New energy charging pile intelligent scheduling system based on big data

Abstract

The invention relates to the technical field of intelligent scheduling, in particular to a new energy charging pile intelligent scheduling system based on big data, and the system comprises an information collection module which is used for obtaining the scheduling period of a charging pile, and collecting the charging amount, power consumption and charging time of the charging pile in each scheduling period; the scheduling identification module is used for identifying a scheduling target and a scheduling price in each scheduling period and checking power consumption distribution in each scheduling period to obtain a consumption evaluation factor; the scheduling adjustment module is used for acquiring a plurality of excitation clusters corresponding to the scheduling targets according to the scheduling targets and the scheduling prices in each scheduling period; each incentive cluster comprises a scheduling target, a scheduling pricing and an incentive effect; clustering the plurality of excitation clusters according to scheduling pricing to obtain excitation factors in each scheduling period, and determining a target set of charging pile scheduling according to the obtained excitation factors; the utilization rate and efficiency of the charging pile are improved, and intelligent scheduling of the charging pile is realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent scheduling technology, and in particular to an intelligent scheduling system for new energy charging piles based on big data. Background Art

[0002] At present, with the popularization of new energy vehicles, the demand for charging piles has increased dramatically. In order to meet the demand for large-scale charging piles, the charging pile management system needs to realize effective management and monitoring of charging piles, including status monitoring, fault diagnosis, real-time scheduling and other functions of charging piles, to ensure the normal operation and service quality of the charging pile network.

[0003] Medium and large charging stations are often composed of multiple charging piles. The charging pile self-organizing network refers to the establishment of a communication network between the charging piles so that the charging piles can exchange data and work together. During the operation of the entire charging station, all charging piles complete real-time interaction of information such as node load and equipment status through the self-organizing network, so that the scheduling server can intelligently schedule each charging pile in the charging station based on this real-time information.

[0004] For example, Chinese patent publication number CN117077872A discloses an intelligent scheduling and management system for new energy electric vehicle charging piles, including an information collection module, an information sorting module, a data analysis module, a data comparison module and an execution module. Through the information collection, sorting module, data analysis module and data comparison module, the system can record and feedback the real-time data of the charging piles, so that managers can better understand the status and needs of the charging piles to optimize the use of the charging piles, improve the efficiency of the charging piles, and reduce the working pressure of the charging piles. The system can intelligently adjust the use of the charging piles to ensure that the charging piles are reasonably utilized during both peak and off-peak periods. The system can dynamically schedule the charging piles according to the actual power pressure conditions to ensure the normal operation of the charging station and the charging needs of the vehicles.

[0005] However, the prior art does not consider how to adjust the scheduling according to the changes in the current goals when the scheduling content and goals change when scheduling charging piles, which makes it easy for charging piles to ignore the priorities between different scheduling goals in the scenario of multi-target scheduling, resulting in confusion in the scheduling order and reduced user experience. Summary of the invention

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: an intelligent scheduling system for new energy charging piles based on big data, including: an information collection module for obtaining the scheduling cycle of the charging piles, and collecting the charging amount, power consumption and charging time of the charging piles in each scheduling cycle.

[0007] The scheduling identification module is used to identify the scheduling target and scheduling pricing in each scheduling cycle, and to check the power consumption distribution in each scheduling cycle to obtain the consumption assessment factor.

[0008] The scheduling adjustment module is used to obtain multiple incentive clusters corresponding to the scheduling target according to the scheduling target and scheduling pricing in each scheduling cycle; each incentive cluster contains the scheduling target, scheduling pricing and incentive effect; the multiple incentive clusters are clustered according to the scheduling pricing to obtain the incentive factor in each scheduling cycle, and the target set of charging pile scheduling is determined based on the obtained incentive factor.

[0009] The scheduling parallel module is used to determine the scheduling order coefficient of the charging piles in multi-objective scheduling according to the target set of the charging pile scheduling, and obtain the evaluation result about the scheduling order.

[0010] The scheduling evaluation module is used to evaluate the current scheduling mode according to the output results of the scheduling identification module, the scheduling adjustment module and the scheduling parallel module, and obtain the comprehensive evaluation results of the charging pile scheduling.

[0011] The beneficial effects of the present invention are as follows: 1. The present invention can monitor the operating status and power consumption distribution of charging piles in real time through the intelligent dispatching system, dynamically adjust the charging power and charging time, and improve the charging efficiency; the system can be flexibly adjusted according to user needs to ensure the reasonable allocation of charging resources and reduce user waiting time.

[0012] 2. The present invention can identify peak and off-peak charging periods, and achieve peak shaving and valley filling through an orderly charging management strategy to optimize energy utilization; charging when the grid load is low and reducing charging when the load is high can reduce charging costs and improve the economic benefits of charging stations; it reduces the waiting time for users and improves user experience.

[0013] 3. The present invention reduces the user's charging cost and promotes the user's willingness to charge through dynamic pricing strategies and incentive mechanisms; and effectively balances the grid load and reduces the pressure during peak hours by adjusting the scheduling strategy in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0015] Figure 1 It is a system framework diagram of the intelligent dispatching system of new energy charging piles based on big data.

[0016] Figure 2 It is a system diagram of the intelligent dispatching system of new energy charging piles based on big data.

[0017] Figure 3It is a flow chart of the dispatching target in the dispatching identification module of the intelligent dispatching system of new energy charging piles based on big data. DETAILED DESCRIPTION

[0018] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0019] See also Figure 1 , Figure 2 The intelligent dispatching system of new energy charging piles based on big data includes: an information acquisition module, a dispatching identification module, a dispatching adjustment module, a dispatching parallel module, and a dispatching evaluation module; the information acquisition module inputs data into the dispatching identification module, the output end of the dispatching identification module is respectively connected with the dispatching adjustment module and the dispatching evaluation module, the output end of the dispatching adjustment module is respectively connected with the dispatching parallel module and the dispatching evaluation module, the output end of the dispatching parallel module is connected with the dispatching evaluation module, and the dispatching evaluation module completes the evaluation of the dispatch after receiving the outputs of the dispatching identification module, the dispatching adjustment module, and the dispatching parallel module.

[0020] The information collection module is used to obtain the scheduling cycle of the charging pile, and collect the charging amount, power consumption and charging time of the charging pile in each scheduling cycle.

[0021] The scheduling identification module is used to identify the scheduling target and scheduling pricing in each scheduling cycle, and to check the power consumption distribution in each scheduling cycle to obtain the consumption assessment factor.

[0022] The scheduling adjustment module is used to obtain multiple incentive clusters corresponding to the scheduling target according to the scheduling target and scheduling pricing in each scheduling cycle; each incentive cluster contains the scheduling target, scheduling pricing and incentive effect; the multiple incentive clusters are clustered according to the scheduling pricing to obtain the incentive factor in each scheduling cycle, and the target set of charging pile scheduling is determined based on the obtained incentive factor.

[0023] The scheduling parallel module is used to determine the scheduling order coefficient of the charging piles in multi-objective scheduling according to the target set of the charging pile scheduling, and obtain the evaluation result about the scheduling order.

[0024] The scheduling evaluation module is used to evaluate the current scheduling mode according to the output results of the scheduling identification module, the scheduling adjustment module and the scheduling parallel module, and obtain the comprehensive evaluation results of the charging pile scheduling.

[0025] The present invention identifies the specific situation of each scheduling of the charging pile, determines whether loss occurs during each scheduling, and clusters the corresponding targets during scheduling, so as to obtain the power used by the corresponding targets at this time and determine whether the scheduling is reasonable. At the same time, it will jointly identify multiple scheduling targets and determine the processing order when multi-target scheduling occurs, ensuring that the charging pile can be adjusted in real time to meet user needs.

[0026] The scheduling cycle is obtained according to the time interval of each intelligent scheduling of the charging pile. For example, the use of the charging pile is arranged every half an hour, an hour or a day. At this time, the scheduling cycle is to obtain data within a time period of corresponding size, and determine how to schedule at this time based on the situation of this part of the data.

[0027] The charging amount of the charging pile is to determine the total cumulative consumption of the charging pile during the scheduling period, and the acquired power consumption is the instantaneous power consumption at the acquisition moment or the corresponding time, so that the currently scheduled power can be accurately identified.

[0028] When identifying the distribution of electricity consumption, the dispatching target and dispatching pricing within the dispatching cycle will be determined first. The dispatching target refers to the need to dispatch user vehicles and the current tasks that need to be dispatched, such as maximizing the utilization rate of charging piles, reducing user waiting time, reducing charging costs, balancing the grid load, reducing queuing time, etc.; the dispatching target at this time needs to clarify the content that needs to be processed at this time; the dispatching pricing represents the current grid electricity price in each dispatching cycle and the charging price that needs to be determined by the dispatching strategy during dispatch, so that the charging cost can be adjusted in combination with the dispatching target, and the charging standard can be adjusted according to the power supply and demand situation, so as to improve the charging experience for different users; the final power consumption distribution is to understand the load situation of the power grid at different times, so as to adjust the dispatching strategy of the charging pile at this time.

[0029] At this time, the method for obtaining the scheduling target and scheduling pricing of the scheduling identification module is as shown below.

[0030] like Figure 3 As shown, the method for obtaining the scheduling target is: A1, obtaining the usage data within the scheduling period, and the usage data includes the number of charging times, charging amount, and charging time of the charging pile.

[0031] A2, based on the usage data within the scheduling cycle, analyze the user's charging habits and identify the peak and trough periods of charging pile usage.

[0032] The usage data obtained at this time will calculate the average value of the number of charging times, charging power and charging time within the scheduling cycle. This average value refers to the average value of adjacent scheduling cycles. The usage data will be input into the array according to the number and position of each charging pile, and a charging curve corresponding to the usage data will be generated according to the order of time points. The charging area will be compared with the data of the current scheduling cycle to identify the charging habits preferred by the user at this time, and according to the peak and trough time periods of the actual use of the charging pile at this time, the preliminary scheduling goals that need to meet the user's habits will be selected at this time, and these scheduling goals will be sorted to find out the scheduling goals with different priorities for achieving the effect at this time. This scheduling goal can be data for only one goal, or it can be data containing multiple goals. For example, the scheduling goal can only maximize the utilization rate of the charging pile; the scheduling goal can also be to achieve the goals of maximizing the utilization rate of the charging pile and reducing the waiting time of the user in order of priority, so as to complete the scheduling goal output at this time.

[0033] A3, calculate the average number of charging times, charging amount, and charging time of each user; classify the users according to the average number of charging times, charging amount, and charging time, and set behavior labels for each user, such as morning peak charging and evening peak charging.

[0034] A4 calculates the user's habit coefficient regarding the number of charging times, charging amount, and charging time under each behavior tag, and uses the habit coefficient, number of charging times, charging amount, and charging time as comprehensive search conditions to retrieve the corresponding preliminary scheduling target from the database.

[0035] The habit coefficient is expressed as normalizing the average values ​​of the number of charging times, the charging amount, and the charging time, and then calculating the weighted sum of the average values ​​of the number of charging times, the charging amount, and the charging time after the normalization, so as to obtain the habit coefficient at this time.

[0036] Since the average value of the above-mentioned charging time is different from the number of charging times and the charging amount, it cannot be directly quantified into a specific value. The charging time at this time will be expressed as the value of the length of the time period from the start of charging to the completion of charging. At the same time, when conducting a comprehensive search, the peak and valley periods of the use of the charging piles need to be used as initial conditions to find the corresponding adjustment targets when charging is frequent and infrequent. At this time, during the comprehensive search, it is necessary to compare the current number of charging times, charging amount, charging time, and habit coefficient values ​​with the values ​​in the database to obtain the confidence levels in turn. At this time, the confidence levels are required to be 0.6, 0.7, 0.5, and 0.7, respectively, according to the number of charging times, charging amount, charging time, and habit coefficient settings. When the confidence level meets the requirements, the preliminary scheduling targets for the corresponding data at this time are output. The preliminary scheduling targets output at this time are presented in the form of a list, containing multiple preliminary scheduling targets, and the preliminary scheduling targets will be set in the database in advance to facilitate subsequent selection and retrieval.

[0037] A5, prioritize the sub-elements in the preliminary scheduling target, obtain the importance ranking of different preliminary scheduling targets, and output them as the scheduling target in the scheduling identification module.

[0038] At this time, the sub-elements in the preliminary scheduling target can be sorted according to different preliminary scheduling targets. For example, when maximizing the utilization rate of charging piles, reducing user waiting time, reducing charging costs, balancing the grid load, and reducing queuing time mentioned above, the sub-elements in the preliminary scheduling target can be expressed as shown below.

[0039] Maximize the utilization rate of charging piles: utilization rate, average usage time, and idle time; utilization rate: the proportion of the usage time of the charging pile in a certain time period to the total time; average usage time: the average usage time of each charging pile in a certain period of time; idle time: the total time that the charging pile is not used.

[0040] Reduce user waiting time: average waiting time, maximum waiting time, and waiting queue length; Average waiting time: the average waiting time from when a user arrives at the charging station to when charging starts; Maximum waiting time: the longest time a user waits for charging; Waiting queue length: the number of users waiting in line for charging.

[0041] Reduce charging costs: average charging cost, total charging cost, electricity price fluctuations; average charging cost: the average cost of charging for each user; total charging cost: the total charging cost of all users in a certain period of time; electricity price fluctuations: the change in electricity prices in different time periods.

[0042] Balanced grid load: grid load distribution, peak load, average load; grid load distribution: the load of the grid in different time periods; peak load: the highest load of the grid in a certain time period; average load: the average load of the grid in a certain time period.

[0043] Reduce queuing time: queuing time, queue length, average number of people in queue; queuing time: the actual waiting time from when the user arrives at the charging station to when charging starts; queue length: the number of users waiting for charging; average number of people in queue: the average number of users waiting in line for charging within a certain period of time.

[0044] The priority at this time can be as shown in Table 1 below.

[0045] Table 1 Priority list

[0046]

[0047]

[0048] At this time, the sub-elements in the preliminary scheduling target are sorted according to the above form, and after sorting for different preliminary scheduling targets, the importance ranking can be obtained. The importance ranking at this time is expressed as a sorted list. At this time, the scheduling target is output according to the priority situation; at this time, the content included in the sub-elements in the scheduling target is the content of the sub-elements in the preliminary scheduling target.

[0049] The method for obtaining dispatch pricing is to obtain real-time electricity price information, take the dispatch target as input based on the real-time electricity price information, determine the dispatch factor corresponding to the dispatch target, and obtain the dispatch pricing based on the dispatch factor and real-time electricity price information.

[0050] The value of the scheduling factor at this time is expressed as the average value of the sub-elements in the scheduling target. The composition of the sub-elements of the scheduling target here is consistent with the content in the above preliminary scheduling target; the number of scheduling factors is consistent with the number of sub-elements in the scheduling target.

[0051] Then the dispatch pricing at this time can be expressed as shown below.

[0052] Where SP′ represents the dispatch pricing, SP represents the value of the real-time electricity price information, and w i represents the weight of the i-th scheduling factor, F i Indicates the value of the i-th dispatch factor, n indicates the number of dispatch factors, and the value of i ranges from 1 to n. During the calculation, the real-time electricity price information, the weight of the dispatch factor, and the value of the dispatch factor will be normalized in advance. At this time, the output dispatch pricing is more inclined to a percentage type value. This value can be used to determine whether the price needs to be adjusted at this time.

[0053] For example, if the dispatch pricing is close to 1, it means that the pricing is not much different from the implemented electricity price. This means that the system does not need to adjust or restrict users. When it is close to the range of 0.4-0.8, it means that the system needs to incentivize users to optimize resource utilization. When the dispatch pricing is greater than 1.1, it means that the grid load is high, and it is hoped that adjustments can be made to limit user charging to avoid overload.

[0054] The purpose of checking the power consumption distribution within each scheduling cycle is to determine whether the current power consumption trend is consistent with the power consumption trend in the historical data; by checking, it can be found whether there is an abnormality in the current power consumption and identify the corresponding changes; the power consumption distribution will construct a coordinate system according to the time and the power consumption value, with time as the vertical coordinate and the power consumption value as the horizontal coordinate to form a coordinate system, and calculate the slope value of the corresponding point in the power consumption distribution at this time.

[0055] For example, according to the scheduling target and scheduling pricing within the scheduling cycle, the power consumption distribution within the corresponding scheduling cycle is selected, and the current power consumption distribution is compared with the power consumption distribution at the same time in the historical data to obtain the consumption assessment factor corresponding to the power consumption distribution under each corresponding scheduling target and scheduling pricing; at this time, according to the number of scheduling factors in the scheduling pricing, the slope value corresponding to the power consumption distribution under the corresponding scheduling factor is obtained. This slope value indicates that the value of the power consumption corresponding to the scheduling factor is obtained at the corresponding time, and the slope value of the current power consumption distribution is compared with the standard value of the slope value in the historical data to obtain the consumption assessment factor at this time; at this time, the standard value of the slope value in the historical data is expressed as the standard deviation of the slope value in the historical data.

[0056] The consumption assessment factors are shown below.

[0057] Among them, CEF represents the consumption evaluation factor, S i represents the slope value of the power consumption distribution corresponding to the ith scheduling factor, S avg represents the average value of the slope value of the power consumption distribution; S std It represents the standard value of the slope value in the historical data, n represents the number of scheduling factors, and the value range of i is 1 to n.

[0058] In the scheduling adjustment module, a data set of incentive clusters is first constructed. Each incentive cluster contains scheduling targets, scheduling pricing and incentive effects. The number of incentive clusters at this time represents the length of this set. When multiple incentive clusters are clustered according to scheduling pricing, the incentive clusters are clustered according to the value range of scheduling pricing at this time. The value ranges of scheduling pricing selection at this time are 0-0.4, 0.4-0.8, 0.8-1.0, 1.0-1.1, and 1.1-1.5 respectively. All data are clustered according to this range, and the incentive factor after clustering is calculated at this time to determine the corresponding situation under each cluster category at this time, so as to select the target set that needs to be paid attention to at this time. The incentive factor is represented by the average value of the incentive effect under each cluster category. The output target set is the cluster with the highest incentive effect, and the cluster with the largest incentive factor value is output as the target set.

[0059] In order to make the output target set more in line with the current processing needs, the incentive effect needs to be set and automatically adjusted according to the scene or the corresponding processing method, so as to select the most suitable set of data.

[0060] Then the incentive effect at this time can be expressed as: obtaining the initial value of the incentive effect corresponding to the scheduling target, adjusting the initial value of the incentive effect according to the sub-elements existing in the scheduling target, and obtaining the output incentive effect.

[0061] IE′(j)=ABS{IE(j)-IE(j) 2 ·In(1+α·IE(j))}; where IE′(j) represents the incentive effect of the output corresponding to the j-th scheduling target, IE(j) represents the initial value of the incentive effect corresponding to the j-th scheduling target, α represents the scheduling coefficient, and ABS{} represents the absolute value of the data in the brackets.

[0062] The scheduling coefficient can be expressed as follows.

[0063] Among them, α represents the scheduling coefficient, represents the average value of the initial value of the incentive effect, m represents the number of scheduling targets; the value range of j is 1 to m, and max(IE(j)) represents the maximum value of the initial value of the incentive effect.

[0064] The incentive effect at this time will be adjusted according to the different initial values ​​of the incentive effect. The initial value of the incentive effect is directly affected by the scheduling target. Therefore, when the sub-elements in the scheduling target change, the incentive effect at this time will also be adjusted accordingly, so that it can be more in line with the current scenario, thereby selecting the most appropriate target set and the corresponding scheduling targets that need to be processed.

[0065] When judging the scheduling order in multi-objective scheduling, you can select the priority of the sub-element of the corresponding scheduling target in the target set, the value of the scheduling pricing, and the value of the incentive factor to set the current scheduling order; the target set obtained at this time is a cluster of scheduling pricing within the corresponding value range, which will include multiple scheduling targets. The scheduling target can reflect the corresponding needs of users when the current charging pile is in use. Therefore, you can select the priority of the sub-element in the scheduling target at this time to set the scheduling order. At the same time, the target set will also reflect the values ​​of the scheduling pricing and the incentive factor. At this time, you should comprehensively consider the values ​​of the three values ​​to obtain a scheduling order coefficient for the scheduling order, and use the scheduling order coefficient obtained at this time as the evaluation result of the scheduling order at this time, thereby completing the evaluation of the scheduling order.

[0066] At the same time, the scheduling order will be affected by the value of the scheduling order coefficient, and the part with a larger value of the scheduling order coefficient will be given priority.

[0067] Therefore, the scheduling order coefficient is expressed as shown below. The priority, scheduling price, and incentive factor of the sub-element corresponding to the scheduling target in the target set are obtained to obtain the scheduling order coefficient.

[0068] SSC=β1×PSO+β2×SP′+β3×IF; where SSC represents the scheduling order coefficient, PSO represents the priority of the sub-elements of the scheduling target, SP′ represents the scheduling pricing, IF represents the incentive factor, β1 represents the weight of the priority of the sub-elements of the scheduling target, β2 represents the weight of the scheduling pricing, and β3 represents the weight of the incentive factor; these three weights are set to 0.6, 0.3, and 0.1 in the order of β1, β2, and β3.

[0069] In the scheduling evaluation module, when evaluating based on the output results of the scheduling identification module, the scheduling adjustment module, and the scheduling parallel module, the output result of the scheduling identification module is the consumption evaluation factor, the scheduling adjustment module is the target set including the scheduling target and the scheduling pricing, and the scheduling parallel module is the scheduling order coefficient. When comprehensively evaluating the contents of these three modules, it is necessary to calculate the loss value between the target set and the predicted value, and combine the consumption evaluation factor and the scheduling order coefficient at this time to obtain a comprehensive evaluation result.

[0070] The comprehensive evaluation results are shown below.

[0071] Among them, CEE represents the comprehensive evaluation result, CEF represents the consumption evaluation factor, SSC represents the scheduling order coefficient, and x(k) represents the value of the kth element in the target set. It represents the predicted value of the kth element in the target set, k represents the number of elements in the target set, and the value range of k is 1 to K. At this time, the elements in the target set include not only the scheduling target but also the scheduling pricing. The losses generated by these elements are calculated in turn, which can quantify the losses generated after the current scheduling target is selected, so as to verify the overall comprehensive evaluation effect. λ1 represents the weight of the consumption evaluation factor, λ2 represents the weight of the scheduling order coefficient, λ3 represents the weight of the target set, and e represents the exponential constant. According to the order of consumption evaluation factor, scheduling order coefficient, and target set, the weights can be set to 0.4, 0.4, and 0.2 respectively to quantify the evaluation effect at this time.

[0072] By obtaining this comprehensive evaluation result, we can determine the deficiencies in the scheduling of the charging pile at this time, and then evaluate the overall status of the charging pile based on the multiple coefficients calculated in this part to obtain the overall evaluation result of the charging pile at this time.

[0073] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. The intelligent dispatching system of new energy charging piles based on big data is characterized by: include: The information collection module is used to obtain the scheduling cycle of the charging pile, and collect the charging amount, power consumption and charging time of the charging pile in each scheduling cycle; A dispatch identification module is used to identify the dispatch target and dispatch pricing in each dispatch cycle, and to check the power consumption distribution in each dispatch cycle to obtain the consumption assessment factor; The scheduling adjustment module is used to obtain multiple incentive clusters corresponding to the scheduling target according to the scheduling target and scheduling pricing in each scheduling cycle; each incentive cluster contains the scheduling target, scheduling pricing and incentive effect; multiple incentive clusters are clustered according to the scheduling pricing to obtain the incentive factor in each scheduling cycle, and the target set of charging pile scheduling is determined according to the obtained incentive factor; The scheduling parallel module is used to determine the scheduling order coefficient of the charging piles in multi-objective scheduling according to the target set of the charging pile scheduling, and obtain the evaluation result about the scheduling order; The scheduling evaluation module is used to evaluate the current scheduling mode according to the output results of the scheduling identification module, the scheduling adjustment module and the scheduling parallel module, and obtain the comprehensive evaluation results of the charging pile scheduling.

2. The intelligent dispatching system for new energy charging piles based on big data according to claim 1 is characterized in that: The method for obtaining the scheduling target is to obtain the usage data within the scheduling period, and the usage data includes the number of charging times, charging amount, and charging time of the charging pile; Analyze user charging habits based on usage data within the scheduling cycle and identify peak and low periods of charging pile usage; Calculate the average number of times each user charges, the amount of charge, and the charging time; classify users according to the average number of times, amount of charge, and charging time, and set a behavior label for each user; Calculate the user's habit coefficient of charging times, charging amount, and charging time under each behavior tag, and use the habit coefficient, charging times, charging amount, and charging time as comprehensive search conditions to retrieve the corresponding preliminary scheduling target from the database; The sub-elements in the preliminary scheduling target are prioritized to obtain the importance ranking of different preliminary scheduling targets, and are output as the scheduling target in the scheduling identification module.

3. The intelligent dispatching system for new energy charging piles based on big data according to claim 1 is characterized in that: The method for obtaining dispatch pricing is to obtain real-time electricity price information, take the dispatch target as input based on the real-time electricity price information, determine the dispatch factor corresponding to the dispatch target, and obtain the dispatch pricing based on the dispatch factor and real-time electricity price information.

4. The intelligent dispatching system for new energy charging piles based on big data according to claim 3 is characterized in that: Dispatch pricing can be expressed as follows: Where SP′ represents the dispatch pricing, SP represents the value of the real-time electricity price information, and w i represents the weight of the i-th scheduling factor, F i Represents the value of the i-th scheduling factor, n represents the number of scheduling factors, and the value range of i is 1 to n.

5. According to claim 3, the intelligent dispatching system for new energy charging piles based on big data is characterized in that: The consumption assessment factors are as follows: Among them, CEF represents the consumption evaluation factor, S i represents the slope value of the power consumption distribution corresponding to the ith scheduling factor, S avg represents the average value of the slope value of the power consumption distribution; S std It represents the standard value of the slope value in the historical data, n represents the number of scheduling factors, and the value range of i is 1 to n.

6. The intelligent dispatching system for new energy charging piles based on big data according to claim 1 is characterized in that: When multiple incentive clusters in the dispatch adjustment module are clustered according to the dispatch pricing, the incentive clusters are clustered according to the value range of the dispatch pricing; The incentive factor in each scheduling cycle is expressed as the average value of the incentive effect under each cluster category; the cluster with the largest incentive factor value is output as the target set.

7. The intelligent dispatching system for new energy charging piles based on big data according to claim 1 is characterized in that: The incentive effect can be expressed as follows: obtaining the initial value of the incentive effect corresponding to the scheduling target, adjusting the initial value of the incentive effect according to the sub-elements existing in the scheduling target, and obtaining the output incentive effect; IE′(j)=ABS{IE(j)-IE(j) 2 ·In(1+α·IE(j))}; Among them, IE′(j) represents the incentive effect of the output corresponding to the j-th scheduling target, IE(j) represents the initial value of the incentive effect corresponding to the j-th scheduling target, α represents the scheduling coefficient, and ABS{} represents the absolute value of the data in the brackets.

8. The intelligent dispatching system for new energy charging piles based on big data according to claim 7 is characterized in that: The scheduling coefficient can be expressed as follows; Among them, α represents the scheduling coefficient, represents the average value of the initial value of the incentive effect, m represents the number of scheduling targets; the value range of j is 1 to m, and max(IE(j)) represents the maximum value of the initial value of the incentive effect.

9. The intelligent dispatching system for new energy charging piles based on big data according to claim 1 is characterized in that: The scheduling order coefficient is expressed as shown below. The priority, scheduling price, and incentive factor of the sub-element corresponding to the scheduling target in the target set are obtained to obtain the scheduling order coefficient; SSC=β1×PSO+β2×SP′+β3×IF; Among them, SSC represents the scheduling order coefficient, PSO represents the priority of the sub-elements of the scheduling target, SP′ represents the scheduling pricing, IF represents the incentive factor, β1 represents the weight of the priority of the sub-elements of the scheduling target, β2 represents the weight of the scheduling pricing, and β3 represents the weight of the incentive factor.

10. The intelligent dispatching system for new energy charging piles based on big data according to claim 1 is characterized in that: The comprehensive evaluation results are shown as follows: Among them, CEE represents the comprehensive evaluation result, CEF represents the consumption evaluation factor, SSC represents the scheduling order coefficient, and x(k) represents the value of the kth element in the target set. represents the predicted value of the kth element in the target set, k represents the number of elements in the target set, and the value range of k is 1 to K; λ1 represents the weight of the consumption evaluation factor, λ2 represents the weight of the scheduling order coefficient, λ3 represents the weight of the target set, and e represents the exponential constant.

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