Ordered charging method and device for charging pile and storage medium

Abstract

The invention relates to the field of new energy automobile power supply, and provides a charging pile ordered charging method, which comprises the following steps: collecting a multi-source data set in real time; based on the multi-source data set, generating a dynamic scheduling strategy through a multi-objective optimization algorithm; generating a charging power control instruction set according to the power distribution matrix of each charging pile, and generating a charging sequence scheduling instruction based on the time window mapping table of each charging pile; issuing the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start a charging process; in the execution process of the charging process, monitoring an actual operation parameter set of each charging pile in real time, and verifying the execution consistency of actual operation parameters in the actual operation parameter set and the charging power control instruction set; and dynamically adjusting a power distribution coefficient in the power distribution matrix based on a deviation analysis result of the operation parameter set and the dynamic scheduling strategy, and triggering rolling optimization calculation of a multi-objective optimization algorithm to update the scheduling strategy.

Description

Technical Field

[0001] This application relates to the field of power supply for new energy vehicles, and particularly to an orderly charging method, device and storage medium for charging piles. Background Art

[0002] With the popularization of electric vehicles, the surge in charging demand has led to an exacerbation of the peak-valley difference in the power grid load, and disorderly charging behaviors have caused problems such as transformer overload and three-phase imbalance. Existing charging scheduling methods have defects such as being unable to adapt to the real-time fluctuations of the power grid load, having a large deviation between the actual charging power and the planned value, affecting the safety and stability of the power grid, and low charging efficiency. Summary of the Invention

[0003] This application provides an orderly charging method, device and storage medium for charging piles, which can realize an intelligent charging management system with data-driven, closed-loop control and human-machine collaboration.

[0004] On the one hand, this application provides an orderly charging method for charging piles, and the method includes: Collecting a multi-source data set in real time; Based on the multi-source data set, generating a dynamic scheduling strategy through a multi-objective optimization algorithm, and the dynamic scheduling strategy at least includes a power distribution matrix and a time window mapping table for each charging pile; Generating a charging power control instruction set according to the power distribution matrix, and generating a charging sequence scheduling instruction based on the time window mapping table; Sending the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start the charging process; During the execution of the charging process, real-time monitoring the actual operation parameter set of each charging pile and verifying the execution consistency between the actual operation parameters in the actual operation parameter set and the charging power control instruction set; Based on the deviation analysis result between the operation parameter set and the dynamic scheduling strategy, dynamically adjusting the power distribution coefficient in the power distribution matrix, and triggering the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0005] On the other hand, this application provides an orderly charging device for charging piles, and the device includes: An acquisition module, configured to collect a multi-source data set in real time; A first generation module, configured to generate a dynamic scheduling strategy through a multi-objective optimization algorithm based on the multi-source data set, and the dynamic scheduling strategy at least includes a power distribution matrix and a time window mapping table for each charging pile; A second generation module, configured to generate a charging power control instruction set according to the power distribution matrix, and generate a charging sequence scheduling instruction based on the time window mapping table; A distribution module, configured to send the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start a charging process; A monitoring module, configured to, during the execution of the charging process, monitor a set of actual operating parameters of each charging pile in real time and verify the execution consistency between the actual operating parameters in the set of actual operating parameters and the charging power control instruction set; A triggering module, configured to dynamically adjust the power distribution coefficient in the power distribution matrix based on the deviation analysis result between the set of operating parameters and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0006] In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the technical solution of the above-mentioned method for orderly charging of charging piles are implemented.

[0007] In a fourth aspect, the present application provides a storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of the technical solution of the above-mentioned method for orderly charging of charging piles are implemented.

[0008] As can be seen from the technical solutions provided by the present application above, on the one hand, by dynamically generating a scheduling strategy through a multi-objective optimization algorithm, the power distribution matrix can intelligently balance grid security, user costs, and facility utilization rate, thereby reducing the peak-valley difference of the grid; on the other hand, through the coordinated issuance of charging power control instructions and charging sequence scheduling instructions, combined with real-time operation monitoring and execution verification, it is ensured that the charging process strictly follows the scheduling plan, and the power deviation rate is controlled within a preset threshold, effectively preventing local overload and timing conflicts; in the third aspect, the dynamic adjustment mechanism based on deviation analysis enables the system to quickly respond to load mutations, and the rolling optimization calculation can complete the strategy update in a short time, significantly improving the response speed compared to traditional static scheduling. In summary, the technical solution of the present application can implement an intelligent charging management system that is data-driven, closed-loop controlled, and human-machine collaborative. Description of the Drawings

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0010] Figure 1 It is a flowchart of the method for orderly charging of charging piles provided by the embodiments of the present application; Figure 2 It is a schematic structural diagram of the orderly charging device for charging piles provided by the embodiments of the present application; Figure 3 It is a schematic structure of the electronic device provided by the embodiments of the present application. Detailed implementation manners

[0011] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0012] In this specification, adjectives such as first and second can only be used to distinguish one element or action from another element or action, and do not necessarily require or imply any actual such relationship or order. Where circumstances permit, reference elements or components or steps (etc.) should not be construed as being limited to only one of the elements, components, or steps, but can be one or more of the elements, components, or steps, etc.

[0013] In this specification, for ease of description, the dimensions of the various parts shown in the drawings are not drawn according to actual proportional relationships.

[0014] With the popularization of electric vehicles, the surging charging demand has led to an exacerbation of the peak-valley difference of the power grid load, and disorderly charging behaviors have caused problems such as transformer overload and three-phase imbalance. Existing charging scheduling methods have defects such as being unable to adapt to the real-time fluctuations of the power grid load, a large deviation between the actual charging power and the planned value, affecting the safety and stability of the power grid, and low charging efficiency. The specific manifestations are as follows: 1) Relying on historical data or fixed electricity price period division, it is unable to adapt to the real-time fluctuations of the power grid load, resulting in the disconnection between the scheduling plan and the actual demand; 2) After the charging instruction is issued unidirectionally, there is a lack of an execution verification mechanism, and when the actual charging power deviates greatly from the planned value, it is unable to correct the deviation in a timely manner; 3) Users cannot adjust the charging plan according to personalized needs, which is likely to cause users to interrupt charging by themselves or repeat starting and stopping, disrupting the scheduling order; 4) Focusing on a single goal (such as the lowest cost), ignoring the dynamic balance of power grid safety, charging efficiency, and user cost, it is difficult to achieve global optimization.

[0015] In view of the above problems of the prior art, the present application proposes an orderly charging method for charging piles, and its flowchart is as shown in the appendix Figure 1 as shown, mainly including steps S101 to S106, which are described in detail as follows: Step S101: Real-time collect a multi-source data set.

[0016] Considering that the power grid load fluctuation data is the premise of power distribution, the charging demand parameters are the basis for user-side optimization, and the time-of-use electricity price is a necessary condition for economic calculation. However, historical data or static parameters (such as fixed electricity prices) cannot respond to sudden power grid loads or temporary user demands, resulting in scheduling failures. Therefore, a multi-source data set composed of the above data can be collected in real time. In the embodiments of the present application, the multi-source data set collected in real time mainly includes the real-time load fluctuation data of power grid nodes, the charging demand characteristic parameters of each electric vehicle in the charging request queue, and the dynamic curve of the time-of-use electricity price, etc. Among them, the real-time load fluctuation data of power grid nodes can be collected in real time by intelligent monitoring terminals such as smart meters and RTU devices installed on the outlet side of the power grid transformer and the bus side of the charging station at a sampling rate of about 1000 times per second, and the processed load data containing time stamps is uploaded to the edge computing node through the industrial Ethernet. For the dynamic curve of the time-of-use electricity price, the time-of-use electricity price data of the power grid can be obtained in real time through the API interface of the power trading platform, and then, based on the load prediction results, the electricity price data is dynamically adjusted to generate a dynamic curve data table of the time-of-use electricity price. Finally, the time-of-use electricity price data of the power grid and the generated electricity price dynamic curve data are aligned according to the time stamp to generate a unified dynamic curve of the time-of-use electricity price. It should be noted that considering the multi-source nature of the data, the data in the above multi-source data set can be fused, that is, a time axis is established, millisecond-level time stamps are added to all the collected data, and time deviations are eliminated through data cleaning. Then, the real-time load fluctuation data, the charging demand characteristic parameters, and the dynamic curve of the time-of-use electricity price are integrated into a multi-dimensional data cube according to a time window (for example, 5 minutes).

[0017] As an embodiment of the present application, the charging demand characteristic parameters of each electric vehicle collected from the multi-source charging request queue may be: parsing the current state of charge, the maximum allowable charging power curve uploaded by the in-vehicle battery management system, and the expected full charge time set by the user; obtaining the charging priority flag and the electricity price sensitivity coefficient submitted by the user terminal; and combining the geographical location of the charging pile and the traffic condition data to calculate the estimated arrival time window of each electric vehicle. In the above embodiment, the calculation of the estimated arrival time window of each electric vehicle by combining the geographical location of the charging pile and the traffic condition data can be realized through steps S1011 to S1015, which are described in detail as follows: Step S1011: Synchronously collect the geographical location of the charging pile, the positioning data of the electric vehicle, and the dynamic traffic conditions in real time.

[0018] The above charging pile geographical location includes the access path topology from the main road to the charging pile gun head, which can be obtained through the charging pile management platform. The electric vehicle positioning data includes information such as the latitude, vehicle speed, and remaining cruising range of the electric vehicle, which can be obtained through the on-vehicle GPS positioning data of the electric vehicle. As for the dynamic traffic conditions (including the real-time road congestion index and the average speed of the road section from the current vehicle position to the target charging pile), they can be obtained by accessing the high-precision traffic condition API.

[0019] Step S1012: Based on the charging pile location and the electric vehicle positioning data, generate a candidate path set through a path planning algorithm.

[0020] Among the generated candidate path sets, the weight of each candidate path can be calculated based on the total distance of the path and the average speed of the road section. For example, , where D is the total distance of the path, is the average speed of the road section. In addition, when generating the candidate path set, it is also necessary to exclude paths with a remaining cruising range less than a certain threshold, such as 1.2 times the total distance of the path.

[0021] Step S1013: For each candidate path in the candidate path set, use a prediction model to calculate the time window interval including the confidence level.

[0022] Specifically, by inputting feature data such as historical traffic flow, real-time weather, and special event data into the trained LSTM neural network, perform a rolling prediction of the vehicle speed of the electric vehicle for the traffic conditions within a certain future duration (such as 30 minutes) for each candidate path. According to the predicted vehicle speed data, calculate the estimated arrival time window of each path according to the following rules:

[0023] where is the driving time based on the current vehicle speed of the electric vehicle, is the standard deviation of the historical on-time rate. Then, select the paths with a confidence level > 80% as the main recommended solution, where the confidence level depends on the historical on-time rate and the real-time traffic fluctuation coefficient.

[0024] Step S1014: Filter the candidate paths from the time window interval according to the path preference parameters submitted by the user, and check the availability of the target charging pile within the time window.

[0025] Specifically, if the user terminal marks "avoid highways", then filter the paths containing highways from the candidate path set. Query the remaining available reservation capacity of the target charging pile within the estimated arrival time window. If it is verified that the target charging pile is unavailable within the time window, select a neighboring charging pile and trigger path replanning, that is, re-execute step S1012 to generate a new candidate path set based on the charging pile location and the electric vehicle positioning data through the path planning algorithm.

[0026] Step S1015: When the electric vehicle deviates from the planned path in the candidate path set by more than a threshold or when the traffic conditions have data updates, re-execute steps S1012 to S1014 to generate an updated time window.

[0027] Step S102: Based on the multi-source data set, generate a dynamic scheduling strategy through a multi-objective optimization algorithm, where the dynamic scheduling strategy at least includes the power distribution matrix of each charging pile and the time window mapping table.

[0028] In the prior art, single-objective optimization (such as only minimizing costs) cannot balance grid security and user needs, while multi-objective optimization is an effective way to solve the contradictions of load balancing, cost control, and resource utilization rate. In the embodiments of the present application, the multi-objective optimization algorithm is an effective model for generating a dynamic scheduling strategy based on a multi-source data set. These dynamic scheduling strategies at least include the power distribution matrix of each charging pile and the time window mapping table, and both are the direct basis for subsequent instruction issuance. The lack of either will lead to out-of-control charging processes. For example, only power distribution without time planning may cause charging time conflicts.

[0029] The multi-objective optimization algorithm in the above embodiments can be established based on a multi-source data set, that is, the real-time load fluctuation data of the grid nodes therein, the charging demand characteristic parameters of each electric vehicle in the charging request queue, and the estimated arrival time window of the electric vehicle, etc. Specifically, it includes: based on the real-time load fluctuation data of the grid nodes, establishing the minimization of the peak-valley difference of the grid load as the first objective function; based on the electricity price sensitivity coefficient in the charging demand characteristic parameters, establishing the minimization of the total charging cost of the user group as the second objective function; according to the estimated arrival time window, establishing the maximization of the average utilization rate of the charging facilities as the third objective function; adopting a dynamic weight allocation strategy to adjust the weight coefficients of the first objective function, the second objective function, and the third objective function according to the real-time grid load rate. In the above embodiments, the adjustment of the weight coefficients of the first objective function, the second objective function, and the third objective function can be calculated in real time through a fuzzy logic controller. For example, when the real-time grid load rate is greater than the first preset threshold (such as 85%), set the weight of the first objective function to , set the weight of the second objective function to , set the weight of the third objective function to , where And ; when the real-time grid load rate is less than the first preset threshold (e.g., 40%), adjust the weight of the first objective function to , adjust the second objective function to , adjust the third objective function to , compared with when the real-time grid load rate is greater than the first preset threshold, the weights of each objective function are compared with the previous weights , , And . The dynamic weight mechanism of the above embodiments can solve problems such as focusing on grid safety during peak hours and user costs during valley hours that fixed weights cannot adapt to load rate changes. Compared with the fixed weight scheme, it not only reduces the peak-valley difference of the grid load, but also improves the utilization rate of facilities such as charging piles.

[0030] Furthermore, in order to prevent equipment from being overloaded and burned out, avoid grid harmonic pollution, and ensure the basic rights and interests of charging pile users, in the embodiments of the present application, the constraint conditions of the multi-objective optimization algorithm in the above embodiments can be set, specifically including: setting the transformer load rate not to exceed the preset safety threshold, and the three-phase unbalance degree calculated based on real-time load fluctuation data is lower than the critical value; setting the charging completion time not to be later than the expected full charge time in the charging demand characteristic parameters; and setting the charging power adjustment rate not to exceed the step change rate in the maximum allowable charging power curve. Experiments show that after clarifying the constraint conditions of the multi-objective optimization algorithm, not only the grid accident rate is significantly reduced, but also the algorithm solving speed is significantly improved because the solution space is reduced.

[0031] After adopting the dynamic weight allocation strategy to construct the multi-objective optimization algorithm and clarifying its constraint conditions, a dynamic scheduling strategy can be generated based on the rolling time window mechanism and the preset algorithm. Specifically, a preset duration (e.g., 30 minutes) can be set as the rolling optimization window, and the optimization calculation is triggered every few minutes. Retain the immutability of the charging tasks that have already started, only optimize the tasks in the unexecuted period, and then randomly generate a certain number (e.g., 100 groups) of candidate solutions. Classify the solution set according to the objective function values for the Pareto front, and use simulated binary crossover and polynomial mutation operations to generate offspring; finally, merge the parent and offspring populations, select the first several optimal solutions to enter the next generation of iteration, and finally, generate a dynamic scheduling strategy. From steps S101 and S102 of the above embodiments, it can be seen that by using the fusion analysis of the time-of-use electricity price dynamic curve and the charging demand characteristic parameters, when generating the dynamic scheduling strategy, synchronously optimizing the peak-valley difference of the grid load, the user charging cost, and the utilization rate of charging facilities can reduce the total charging cost of the electric vehicle user group and improve the average utilization rate of charging piles at the same time.

[0032] Step S103: Generate a charging power control instruction set based on the power distribution matrix of each charging pile, and generate a charging sequence scheduling instruction based on the time window mapping table of each charging pile.

[0033] Specifically, generating a charging power control instruction set based on the power distribution matrix of each charging pile can be to perform cubic spline interpolation on the discrete time slice power values in the power distribution matrix (for example, one value every 5 minutes) to generate a continuous power change curve, so that the power change rate does not exceed the allowable step change rate of the battery, and encapsulate the charging power control instruction set, that is, encapsulate the instruction unique identifier, time-power key-value pair, and validity check code, etc., and verify the integrity and source credibility of the instruction through digital signature. As for generating a charging sequence scheduling instruction based on the time window mapping table, it can be implemented through steps S1031 to S1033, and the detailed description is as follows: Step S1031: Detect time window conflicts.

[0034] Specifically, it includes: traversing the time window mapping table to detect the overlap of time windows on the same charging pile. If a conflict is detected (for example, two electric vehicles are assigned to the same charging pile and the time overlaps for more than 5 minutes), start the conflict resolution algorithm, that is, preferentially retain the time window of the high-priority vehicle (such as emergency charging), adopt the time window right-shift strategy for the low-priority vehicle, and calculate the compensation points (such as giving 15 minutes of free charging).

[0035] Step S1032: Plan the start-stop sequence of the charging pile.

[0036] Specifically, it includes: waking up the charging pile and performing self-check several minutes before the start of charging. When the start time arrives, send a charging start instruction and load the power control curve. Several minutes before the end time arrives, send a gentle power reduction instruction (such as linearly reducing from 50kW to 0kW) to avoid battery thermal shock.

[0037] Step S1033: Embed exception handling instructions.

[0038] Specifically, if a certain charging pile does not respond to instructions for several consecutive time slices, it is marked as faulty and the standby charging pile switching process is started; if the grid load mutation exceeds the preset threshold, a global power reduction instruction is inserted and the power of all charging piles is uniformly reduced.

[0039] Further, to address the queue-jumping issue where the traditional first-come-first-served mechanism cannot handle sudden high-priority charging demands (such as ambulance charging) and the problem of increased user complaint rates caused by forced scheduling, the charging sequence scheduling instruction generated based on the time window mapping table in the above embodiments may further include: dividing the charging requests into an emergency charging queue and a flexible charging queue according to the time period priority markers in the time window mapping table; adopting a fixed time window allocation strategy for the emergency charging queue and implementing a dynamic time window bidding mechanism for the flexible charging queue; when a high-priority charging request is detected to be inserted, starting a queue rearrangement compensation algorithm and calculating the integral compensation value for the affected users. The dynamic time window bidding mechanism in the above embodiments may be: pushing optional flexible charging time periods and corresponding electricity price discount rates to the user terminals of electric vehicle users; receiving the time period selection intentions feedback by these users and generating a successful bidding queue; dynamically updating the time period allocation records in the time window mapping table according to the bidding results. In this way, the average response time of high-priority charging requests can be significantly reduced, and the bidding mechanism can also increase the proportion of charging volume during the low grid valley period.

[0040] Step S104: Send the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process.

[0041] Since the charging power control instruction set is generated according to the power distribution matrix of each charging pile, and the charging sequence scheduling instruction is generated based on the time window mapping table, and the power distribution matrix determines "how much electricity to charge", and the time window mapping table determines "when to charge", therefore, sending the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process, the two can cooperate to achieve orderliness in the space-time dimension and prevent users or charging piles from starting spontaneously and disorderly.

[0042] Step S105: During the execution of the charging process, monitor the actual operation parameter set of each charging pile in real time and verify the execution consistency between the actual operation parameters in the actual operation parameter set and the charging power control instruction set.

[0043] Considering that the deviation between the actual operating parameters (such as instantaneous power) and the command value directly affects the power grid safety, when there is a lack of verification link, the charging pile failure or user's illegal operation will cause the dispatching strategy to gradually fail, and the health indicators of the charging pile (such as temperature, insulation impedance) are the premise of charging safety. The traditional method only monitors the power consumption and cannot prevent the safety accidents caused by equipment overload. Therefore, in the embodiment of the present application, during the execution of the charging process, the actual operating parameter sets of each charging pile are monitored in real time, and the execution consistency between the actual operating parameters in the actual operating parameter sets and the charging power control instruction set is verified. Among them, the operating parameter set at least includes instantaneous power value, cumulative charging amount, and equipment health status indicators, etc. Specifically, verifying the execution consistency between the actual operating parameters in the actual operating parameter set and the charging power control instruction set can be: constructing an instruction-response comparison model, and calculating the dynamic time warping (DTW) distance between the actual power curve and the command value; when the DTW distance exceeds the preset tolerance threshold, generating an execution exception event log and triggering a warning signal; performing hierarchical response according to the exception type, including local power correction, charging pile switching, or global strategy recalculation. The hierarchical response in the above embodiment includes a first-level response, a second-level response, and a third-level response. Among them, the first-level response is to perform dynamic power compensation on the instantaneous power deviation to maintain the current charging pile operation, the second-level response is to trigger a charging pile switching instruction for the abnormal equipment health status and update the power distribution matrix, and the third-level response is to start an emergency dispatching strategy for the grid load mutation event and suspend the low-priority charging queue. Compared with the simple threshold comparison (such as alarming when the power deviation exceeds the preset threshold), the DTW algorithm can identify the abnormal time series pattern and predict equipment failures in advance, thus avoiding the waste of resources caused by "one-size-fits-all" shutdown. The local correction can greatly reduce the probability of charging interruption events.

[0044] Step S106: Based on the deviation analysis result of the operating parameter set and the dynamic dispatching strategy, dynamically adjust the power distribution coefficient in the power distribution matrix, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the dispatching strategy.

[0045] On the one hand, if only the actual operation parameter sets of each charging pile are monitored without adjustment, the system will degenerate into an open-loop control and cannot cope with continuous changes. On the other hand, the dynamic changes of the grid load and user demand also inherently require real-time updates of the scheduling strategy. Therefore, in the embodiments of the present application, based on the deviation analysis results of the operation parameter sets and the dynamic scheduling strategy, the power distribution coefficients in the power distribution matrix can be dynamically adjusted to trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy. Based on the deviation analysis results of the operation parameter sets and the dynamic scheduling strategy, the power distribution coefficients are dynamically adjusted and the rolling optimization calculation is triggered, so that the scheduling strategy can respond in real time to the sudden load of the grid or the insertion of charging demands. In the above embodiments, the rolling optimization calculation of the multi-objective optimization algorithm can be: setting the length of the rolling time window (the length of this time window can be adaptively adjusted according to the grid load volatility); in each rolling optimization, retaining the immutability constraints of the executed instructions; using an improved particle swarm optimization algorithm to solve the optimal power distribution coefficients within the current time window, where the improved particle swarm optimization algorithm can be: introducing an inertia weight adaptive adjustment mechanism, and the weight value decays non-linearly with the number of iterations; adding a population diversity maintenance strategy, and triggering local perturbation operations when the particle aggregation degree is too high; combining the greedy algorithm to quickly screen the Pareto optimal solution set of each iteration.

[0046] To avoid resistance behaviors caused by forced scheduling, such as electric vehicle users unplugging the gun and switching to another charging pile, and using the parameters confirmed by electric vehicle users as hard constraints to input into the optimization algorithm to prevent system oscillations caused by the random modification of electric vehicle users, the above method embodiments also include user collaboration and constraint feedback, that is, constructing a user interaction feedback loop, real-time displaying the adjusted charging parameters and receiving the confirmation instructions of electric vehicle users, and feeding back the confirmed charging parameters to the multi-objective optimization algorithm for strategy correction. Among them, constructing the user interaction feedback loop can be: generating a visual suggestion interface based on the updated scheduling strategy, including a comparison view of the charging periods before and after adjustment and an analysis of the power curve differences; real-time capturing the operation trajectory data of users on the visual suggestion interface, extracting user preference features through a clustering algorithm, and dynamically updating the price sensitivity coefficient and charging priority mark in the charging demand characteristic parameters; when it is detected that the user rejects the adjustment plan, generating at least two alternative charging plans according to the user preference features of electric vehicle users, and triggering the multi-objective optimization algorithm for rapid recalculation; feeding back the final charging plan confirmed by electric vehicle users as a hard constraint condition to the multi-objective optimization algorithm to update the power distribution matrix and time window mapping table of the charging pile.

[0047] Furthermore, the method of the above embodiments may further include a security authentication mechanism, that is, in the charging startup phase, the legitimacy of the charging pile and the electric vehicle is verified through two-way digital certificates; in the data transmission phase, the SM4 national cryptography algorithm is used to encrypt the control instructions end-to-end; in the billing and settlement phase, the blockchain technology is applied to perform distributed storage of transaction records. Among them, the specific method of encrypting the control instructions end-to-end using the SM4 national cryptography algorithm is: generating a unique session key for each charging session, and this key is negotiated based on the elliptic curve cryptography algorithm; adding a timestamp and a device fingerprint watermark to the power control instructions to prevent replay attacks; implementing key lifecycle management, and the session key is automatically destroyed after the charging ends.

[0048] Furthermore, the method of the above embodiments may further include load prediction optimization, that is, constructing a short-term load prediction model based on a deep belief network, and the input features include historical load curves, weather data, and holiday markers; adopting transfer learning technology to adapt the pre-trained general model to the operating characteristics of a specific charging station; updating the prediction results every preset time (for example, 15 minutes), and using them as the feedforward input parameters of the multi-objective optimization algorithm.

[0049] Furthermore, in order to predict the device life decay trend and guide users to select efficient charging piles to reduce the overall energy consumption, the method of the above embodiments may further include an energy efficiency-driven dynamic optimization link, that is, during the real-time monitoring of the actual operating parameter sets of each charging pile, the instantaneous efficiency indicators of each charging pile (including the unit power loss rate, energy conversion efficiency, etc.) are calculated in real time; an energy efficiency degradation model is constructed based on historical efficiency data to predict the life decay trend of the charging pile; an energy efficiency heat map is generated, which shows the life prediction results of the charging pile, and optimization suggestions are pushed through the user terminal; the instantaneous efficiency indicators and life prediction results of each charging pile are used as optimization variables to input into the multi-objective optimization algorithm to dynamically adjust the power distribution coefficient of the high-loss charging piles in the power distribution matrix; when it is detected that the efficiency value of a certain charging pile continuously is lower than the safety threshold, a device maintenance alarm is triggered and the charging sequence scheduling instruction is updated.

[0050] From the above attachment Figure 1As can be seen from the exemplary method for orderly charging of charging piles, on the one hand, a scheduling strategy is dynamically generated through a multi-objective optimization algorithm, enabling the power distribution matrix to intelligently balance grid security, user costs, and facility utilization rate, thereby reducing the peak-valley difference of the grid; on the other hand, through the collaborative issuance of charging power control instructions and charging sequence scheduling instructions, combined with real-time operation monitoring and execution verification, it is ensured that the charging process strictly follows the scheduling plan, and the power deviation rate is controlled within a preset threshold, effectively preventing local overload and timing conflicts; thirdly, the dynamic adjustment mechanism based on deviation analysis enables the system to quickly respond to load mutations, and the rolling optimization calculation can complete the strategy update within a short time, with a significantly improved response speed compared to traditional static scheduling. In summary, the technical solution of this application can achieve an intelligent charging management system featuring data-driven, closed-loop control, and human-machine collaboration.

[0051] Please refer to the attached Figure 2 , which is an orderly charging device for charging piles provided by an embodiment of this application. The device may include an acquisition module 201, a first generation module 202, a second generation module 203, a distribution module 204, a monitoring module 205, and a trigger module 206, which are described in detail as follows: The acquisition module 201 is configured to collect a multi-source data set in real time; The first generation module 202 is configured to generate a dynamic scheduling strategy through a multi-objective optimization algorithm based on the multi-source data set. Among them, the dynamic scheduling strategy at least includes a power distribution matrix and a time window mapping table for each charging pile; The second generation module 203 is configured to generate a charging power control instruction set according to the power distribution matrix of each charging pile, and generate a charging sequence scheduling instruction based on the time window mapping table of each charging pile; The distribution module 204 is configured to issue the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start the charging process; The monitoring module 205 is configured to, during the execution of the charging process, collect the actual operating parameter sets of each charging pile in real time and verify the execution consistency between the actual operating parameters in the actual operating parameter sets and the charging power control instruction set; The trigger module 206 is configured to dynamically adjust the power distribution coefficient in the power distribution matrix based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0052] From the above attached Figure 2As can be seen from the exemplary charging pile orderly charging device, on the one hand, a scheduling strategy is dynamically generated through a multi-objective optimization algorithm, enabling the power distribution matrix to intelligently balance grid security, user costs, and facility utilization rate, thereby reducing the peak-to-valley difference of the grid. On the other hand, through the collaborative issuance of charging power control instructions and charging sequence scheduling instructions, combined with real-time operation monitoring and execution verification, it is ensured that the charging process strictly follows the scheduling plan, and the power deviation rate is controlled within a preset threshold, effectively preventing local overload and timing conflicts. Thirdly, the dynamic adjustment mechanism based on deviation analysis enables the system to quickly respond to load mutations, and the rolling optimization calculation can complete the strategy update within a short time, significantly improving the response speed compared with traditional static scheduling. In summary, the technical solution of this application can realize an intelligent charging management system featuring data-driven, closed-loop control, and human-machine collaboration.

[0053] Figure 3 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As Figure 3 shown, the electronic device 3 of this embodiment mainly includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a program for the charging pile orderly charging method. When the processor 30 executes the computer program 32, it implements the steps in the above-mentioned charging pile orderly charging method embodiment, such as Figure 1 the steps S101 to S106 shown. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module / unit in the above-mentioned device embodiments, such as Figure 2 the functions of the acquisition module 201, the first generation module 202, the second generation module 203, the issuance module 204, the monitoring module 205, and the trigger module 206 shown.

[0054] Exemplarily, the computer program 32 for the orderly charging method of charging piles mainly includes: collecting a multi-source data set in real time; generating a dynamic scheduling strategy through a multi-objective optimization algorithm based on the multi-source data set, where the dynamic scheduling strategy at least includes a power distribution matrix and a time window mapping table for each charging pile; generating a charging power control instruction set according to the power distribution matrix of each charging pile, and generating a charging sequence scheduling instruction based on the time window mapping table of each charging pile; sending the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start a charging process; during the execution of the charging process, monitoring the actual operation parameter set of each charging pile in real time and verifying the execution consistency between the actual operation parameters in the actual operation parameter set and the charging power control instruction set; based on the deviation analysis result between the operation parameter set and the dynamic scheduling strategy, dynamically adjusting the power distribution coefficient in the power distribution matrix, and triggering the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy. The computer program 32 can be divided into one or more modules / units, and one or more modules / units are stored in the memory 31 and executed by the processor 30 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, and the instruction segments are used to describe the execution process of the computer program 32 in the electronic device 3. For example, the computer program 32 can be divided into the functions of an acquisition module 201, a first generation module 202, a second generation module 203, a sending module 204, a monitoring module 205, and a triggering module 206 (modules in a virtual device). The specific functions of each module are as follows: The acquisition module 201 is used to collect a multi-source data set in real time; the first generation module 202 is used to generate a dynamic scheduling strategy through a multi-objective optimization algorithm based on the multi-source data set, where the dynamic scheduling strategy at least includes a power distribution matrix and a time window mapping table for each charging pile; the second generation module 203 is used to generate a charging power control instruction set according to the power distribution matrix of each charging pile, and generate a charging sequence scheduling instruction based on the time window mapping table of each charging pile; the sending module 204 is used to send the charging power control instruction set and the charging sequence scheduling instruction to a target charging pile group to start a charging process; the monitoring module 205 is used to monitor the actual operation parameter set of each charging pile in real time during the execution of the charging process and verify the execution consistency between the actual operation parameters in the actual operation parameter set and the charging power control instruction set; the triggering module 206 is used to dynamically adjust the power distribution coefficient in the power distribution matrix based on the deviation analysis result between the operation parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

[0055] The electronic device 3 may include but is not limited to the processor 30 and the memory 31. Those skilled in the art can understand that Figure 3This is only an example of the electronic device 3, which does not constitute a limitation on the electronic device 3. It may include more or fewer components than those shown in the figure, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0056] The so-called processor 30 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may also be any conventional processor, etc.

[0057] The memory 31 may be an internal storage unit of the electronic device 3, such as the hard disk or memory of the electronic device 3. The memory 31 may also be an external storage device of the electronic device 3, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 3. Further, the memory 31 may also include both the internal storage unit and the external storage device of the electronic device 3. The memory 31 is used to store computer programs and other programs and data required by the electronic device. The memory 31 may also be used to temporarily store data that has been output or will be output.

[0058] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment may be integrated into a processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of the present application. The specific working processes of the units and modules in the above device can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated here.

[0059] In the above embodiments, the descriptions of the respective embodiments each have their own focuses. For parts not detailed or recorded in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0060] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0061] In the embodiments provided in this application, it should be understood that the disclosed device / equipment and method can be implemented in other ways. For example, the device / equipment embodiments described above are merely illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the device or unit can be in electrical, mechanical or other forms.

[0062] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] In addition, the functional units in the various embodiments of this application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0064] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, to implement all or part of the processes in the above-mentioned embodiment methods of the present application, it can also be completed by a computer program instructing related hardware. The computer program of the charging pile orderly charging method can be stored in a storage medium. When the computer program is executed by a processor, it can implement the steps of the above-mentioned various method embodiments, that is, collect a multi-source data set in real time; based on the multi-source data set, generate a dynamic scheduling strategy through a multi-objective optimization algorithm, where the dynamic scheduling strategy at least includes a power distribution matrix and a time window mapping table for each charging pile; generate a charging power control instruction set according to the power distribution matrix of each charging pile, and generate a charging sequence scheduling instruction based on the time window mapping table of each charging pile; send the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process; during the execution of the charging process, monitor the actual operation parameter set of each charging pile in real time and verify the execution consistency between the actual operation parameters in the actual operation parameter set and the charging power control instruction set; based on the deviation analysis result between the operation parameter set and the dynamic scheduling strategy, dynamically adjust the power distribution coefficient in the power distribution matrix, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file or some intermediate form, etc. The storage medium can include: any entity or device capable of carrying computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content included in the storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the storage medium does not include electrical carrier signals and telecommunication signals.

[0065] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application. The specific implementation manners described above have further elaborated on the purpose, technical solutions, and beneficial effects of the present application. It should be understood that the above are only the specific implementation manners of the present application and are not used to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application should all be included within the protection scope of the present invention.

Claims

1. A method for orderly charging of a charging pile, characterized in that: The method comprises: Real-time collection of multi-source data sets; Based on the multi-source data set, a dynamic scheduling strategy is generated by a multi-objective optimization algorithm, wherein the dynamic scheduling strategy at least includes a power allocation matrix and a time window mapping table for each charging pile; generating a charging power control instruction set according to the power allocation matrix, and generating a charging sequence scheduling instruction based on the time window mapping table; Sending the charging power control instruction set and charging sequence scheduling instruction to the target charging pile group to start the charging process; During the execution of the charging process, real-time monitoring of the actual operating parameter set of each charging pile and verification of the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set; Based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, the power allocation coefficient in the power allocation matrix is ​​dynamically adjusted, and the rolling optimization calculation of the multi-objective optimization algorithm is triggered to update the scheduling strategy.

2. The method for orderly charging a charging pile according to claim 1, characterized in that: The multi-source data set includes charging demand characteristic parameters of each electric vehicle in the charging request queue, and collecting the charging demand characteristic parameters of each electric vehicle in the multi-source charging request queue includes: Analyze the current state of charge, maximum allowable charging power curve, and expected full charge time set by the user uploaded by the vehicle battery management system; Obtaining the charging priority mark and electricity price sensitivity coefficient submitted by the user terminal; The estimated arrival time window of each electric vehicle is calculated by combining the geographical location of the charging pile with traffic condition data.

3. The method for orderly charging a charging pile according to claim 2, characterized in that: The construction of the multi-objective optimization algorithm includes: Based on the real-time load fluctuation data of the power grid nodes, the first objective function is established to minimize the peak-to-valley difference of the power grid load; Based on the electricity price sensitivity coefficient in the charging demand characteristic parameter, a second objective function is established with the lowest total charging cost for the user group; According to the estimated arrival time window, establishing a third objective function with maximizing the average utilization rate of charging facilities; A dynamic weight allocation strategy is adopted to adjust the weight coefficients of the first objective function, the second objective function and the third objective function according to the real-time load rate of the power grid.

4. The method for orderly charging a charging pile according to claim 3, characterized in that: The constraints of the multi-objective optimization algorithm include: The transformer load rate is set to not exceed a preset safety threshold, and the three-phase imbalance calculated based on the real-time load fluctuation data is lower than a critical value; Setting the charging completion time to be no later than the expected full charge time in the charging requirement characteristic parameter; and The charging power adjustment rate is set not to exceed the step change rate in the maximum allowed charging power curve.

5. The method for orderly charging a charging pile according to claim 1, characterized in that: The verifying the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set includes: Construct a command-response comparison model to calculate the dynamic time warping (DTW) distance between the actual power curve and the command value; When the DTW distance exceeds a preset tolerance threshold, an execution abnormality event log is generated and an early warning signal is triggered; The system provides graded responses based on the type of anomaly, including local power correction, charging station switching, or global strategy recalculation.

6. The method for orderly charging of a charging pile according to claim 1, characterized in that: The rolling optimization calculation of the multi-objective optimization algorithm includes: Setting the length of the rolling time window, wherein the length is adaptively adjusted according to the power grid load fluctuation rate; During each rolling optimization, the immutability constraints of executed instructions are preserved; An improved particle swarm optimization algorithm is used to solve the optimal power allocation coefficient in the current time window.

7. The method for orderly charging a charging pile according to claim 1, characterized in that: The method further comprises: During the monitoring process of the actual operating parameter set of each charging pile, the instantaneous efficiency index of each charging pile is calculated in real time; Build an energy efficiency degradation model based on historical efficiency data to predict the life decay trend of charging piles; Generate an energy efficiency heat map, the heat map displays the life prediction results of each charging pile, and pushes a preferred recommendation through a user terminal; Input the instantaneous efficiency index and the life prediction result into the multi-objective optimization algorithm as optimization variables, and dynamically adjust the power allocation coefficient of the high-loss charging pile in the power allocation matrix; When it is detected that the efficiency value of a charging pile is continuously lower than the safety threshold, an equipment maintenance alarm is triggered and the charging sequence scheduling instruction is updated.

8. A charging pile orderly charging device, characterized in that: The device comprises: Acquisition module, used for real-time collection of multi-source data sets; A first generating module, configured to generate a dynamic scheduling strategy based on the multi-source data set by a multi-objective optimization algorithm, wherein the dynamic scheduling strategy includes at least a power allocation matrix and a time window mapping table of each charging pile; a second generating module, configured to generate a charging power control instruction set according to the power allocation matrix, and generate a charging sequence scheduling instruction based on the time window mapping table; A sending module, used for sending the charging power control instruction set and the charging sequence scheduling instruction to the target charging pile group to start the charging process; A monitoring module, used to monitor the actual operating parameter set of each charging pile in real time during the execution of the charging process and verify the execution consistency of the actual operating parameters in the actual operating parameter set and the charging power control instruction set; A trigger module is used to dynamically adjust the power allocation coefficient in the power allocation matrix based on the deviation analysis result between the operating parameter set and the dynamic scheduling strategy, and trigger the rolling optimization calculation of the multi-objective optimization algorithm to update the scheduling strategy.

9. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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