the distribution details of abnormal nodes. By obtaining the distribution details of the abnormal nodes, the specific data required for load stability assessment is obtained. Data processing tools are used to classify and statistically analyze the frequency and impact range of 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 distribution effect map is generated using data visualization tools. Combined with historical records in the scheme adjustment database, the range of power allocation parameters that need to be optimized is determined, and adjustment reference data is output.

6. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, 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 the voltage fluctuation and current overload indicators that exceed preset thresholds, use data comparison tools to determine the specific distribution location of the voltage fluctuation and current overload indicators that exceed preset thresholds, and obtain node information of abnormal loads. Based on the node information of the abnormal load, the corresponding allocation parameters in the power matrix are obtained, and the allocation parameters are adjusted a second time using a matrix operation tool. The preliminary optimization results after the power matrix adjustment are determined through simulation calculation. If the voltage fluctuation or current overload index still exceeds the preset threshold in the preliminary optimization results, the iterative calculation process is started, and the allocation ratio of the power matrix is ​​repeatedly adjusted through the cyclic comparison tool to obtain an intermediate optimization scheme that meets the requirements of power grid stability. The final load allocation scheme is generated based on the intermediate optimization scheme. The execution process of the final load allocation scheme is monitored using real-time monitoring tools. The dynamic changes during the execution process are recorded to determine whether the continuous stability requirements of the power grid are met.

7. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 7 specifically includes: Based on load fluctuations, the system obtains charging facility operation data from the real-time monitoring interface. Based on the changes in user demand reflected in the operation data, it determines whether the load exceeds a preset threshold range. If it does, a preliminary adjustment signal is generated to obtain a load anomaly determination result. Based on the initial adjustment signal and the resource allocation balance target, the operating parameters of the charging facilities are analyzed. Using pre-established power allocation rules, the adjustment range of the power upper limit is determined, and the adjusted power parameter values ​​are obtained. Indicates the amount of power adjustment. Indicates the maximum allowable adjustment range. Indicates the adjustment factor. Indicates the predicted signal value. Indicates the current signal value. Indicates the maximum value of the signal. This represents the final determined power parameter value. This indicates the adjusted power value. This indicates a lower limit constraint on power. This indicates a power upper limit constraint; Based on the adjusted power parameter values, a new time scheduling scheme is generated to meet the specific needs of time scheduling. If user demand changes dynamically within a specific time period, the operating period of the charging facility is adjusted to obtain an updated time scheduling instruction. The final load management command is generated by updating the time scheduling command and power parameter values. Control signals are sent according to the actual operating status of the charging facility to determine whether the command was successfully transmitted and to obtain the execution feedback data after it was sent.

8. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 8 specifically includes: Based on the real-time adjustment requirements, the current operating data and power allocation status of the charging facility are obtained from the load management module. Based on the operating data and the power allocation status, it is determined whether the requirements for service continuity and response speed are met. If the preset response speed standard is not met, an adjusted power control signal is generated, and the specific content of the power control signal is determined. The power control signal is used to send instructions to the charging facility, obtain the operation feedback data after the charging facility executes the instructions, and determine whether the operation feedback data meets the user experience optimization goal. If the operation feedback data indicates that there is an interruption or delay, the backup scheduling mechanism is triggered to obtain a backup resource call plan. According to the backup resource mobilization scheme, the available backup charging resource list is obtained from the resource allocation database. Based on the available backup charging resource list and the situation where the satisfaction feedback is lower than the preset threshold, it is determined which resources can respond quickly and the resource list to be allocated first is determined. An improved service allocation scheme is generated using the priority resource list. User satisfaction feedback data is obtained for the improved service allocation scheme to determine whether the power control signal needs to be adjusted, and an optimized allocation instruction is obtained.

9. The new energy vehicle charging load balancing scheduling method according to claim 1, characterized in that, Step 9 specifically includes: Real-time feedback information on user satisfaction and load data is obtained from the user interface and background logs. The load data is categorized and stored in a pre-established database based on the real-time feedback information. By comparing it with a preset threshold range, it is determined whether the user satisfaction meets the expected standard, and a preliminary feedback evaluation result is obtained. 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, and the adjusted parameter configuration scheme is determined. The adjusted parameter configuration scheme is used to update the allocation logic of the scheduling rules. If the load data exceeds the preset threshold range, the resource allocation ratio is adjusted, the updated scheduling rule combination is obtained, and it is determined whether it matches the dynamically changing user needs. Based on the updated scheduling rule combination, an optimized path for load management is generated. Database query tools are used to monitor the resource balance status, and the final optimized path scheme is determined for load fluctuations during long-term operation.

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