Management service system

Abstract

The invention, which relates to the technical field of the new energy charging management service, discloses a management service system comprising a data acquisition module, a data processing module, a calculation module and a management module. According to the method, multiple influence factors can be comprehensively considered to realize accurate prediction of the user waiting time and accurate charging time expectation so as to reduce anxiety of the user in the waiting process, and a scientific planning basis can be provided for a charging station to optimize configuration and use efficiency of charging pile resources so as to shorten the user waiting time; a plurality of factors such as waiting time of the user, the charging demand and the charging rate of the charging pile are comprehensively considered, an optimized energy distribution strategy is constructed to realize efficient utilization of charging pile resources, and the energy waste and the idle time of the charging pile can be reduced through the optimized energy distribution strategy; and a comprehensive operation benefit evaluation system can be constructed by considering a plurality of key indexes such as income, cost and user satisfaction of the charging station, and scientific operation decision support is provided for the charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy charging management services, and in particular to a management service system. Background Art

[0002] With the rapid development of the new energy vehicle industry, new energy charging piles, as important infrastructure for the endurance of new energy vehicles, have a direct impact on the user's charging experience and the operating efficiency of charging stations. Beneficial and intelligent management services can improve user comfort and stickiness, thereby improving the use efficiency of new energy charging piles.

[0003] However, the current management service mode may lack an intelligent and scientific prediction system, which may lead to inaccurate prediction results in estimating the waiting time of charging users, especially during peak charging hours when the waiting time of users may fluctuate greatly. In addition, the energy distribution of existing new energy charging piles may be based on a simple first-come, first-served principle, and lacks an optimization strategy that comprehensively considers factors such as user waiting time, charging needs, charging rate of charging piles and remaining power. In addition, the overall management service mode may lack a comprehensive evaluation of multiple aspects such as user satisfaction, charging pile utilization, energy waste, etc., which affects the use efficiency of charging piles. Summary of the Invention

[0004] The purpose of the present invention is to provide a management service system that solves the problems raised in the above background technology.

[0005] To achieve the above objectives, the present invention provides the following technical solutions: a management service system, comprising:

[0006] Data collection module: collects the charging rate of the charging pile, the charging time of the charging pile, the time when the charging user arrives, the number of new energy vehicles currently waiting in line for charging, and the current waiting time for charging, and saves the collected data into the database;

[0007] Data processing module: cleans, verifies and organizes the charging rate of the charging pile, the charging time of the charging pile, the arrival time of the charging user, the number of new energy vehicles currently waiting in line for charging and the current queuing time for charging, and outputs the number of new energy vehicles currently waiting in line for charging UUXN, the number of users currently waiting for charging n, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT;

[0008] Calculation module: The number of new energy vehicles currently waiting to charge UUXN, the number of users currently waiting to charge n, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT are input. The calculation module outputs the charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the charging station operation benefit evaluation value CBQ;

[0009] Management module: The charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the charging station operation benefit evaluation value CBQ are input, and the management module generates corresponding reports and visualization interfaces for management personnel to view and analyze.

[0010] Optionally, the calculation module includes: a charging waiting time analysis submodule, a charging station energy allocation submodule and a charging station operation benefit submodule.

[0011] Optionally, the calculation formula of the charging waiting time analysis submodule is as follows:

[0012]

[0013] in:

[0014] UUX refers to the evaluation value of the waiting time of charging users, UUXN refers to the number of new energy vehicles currently waiting in line for charging, UUXR refers to the average charging efficiency of the charging station, UUXT refers to the average charging time of new energy vehicles, UA refers to the influence coefficient of the change rate of the number of queues, UUXA refers to the difference between the current number of queues and the number of queues in the previous time period, UB refers to the charging peak adjustment coefficient, UUXP refers to the additional waiting time during the peak period, and UUXN / UUXR refer to the intensity value of the service capacity of the charging station;

[0015] The processing process of the charging waiting time analysis submodule is as follows: the number of new energy vehicles currently waiting in line for charging UUXN, the average charging efficiency UUXR of the power station and the average charging time UUXT of new energy vehicles are input into the charging waiting time analysis submodule, and the charging user waiting time evaluation value UUX is output based on the influence coefficient UA of the queue number change rate.

[0016] Optionally, the calculation formula of the charging station energy allocation submodule is as follows:

[0017]

[0018] in:

[0019] ECU refers to the optimal energy allocation index of the charging station, argmin refers to the parameter value of ECU that achieves the minimum value in its domain, and UUX irefers to the predicted waiting time of the i-th user, n refers to the number of users currently waiting for charging, EA refers to the waiting time weight of the i-th user, ESA refers to the priority coefficient of the i-th user, m refers to the number of charging piles in the charging station, ECUW j Refers to the unused electric energy, ECUC of the jth charging pile during the optimization period j Refers to the penalty cost of not using electricity at the j-th charging pile, EE j Refers to the energy waste penalty coefficient of the j-th charging pile, Refers to the total waiting time cost, Refers to the total energy waste cost;

[0020] The processing process of the charging station energy allocation submodule is as follows: the charging user waiting time evaluation value UUX and the number of users currently waiting for charging n are input into the charging station energy allocation submodule, and the charging station optimal energy allocation index ECU is output based on the number of charging piles m in the charging station.

[0021] Optionally, the calculation formula of the charging station operation benefit submodule is as follows:

[0022]

[0023] in:

[0024] CBQ refers to the operational benefit evaluation value of the charging station, CBQA refers to the number of users successfully served during the evaluation period, and CBQB refers to the total number of users arriving at the charging station during the evaluation period; CBQC refers to the maximum number of queues that the charging station can accommodate, CA refers to the impact coefficient one, CBQD refers to the fixed operating cost of the charging station, CB refers to the cost coefficient per unit of unused electricity, CBQS refers to the total revenue of the charging station, CC refers to the impact coefficient two, and (1-UUXN / CBQC) refers to user satisfaction. Refers to the total unused power of all charging piles during the optimization period;

[0025] The processing process of the charging station operation efficiency submodule is as follows: the number of new energy vehicles currently waiting to be charged UUXN and the unused electric energy ECUW of the j-th charging pile in the optimization period are combined. j The input is sent to the charging station operation benefit submodule, and the charging station operation benefit evaluation value CBQ is output based on the maximum number of queues CBQC that the charging station can accommodate and the fixed operation cost CBQD of the charging station.

[0026] Optionally, the management module adjusts the queue management and reservation service of the charging station based on the charging user waiting time evaluation value UUX to optimize the user waiting time, the management module optimizes the energy allocation strategy of the charging pile based on the charging station optimal energy allocation index ECU to improve energy utilization efficiency, and the management module evaluates the operating efficiency of the charging station based on the operating benefit evaluation value CBQ of the charging station, and formulates targeted improvement measures.

[0027] Optionally, the data processing module is specifically for cleaning and verifying the collected data to ensure the accuracy and completeness of the data, and organizing the data to meet the input of the subsequent calculation module, and then uploading the processed data to the database for storage.

[0028] Optionally, the data acquisition module is to install sensors and data acquisition equipment at each charging pile, queuing area and user entrance of the new energy charging station.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The present invention outputs the charging user waiting time evaluation value UUX through the charging waiting time analysis submodule. This submodule collects and analyzes key information such as historical charging data, charging pile usage frequency, and user arrival pattern, and can accurately predict the waiting time of users after arriving at the charging station in different time periods. Based on the prediction results, the charging station can schedule resources in advance. This submodule can help the charging station optimize resource scheduling and improve user experience. It can also provide charging stations with data support for user behavior analysis, which helps charging stations better understand user needs and formulate more personalized service strategies. By accurately predicting user waiting time, the charging station can schedule and plan resources in advance to effectively alleviate the queuing pressure during peak hours. At the same time, it can also provide users with more accurate charging time expectations to reduce user anxiety and dissatisfaction during the waiting process.

[0031] 2. The present invention outputs the optimal energy distribution index of the charging station through the charging station energy distribution submodule. This submodule dynamically adjusts the charging power and charging time of each charging pile based on the energy efficiency evaluation results of the charging station and the predicted charging demand to achieve optimal energy distribution, and based on the optimized energy distribution, reduces energy waste, improves the utilization rate and charging efficiency of the charging piles, thereby avoiding overload operation of the charging piles during peak hours, extends the service life of the charging piles, and ensures that the charging piles can maintain a certain level of activity during off-peak hours. By optimizing energy distribution, the utilization rate and charging efficiency of the charging piles can be improved, and the load fluctuation of the power grid can be reduced, which has positive significance for the stable operation of the power grid and energy conservation and emission reduction. By optimizing energy distribution, the charging station can ensure that each charging pile can meet the needs of more users during peak hours, while reducing energy waste and idle time of charging piles, and can also balance the load of the power grid, which has positive significance for the stable operation of the power grid and energy conservation and emission reduction.

[0032] 3. The present invention outputs the operating benefit evaluation value of the charging station through the charging station operating benefit sub-module. This sub-module comprehensively evaluates the operating benefit of the charging station by considering multiple factors such as the energy efficiency, charging demand, operating cost and user satisfaction of the charging station. Based on the evaluation results, the charging station manager can formulate a more scientific and reasonable operating strategy, such as adjusting the charging price, optimizing the charging service process, etc. to improve the profitability of the charging station. By regularly evaluating the charging station, problems and deficiencies in operation can be discovered in a timely manner, and targeted improvement measures can be taken to continuously improve the operating benefit.

[0033] 4. The present invention iterates the average charging efficiency of the charging station in the charging waiting time analysis submodule through the operational benefit evaluation value of the charging station. This iterative form realizes more refined management. By quantitatively analyzing the relationship between the operational benefit evaluation value of the charging station and the average charging efficiency of the charging station, the impact of different parameter adjustments on the operational benefit can be understood more accurately, thereby making more detailed adjustments and optimizations. The average charging rate of the charging pile is dynamically adjusted according to the real-time operational benefit evaluation results, thereby more flexibly responding to operational challenges in different time periods and different charging demands. This iterative method facilitates improving the operational efficiency and service quality of the charging station and provides a scientific basis for management decisions of the charging station. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A flow chart of the method steps of this management service system;

[0035] Figure 2 This is a schematic diagram of the overall structure of the management service system;

[0036] Figure 3 This is a structural diagram of the computing module in this management service system. DETAILED DESCRIPTION

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] This management service system is different from existing management service systems. Existing management service systems lack a scientific prediction system and cannot accurately estimate the waiting time after users arrive at charging stations at different time periods, resulting in poor user experience, especially during peak hours. Some charging piles are overloaded during peak hours, while others are idle, resulting in resource waste. In addition, existing technologies lack effective energy allocation strategies and cannot achieve balanced utilization of charging piles.

[0039] The module of the management service system can accurately predict user waiting time, thereby planning and managing charging pile resources in advance, reducing user waiting time, and improving the charging experience. The present invention can calculate and analyze the optimal energy allocation strategy, achieve balanced utilization of charging piles, avoid resource waste and improve energy utilization efficiency. In addition, the present invention can evaluate the operating benefits of the charging station and formulate targeted improvement measures based on the evaluation results, thereby improving the revenue and cost control capabilities of the charging station and maximizing the operating benefits.

[0040] Example 1: Please refer to Figures 1 to 3 , this implementation provides a management service system method, including:

[0041] Data collection module: collects the charging rate of the charging pile, the charging time of the charging pile, the time when the charging user arrives, the number of new energy vehicles currently waiting in line for charging, and the current waiting time for charging, and saves the collected data into the database;

[0042] Data processing module: cleans, verifies and organizes the charging rate of the charging pile, the charging time of the charging pile, the arrival time of the charging user, the number of new energy vehicles currently waiting in line for charging and the current queuing time for charging, and outputs the number of new energy vehicles currently waiting in line for charging UUXN, the number of users currently waiting for charging n, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT;

[0043] Calculation module: The number of new energy vehicles currently waiting to charge UUXN, the number of users currently waiting to charge n, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT are input. The calculation module outputs the charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the charging station operation benefit evaluation value CBQ;

[0044] Management module: The charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the charging station operation benefit evaluation value CBQ are input, and the management module generates corresponding reports and visualization interfaces for management personnel to review and analyze;

[0045] The calculation module includes: charging waiting time analysis submodule, charging station energy allocation submodule and charging station operation benefit submodule.

[0046] In this embodiment, the calculation module in this management service system is based on three groups of submodules. From user waiting time prediction and energy allocation optimization to charging station operational efficiency evaluation, it comprehensively covers all aspects of new energy charging pile management and services, and has excellent comprehensiveness. Based on a large amount of historical data and scientific prediction models, this system can accurately predict user waiting time, optimize energy allocation, and evaluate charging station operational efficiency, providing scientific decision-making support for charging station managers. This system is scientific and has not only theoretical value but also strong practicality. By applying this method, charging stations can significantly improve user experience, increase operational efficiency and profitability, provide strong support for the sustainable development of the new energy vehicle industry, and have excellent practicality.

[0047] See also Figures 1 to 3 ,The processing process of the charging waiting time analysis submodule is as follows:

[0048]

[0049] in:

[0050] UUX refers to the charging user waiting time evaluation value, and UUXN refers to the number of new energy vehicles currently waiting in line for charging;

[0051] UUXR refers to the average charging efficiency of a charging station, which is the number of cars that can be charged per hour. It is an important indicator of the service capacity of a charging station.

[0052] UUXT refers to the average charging time of new energy vehicles, that is, the average time required for each vehicle to charge from the start to the completion of charging;

[0053] UA refers to the influence coefficient of the queue number change rate;

[0054] UUXA refers to the difference between the current queue number and the queue number in the previous time period, indicating the change in the number of queue members;

[0055] UB refers to the charging peak adjustment factor, which is used to adjust the predicted value of user waiting time during the peak period to reflect the additional impact of the peak period on waiting time;

[0056] UUXP refers to the additional waiting time during peak hours. If you are currently in the peak charging period, additional time will be required;

[0057] UUXN / UUXR refers to the intensity value of the charging station's service capacity, which indicates the average charging rate that each user can obtain under the current number of people in the queue;

[0058] The processing process of the charging waiting time analysis submodule is as follows: the number of new energy vehicles currently waiting in line for charging UUXN, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT are input into the charging waiting time analysis submodule, and the charging user waiting time evaluation value UUX is output based on the influence coefficient UA of the queue number change rate.

[0059] In this embodiment: this submodule reflects the difference between the current queue number and the queue number in the previous time period based on the difference UUXA between the current queue number and the queue number in the previous time period, that is, the dynamic change of the queue number, and can more accurately predict the user waiting time. When the queue number increases sharply, the predicted waiting time will also increase accordingly, thereby reminding the management personnel to take timely measures, such as adding charging piles, optimizing the charging process, etc., to alleviate the queuing pressure. The introduction of the charging peak adjustment coefficient UB and the additional waiting time UUXP during the peak period jointly reflects the congestion situation of the charging station during the peak period, which can more realistically reflect the waiting time of users during the peak period, and help management personnel to formulate targeted peak response strategies, such as opening more charging piles in advance, providing appointment charging services, etc., to improve user satisfaction and charging efficiency;

[0060] This submodule collects and analyzes key information such as historical charging data, frequency of charging pile usage, and user arrival patterns, and can accurately predict the waiting time of users after arriving at charging stations in different time periods. Based on the prediction results, the charging station can schedule resources in advance, such as adding temporary charging piles or guiding users to other charging stations during peak hours, effectively alleviating queuing pressure, reducing user waiting time, improving user charging experience, and enhancing user satisfaction and loyalty to charging services. This submodule can not only help charging stations optimize resource scheduling and improve user experience, but also provide charging stations with data support for user behavior analysis, which helps charging stations better understand user needs and formulate more personalized service strategies. By accurately predicting user waiting time, charging stations can schedule and plan resources in advance, effectively alleviating queuing pressure during peak hours, and at the same time provide users with more accurate charging time expectations, reducing user anxiety and dissatisfaction during the waiting process.

[0061] See also Figures 1 to 3 ,The processing process of the power station energy distribution submodule is as follows:

[0062]

[0063] in:

[0064] ECU refers to the optimal energy allocation index of the charging station, which indicates the energy allocation that achieves the best balance between meeting user needs and reducing energy waste;

[0065] argmin refers to the parameter value that the ECU achieves the minimum value in its domain;

[0066] UUX i Refers to the predicted waiting time of the i-th user, which is used to measure the cost of the user's waiting time;

[0067] n refers to the number of users currently waiting to charge;

[0068] EA refers to the waiting time weight of the i-th user, which reflects the sensitivity or importance of waiting time for different users;

[0069] ESA refers to the priority coefficient of the i-th user, which is used to adjust the waiting time cost of different users to reflect the impact of factors such as user type and membership level on energy allocation;

[0070] m refers to the number of charging piles in the charging station;

[0071] ECUW j Refers to the unused electric energy of the jth charging pile during the optimization period, indicating the energy waste of the charging pile;

[0072] ECUC j Refers to the penalty cost of not using electricity at the j-th charging station, which is used to measure the economic loss of energy waste;

[0073] EE j Refers to the energy waste penalty coefficient of the jth charging pile, which is used to adjust the energy waste cost of different charging piles to reflect the impact of factors such as charging pile type and capacity on energy distribution;

[0074] Refers to the total waiting time cost, which is the sum of the waiting time costs of all users. The lower the total cost, the better the energy allocation scheme is in meeting user needs;

[0075] Refers to the total energy waste cost, which is the sum of the energy waste costs of all charging piles. The lower the total cost, the better the energy allocation plan is in reducing energy waste;

[0076] The processing process of the charging station energy allocation submodule is as follows: the charging user waiting time evaluation value UUX and the number of users currently waiting for charging n are input into the charging station energy allocation submodule, and the charging station optimal energy allocation index ECU is output based on the number of charging piles m in the charging station.

[0077] In this embodiment: In this submodule, ECUW j 、ECUC j and EE jThe combined operation reflects the penalty cost of unused electricity from charging piles during the optimization period, which can motivate managers to optimize energy allocation strategies, reduce the idle time of charging piles, and improve energy efficiency. At the same time, this is also in line with the concept of green and sustainable development and helps promote the healthy development of the new energy vehicle industry. The priority coefficient ESA of the i-th user reflects the priority of different user types or membership levels in energy allocation, which can more flexibly meet the needs of different users, such as providing priority charging services for emergency vehicles or high-value users, thereby improving user satisfaction and loyalty.

[0078] This submodule dynamically adjusts the charging power and charging time of each charging pile according to the energy efficiency evaluation results of the charging station and the predicted charging demand to achieve optimal energy distribution. By optimizing energy distribution, it reduces energy waste and improves the utilization rate and charging efficiency of charging piles to avoid overload operation of charging piles during peak hours, extend the service life of charging piles, and ensure that charging piles can maintain a certain level of activity during off-peak hours. By optimizing energy distribution, not only can the utilization rate and charging efficiency of charging piles be improved, but also the load fluctuation of the power grid can be reduced, which has positive significance for the stable operation of the power grid and energy conservation and emission reduction. By optimizing energy distribution, the charging station can ensure that each charging pile can meet the needs of more users during peak hours, while reducing energy waste and idle time of charging piles; in addition, it can also balance the load of the power grid, which has positive significance for the stable operation of the power grid and energy conservation and emission reduction.

[0079] See also Figures 1 to 3 ,The processing process of the charging station operation benefit submodule is as follows:

[0080]

[0081] in:

[0082] CBQ refers to the operational benefit evaluation value of a charging station, which is used to measure the economic benefits and service quality of a charging station;

[0083] CBQA refers to the number of users successfully served during the evaluation period, indicating the service capability of the charging station;

[0084] CBQB refers to the total number of users arriving at the charging station during the evaluation period, reflecting the passenger flow of the charging station;

[0085] CBQC refers to the maximum number of queues that a charging station can accommodate, CA refers to the impact coefficient 1, and CBQD refers to the fixed operating cost of the charging station;

[0086] CB refers to the cost coefficient per unit of unused electricity, which indicates how much the operating cost increases for each additional unit of unused electricity;

[0087] CBQS refers to the total revenue of the charging station, CC refers to the impact coefficient two;

[0088] (1-UUXN / CBQC) refers to user satisfaction. When UUXN=0, the highest satisfaction is 1, and when UUXN=CBQC, the lowest satisfaction is 0.

[0089] Refers to the total unused power of all charging piles during the optimization period;

[0090] The processing process of the charging station operation efficiency submodule is as follows: the number of new energy vehicles currently waiting to be charged UUXN and the unused electric energy ECUW of the j-th charging pile in the optimization period are calculated as follows: j The input is sent to the charging station operation benefit submodule, and the charging station operation benefit evaluation value CBQ is output based on the maximum number of queues CBQC that the charging station can accommodate and the fixed operation cost CBQD of the charging station.

[0091] In this embodiment, this submodule introduces user satisfaction to reflect the user's overall evaluation of the charging station service, enabling this submodule to more comprehensively evaluate the operational efficiency of the charging station. Improving user satisfaction means better service quality and higher user loyalty, which helps attract more users to charge and increase the charging station's revenue and market share. The fixed operating cost CBQD of the charging station reflects the various expenses incurred during the operation of the charging station, enabling this submodule to more accurately evaluate the profitability of the charging station. Managers can adjust operational strategies in a timely manner according to changes in operating costs, such as optimizing charging prices and reducing energy consumption, to improve the operational efficiency and economic benefits of the charging station.

[0092] This sub-module comprehensively considers multiple factors such as the energy efficiency, charging demand, operating costs, and user satisfaction of the charging station, and conducts a comprehensive assessment of the operating benefits of the charging station. Based on the assessment results, the charging station manager can formulate more scientific and reasonable operating strategies, such as adjusting charging prices, optimizing charging service processes, etc., to improve the profitability of the charging station. Through regular assessments, the charging station can promptly identify problems and deficiencies in operation, take targeted improvement measures, and continuously improve operational benefits.

[0093] It is worth noting that the average charging efficiency UUXR of the charging station in the charging waiting time analysis submodule is iterated by calculating the operating benefit evaluation value CBQ of the charging station and the expected operating benefit value CBQW of the charging station, so that UUXR is iterated in a loop, thereby continuously optimizing the charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the operating benefit evaluation value CBQ of the charging station. The specific working process is as follows:

[0094] First up: UUXRnew =UUXR old +β×(CBQW-CBQ);

[0095] Second: Set the iteration termination condition:

[0096] Termination condition 1: The number of iterations is 100;

[0097] Termination condition 2: |CBQ new -CBQ old |<0.002;

[0098] in:

[0099] UUXR new Refers to the average charging efficiency of the charging station after iteration, UUXR old refers to the average charging efficiency of the charging station before iteration, β refers to the adjustment step, CBQW refers to the expected value of the operating benefit of the charging station, CBQ new Refers to the operational benefit evaluation value of the charging station after iteration, CBQ old Refers to the operational benefit evaluation value of the charging station before iteration.

[0100] In this embodiment: the average charging rate UUXR of the charging pile in the charging waiting time analysis submodule is iterated by calculating the result CBQ. This iterative method has significant substantial contributions and effects. From the perspective of management service methods, this iterative method realizes a dynamic optimization and feedback mechanism. Traditional charging station management services may rely more on static rules or preset parameters, while this method can dynamically adjust the average charging rate UUXR of the charging pile according to the real-time operational benefit evaluation result CBQ, so as to more flexibly respond to operational challenges under different time periods and different charging demands. This iterative method helps to improve the operational efficiency and service quality of the charging station. By continuously optimizing UUXR, charging resources can be allocated more effectively, user waiting time can be reduced, and user satisfaction can be improved. At the same time, it also helps to reduce operating costs and improve the profitability of charging stations. This iterative method also provides a scientific basis for management decisions of charging stations. By quantitatively analyzing CBQ and UUXR By understanding the relationship between them, managers can more clearly understand the impact of different parameter adjustments on operational benefits, and thus make more informed decisions. After iteration, the average charging rate UUXR of the charging pile has a significant optimization and effect on the calculation result UUX of the charging waiting time analysis submodule. From the formula itself, UUX and UUXR are inversely proportional, that is, when UUXR increases, the same number of queued vehicles can be charged in a shorter time, thereby reducing user waiting time. This optimization effect is also significant in actual situations. By increasing the charging rate, the user's charging needs can be met more quickly, the phenomenon of queuing can be reduced, and the traffic capacity and service quality of the charging station can be improved. The iterative UUXR also helps to balance the operational benefits and user satisfaction of the charging station. While pursuing efficient operation, it is also necessary to consider the user's charging experience and satisfaction. By reasonably adjusting UUXR, while ensuring operational benefits, the user's waiting time can be reduced as much as possible, and user satisfaction can be improved.

[0101] In summary, this iterative form achieves more refined management. By quantitatively analyzing the relationship between CBQ and UUXR, we can more accurately understand the impact of different parameter adjustments on operational benefits, thereby making more detailed adjustments and optimizations. Secondly, it improves the flexibility and adaptability of management. Traditional charging station management services may rely more on fixed rules and parameter settings, while this method can dynamically adjust parameters based on real-time operational benefit evaluation results, and respond more flexibly to operational challenges in different situations. Finally, it helps to promote the intelligence and automation of charging station management. By introducing this iterative method, the intelligence and automation of charging station management can be gradually realized. Automation reduces the cost and risk of manual intervention and improves management efficiency and accuracy. CBQ and UUXR are closely correlated. As an evaluation metric for charging station operational efficiency, CBQ reflects the comprehensive performance of a charging station in terms of operational efficiency, service quality, and profitability over a specific time period. UUXR, a key factor influencing a charging station's operational efficiency and service quality, directly determines the station's capacity and the user's charging experience. The targeted role of iterating UUXR through CBQ lies in its ability to dynamically adjust its value based on real-time operational efficiency evaluation results, thereby optimizing the station's operational efficiency and service quality. Specifically, a low CBQ value may indicate low operational efficiency or poor service quality. In this case, increasing UUXR can improve the station's capacity and service quality. However, a high CBQ value can appropriately reduce UUXR to save operating costs or improve user satisfaction. This targeted, iterative approach helps maximize charging station operational efficiency.

[0102] In the specific implementation process, multiple sub-modules in this method are used to form a management service system. By inputting the number of new energy vehicles currently waiting in line for charging UUXN, the average charging efficiency UUXR of the power station, and the average charging time UUXT of new energy vehicles into the charging waiting time analysis sub-module, the charging user waiting time evaluation value UUX is output. This sub-module collects and analyzes key information such as historical charging data, frequency of charging pile use, and user arrival pattern, and can accurately predict the waiting time of users after arriving at the charging station in different time periods, and based on the prediction results, the charging station can schedule resources in advance. This sub-module can not only help the charging station optimize resource scheduling and improve user experience, but also provide charging stations with data support for user behavior analysis, which helps charging stations better understand user needs and formulate more personalized service strategies. By accurately predicting user waiting time, the charging station can schedule and plan resources in advance, effectively alleviating the queuing pressure during peak hours, and at the same time provide users with more accurate charging time expectations to reduce user anxiety and dissatisfaction during the waiting process.

[0103] By inputting the charging user waiting time evaluation value UUX and the number of users currently waiting for charging n into the charging station energy distribution submodule, the optimal energy distribution index ECU of the charging station is output. This submodule dynamically adjusts the charging power and charging time of each charging pile according to the energy efficiency evaluation result of the charging station and the predicted charging demand to achieve the optimal distribution of energy, and reduces energy waste by optimizing energy distribution, improves the utilization rate and charging efficiency of the charging pile to avoid overload operation of the charging pile during peak hours, extends the service life of the charging pile while ensuring that the charging pile can maintain a certain level of activity during off-peak hours. By optimizing energy distribution, not only can the utilization rate and charging efficiency of the charging pile be improved, but also the load fluctuation of the power grid can be reduced, which has positive significance for the stable operation of the power grid and energy conservation and emission reduction. By optimizing energy distribution, the charging station can ensure that each charging pile can meet the needs of more users during peak hours, while reducing energy waste and idle time of the charging pile. In addition, it can balance the load of the power grid, which has positive significance for the stable operation of the power grid and energy conservation and emission reduction.

[0104] By calculating the number of new energy vehicles currently waiting in line for charging UUXN and the unused electric energy ECUW of the j-th charging pile during the optimization period j Input to the charging station operation benefit submodule, and output the charging station operation benefit evaluation value CBQ. This submodule comprehensively evaluates the charging station's operation benefit by considering multiple factors such as the charging station's energy efficiency, charging demand, operating costs, and user satisfaction. Based on the evaluation results, charging station managers can formulate more scientific and reasonable operation strategies, such as adjusting charging prices and optimizing charging service processes to improve the profitability of charging stations. By regularly evaluating charging stations, problems and deficiencies in operation can be discovered in a timely manner, and targeted improvement measures can be taken to continuously improve operational benefits.

[0105] By calculating the operating benefit evaluation value CBQ of the charging station and the expected operating benefit value CBQW of the charging station, the average charging efficiency UUXR of the charging station in the charging waiting time analysis submodule is iterated, so that UUXR is iterated cyclically. This iterative form realizes more refined management. By quantitatively analyzing the relationship between CBQ and UUXR, the impact of different parameter adjustments on operating benefits can be understood more accurately, so as to make more detailed adjustments and optimizations. According to the real-time operating benefit evaluation results CBQ, the average charging rate UUXR of the charging pile is dynamically adjusted, so as to more flexibly respond to operational challenges under different time periods and different charging demands. This iterative method helps to improve the operating efficiency and service quality of the charging station. This iterative method also provides a scientific basis for the management decision-making of the charging station. The targeted role of iterating UUXR through CBQ is that the value of UUXR can be dynamically adjusted according to the real-time operating benefit evaluation results, so as to optimize the operating efficiency and service quality of the charging station.

[0106] This allows the various sub-modules to cooperate with each other in calculations, and to perform overall cycles and iterations, so that the overall system has the effect of automatic optimization and updating, and thus better adaptability.

[0107] Example 2: Please refer to Figure 1 、 Figure 2 and Figure 3 The management module adjusts the queue management and reservation service of the charging station based on the charging user waiting time evaluation value UUX to optimize the user waiting time. The management module optimizes the energy allocation strategy of the charging pile based on the charging station optimal energy allocation index ECU to improve energy utilization efficiency. The management module evaluates the operating efficiency of the charging station based on the operating benefit evaluation value CBQ of the charging station and formulates targeted improvement measures. The data processing module specifically cleans and verifies the collected data to ensure the accuracy and completeness of the data, and organizes the data to meet the input of the subsequent calculation module, and then uploads the processed data to the database for storage. The data acquisition module installs sensors and data acquisition equipment at each charging pile, queuing area and user entrance of the new energy charging station.

[0108] In this embodiment: the data acquisition module is equipped with multiple sensors, specifically: a charging status sensor, which is used to monitor the charging status of the charging pile in real time; an energy metering sensor, which is used to accurately measure the electric energy output by the charging pile to help the charging station manager understand the energy consumption and efficiency of the charging pile; a vehicle identification sensor, which is used to automatically identify vehicles entering the queuing area and record information such as vehicle type and arrival time; an occupancy sensor, such as an infrared sensor or a pressure sensor, which is used to detect whether the charging parking space is occupied; a crowd counter, such as an infrared beam sensor and video analysis technology, which is used to count the number of users entering the charging station and the user's arrival time; a user identity recognition device, such as a membership card reader and a QR code scanner, which is used to identify the user's identity, such as members and non-members, in order to provide users with personalized services, and at the same time collect user type information to provide user portrait data for the charging station manager; a temperature and humidity sensor, which is used to monitor the temperature and humidity in the charging station to ensure that the charging pile and battery are operating within an appropriate range of conditions.

[0109] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Management service system, characterized by: include: Data collection module: collects the charging rate of the charging pile, the charging time of the charging pile, the time when the charging user arrives, the number of new energy vehicles currently waiting in line for charging, and the current waiting time for charging, and saves the collected data into the database; Data processing module: cleans, verifies and organizes the charging rate of the charging pile, the charging time of the charging pile, the arrival time of the charging user, the number of new energy vehicles currently waiting in line for charging and the current queuing time for charging, and outputs the number of new energy vehicles currently waiting in line for charging UUXN, the number of users currently waiting for charging n, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT; Calculation module: The number of new energy vehicles currently waiting to charge UUXN, the number of users currently waiting to charge n, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT are input. The calculation module outputs the charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the charging station operation benefit evaluation value CBQ; Management module: The charging user waiting time evaluation value UUX, the charging station optimal energy allocation index ECU and the charging station operation benefit evaluation value CBQ are input, and the management module generates corresponding reports and visualization interfaces for management personnel to view and analyze.

2. The management service system according to claim 1, characterized in that: The calculation module includes: a charging waiting time analysis submodule, a charging station energy allocation submodule and a charging station operation benefit submodule.

3. The management service system according to claim 2, characterized in that: The calculation formula of the charging waiting time analysis submodule is as follows: in: UUX refers to the evaluation value of the waiting time of charging users, UUXN refers to the number of new energy vehicles currently waiting in line for charging, UUXR refers to the average charging efficiency of the charging station, UUXT refers to the average charging time of new energy vehicles, UA refers to the influence coefficient of the change rate of the number of queues, UUXA refers to the difference between the current number of queues and the number of queues in the previous time period, UB refers to the charging peak adjustment coefficient, UUXP refers to the additional waiting time during the peak period, and UUXN / UUXR refer to the intensity value of the service capacity of the charging station; The processing process of the charging waiting time analysis submodule is as follows: the number of new energy vehicles currently waiting in line for charging UUXN, the average charging efficiency of the power station UUXR and the average charging time of new energy vehicles UUXT are input into the charging waiting time analysis submodule, and the charging user waiting time evaluation value UUX is output based on the influence coefficient UA of the queue number change rate; The calculation formula of the charging station energy distribution submodule is as follows: in: ECU refers to the optimal energy allocation index of the charging station, argmin refers to the parameter value of ECU that achieves the minimum value in its domain, and UUX i refers to the predicted waiting time of the i-th user, n refers to the number of users currently waiting for charging, EA refers to the waiting time weight of the i-th user, ESA refers to the priority coefficient of the i-th user, m refers to the number of charging piles in the charging station, ECUW j Refers to the unused electric energy, ECUC of the jth charging pile during the optimization period j Refers to the penalty cost of not using electricity at the j-th charging pile, EE j Refers to the energy waste penalty coefficient of the j-th charging pile, Refers to the total waiting time cost, Refers to the total energy wastage cost.

4. The management service system according to claim 3, characterized in that: The processing process of the charging station energy allocation submodule is as follows: the charging user waiting time evaluation value UUX and the number of users currently waiting for charging n are input into the charging station energy allocation submodule, and the charging station optimal energy allocation index ECU is output based on the number of charging piles m in the charging station.

5. The management service system according to claim 4, characterized in that: The calculation formula of the charging station operation benefit submodule is as follows: in: CBQ refers to the operational benefit evaluation value of the charging station, CBQA refers to the number of users successfully served during the evaluation period, and CBQB refers to the total number of users arriving at the charging station during the evaluation period; CBQC refers to the maximum number of queues that the charging station can accommodate, CA refers to the impact coefficient one, CBQD refers to the fixed operating cost of the charging station, CB refers to the cost coefficient per unit of unused electricity, CBQS refers to the total revenue of the charging station, CC refers to the impact coefficient two, and (1-UUXN / CBQC) refers to user satisfaction. Refers to the total unused power of all charging piles during the optimization period; The processing process of the charging station operation efficiency submodule is as follows: the number of new energy vehicles currently waiting to be charged UUXN and the unused electric energy ECUW of the j-th charging pile in the optimization period are combined. j The input is sent to the charging station operation benefit submodule, and the charging station operation benefit evaluation value CBQ is output based on the maximum number of queues CBQC that the charging station can accommodate and the fixed operation cost CBQD of the charging station.

6. The management service system according to claim 1, characterized in that: The management module adjusts the queue management and reservation service of the charging station based on the charging user waiting time evaluation value UUX to optimize the user waiting time. The management module optimizes the energy allocation strategy of the charging pile based on the charging station optimal energy allocation index ECU to improve energy utilization efficiency. The management module evaluates the operating efficiency of the charging station based on the operating benefit evaluation value CBQ of the charging station and formulates targeted improvement measures.

7. The management service system according to claim 1, characterized in that: The data processing module is specifically responsible for cleaning and verifying the collected data to ensure the accuracy and completeness of the data, organizing the data to meet the input of the subsequent calculation module, and then uploading the processed data to the database for storage.

8. The management service system according to claim 1, characterized in that: The data acquisition module is to install sensors and data acquisition equipment at each charging pile, queuing area and user entrance of the new energy charging station.

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