228 results about "User charge" patented technology

The invention discloses a charging chip real-time power scheduling control method, and relates to the technical field of new energy automobile charging pile power scheduling, and the method comprises the following steps: S1, obtaining the operation parameters of charging equipment in real time, including the voltage fluctuation value of an input power grid, the output current of a charging interface, and the temperature of a charging chip; according to the method, the power grid voltage fluctuation, battery SOC / temperature and chip working condition multi-source data are collected in real time, a multi-target optimization model of dynamic weight distribution is combined, the service life loss of the battery is minimized, the charging efficiency and the power grid load are balanced at the same time, the user charging mode and the battery state difference are taken into consideration by adopting a priority scheduling strategy, and the charging efficiency is improved. Millisecond-level power fine control is achieved through cooperative adjustment of the PWM duty ratio and the switching frequency, personalized charging requirements are met, the overall energy efficiency is improved, a dual safety defense line is constructed through a graded load reduction protection mechanism and a fuzzy control algorithm, and a power distribution scheme is intelligently reconstructed when a power grid is abnormal or a battery is overheated.

The invention provides a modeling method and system for a user charging behavior portrait. Wherein the charging time point, the driving mileage and the battery attenuation data of a user are collected, a regional charging demand thermodynamic diagram is generated in combination with a driving track, and the electric energy consumption and the battery attenuation data are fused to form energy consumption characteristics. In an underground parking lot and other satellite signal missing areas, a differential positioning base station and vehicle-mounted inertial navigation are used for compensating positioning, and continuous space-time trajectory coordinates are generated. Energy consumption characteristics and track coordinates are analyzed through deep reinforcement learning, the correlation among the charging time period, the pile distribution density and the power grid load is identified, and a charging demand space-time weight coefficient is generated; and constructing a user charging behavior feature vector based on the coefficient and power grid data, performing similarity matching classification, and finally establishing a user charging behavior portrait model. According to the technical scheme provided by the invention, accurate prediction of the charging demand and space-time collaborative optimization of the power grid load are realized.

The invention relates to a new energy charging pile dynamic scheduling method, system and device and a medium, and the method comprises the steps: collecting the real-time load data of a power grid, user charging request information, battery health state data and historical charging data, and carrying out the classification and aggregation of charging behavior characteristics, and generating behavior grouping labels; demand prediction and power safety boundary calculation are carried out based on a preset power prediction model in combination with the battery health state, and a power prediction result is formed; detecting power grid load conflicts and optimizing power distribution to generate a preliminary scheduling scheme; performing multi-target power optimization adjustment on the conflict points to generate a charging power scheme, and issuing and executing the charging power scheme; and finally, dynamically updating prediction model parameters according to charging pile feedback data. According to the method, the problems of neglect of battery health change, insufficient charging behavior distinguishing, response lag, weak collaborative optimization capability and the like in the prior art are solved, and collaborative optimization of power grid stability, equipment safety and charging efficiency is realized.

The invention discloses a charging user behavior pattern analysis system and method, and relates to the technical field of electric vehicle charging management, and the method comprises the steps: obtaining and building a multi-source data pool, fusing the multi-source data pool, analyzing a charging behavior pattern of a user, calculating the abnormal behavior probability, building a dynamic scheduling rule according to the charging behavior pattern of the user, and carrying out the dynamic scheduling according to the abnormal behavior probability. According to the method, through multi-source data collection and analysis, the user charging behavior is accurately predicted, resource distribution is dynamically optimized, the charging station operation efficiency and the user satisfaction degree are improved, virtual power plant integration is promoted, and the power generation efficiency is improved. According to the method, a vehicle-to-power grid strategy optimization module is constructed, the user participation power grid peak regulation potential is quantified, regional energy scheduling is optimized, in addition, an anomaly detection and response mechanism is provided, safety and stability are ensured, and the charging service is promoted to develop towards the efficient, intelligent and sustainable direction.

The invention relates to the technical field of data security management, in particular to a charging user portrait data security management system and method, and the system comprises a channel state sensing module, a key updating control module, a behavior sequence evaluation module, a data exception recognition module and a key scheduling encryption module. In the invention, active perception of communication quality change is realized, agility and protection of data transmission are enhanced, and signal attenuation and equipment number labels are extracted for secret key updating judgment by comparing communication link quality with a user positioning data sequence, capturing a hopping trend of signal intensity and a noise ratio and mapping an abnormal fluctuation segment to an equipment identifier. The pertinence and efficiency of an encryption strategy are improved, a portrait feature deviation behavior is recognized by combining a user charging behavior and a frequency sequence, a specific user number is positioned, the anomaly recognition precision is improved, a path encryption strategy is dynamically generated according to behavior deviation and access path information, and a flexible and efficient data protection system is constructed.

The invention provides a charging pile dynamic power regulation and control system and method based on multi-parameter coupling. Belongs to the field of charging of charging piles, and comprises the steps that when the total power of a bus reaches a load reduction threshold value, a phase cooperative load reduction strategy is activated, a multi-stage response mechanism is configured through a safety control execution unit, peak clipping and valley filling control is implemented in the time periods t1-t2, and a total load curve is smoothed through time shifting power distribution; and when the power is close to the power limit, the power of the non-emergency charging pile is automatically reduced to a safety threshold, and the equipment utilization rate is intelligently improved in a load sufficient period. And the concurrent access number of the charging piles is maximized on the premise that the bearing limit of the phase lines is not exceeded. The user capacity under the rated load of the charging station is improved, the user charging cost in the time-of-use electricity price period is reduced, the utilization rate of electric power resources is improved, millisecond-level power adjustment response is achieved, the full-time-period compliance of dynamic load limitation on the power grid side is ensured, and the operation benefits, safety and reliability of charging infrastructures are remarkably improved.

The invention discloses a multifunctional slot intelligent charging pile remote control and dispatching system, particularly relates to the field of charging piles, is used for solving the problems of low charging resource utilization rate and large power grid load impact, and provides an accurate basis for power distribution by collecting and predicting user charging requirements and vehicle charging capacity in real time based on federal learning. A prediction result is combined with real-time load data of the power grid through a multi-objective optimization algorithm, a power distribution baseline value considering user requirements and peak clipping and valley filling of the power grid is generated, and charging resources and the stability of the power grid are effectively balanced; a reinforcement learning algorithm is adopted to construct a dynamic game model of a user and a power grid, the output power of the charging pile is adjusted in real time, user behaviors are guided through an integral excitation strategy, conflicts between demands and loads are dynamically coped, and the system adaptability is improved; and finally, performing full-link simulation verification on the charging pile group by using a digital twinning technology, comparing a continuous optimization scheduling strategy based on actual operation data, and ensuring continuous improvement of system performance.

The invention relates to the technical field of community new energy control, and discloses an orderly and rapid charging new energy control method, which comprises the following steps: collecting photovoltaic output data, power grid voltage / current data, user charging behavior data and battery state parameters in real time; generating a dynamic charging strategy based on the power grid load data, the time-of-use electricity price information, the photovoltaic output fluctuation range and the battery health state evaluation result; generating a corresponding local optimization strategy based on the operation state data of each charging pile; an economic index and a technical index are calculated, and when any index exceeds a preset threshold value, a strategy adjustment instruction is triggered, and a charging mode switching threshold value is updated; an equipment health index is generated by combining inverter temperature, coil coupling efficiency and insulation impedance parameters with historical fault data, and maintenance early warning is triggered when the health index is lower than a safety threshold value. According to the invention, the electricity cost of the user can be reduced on the basis of improving the multi-energy collaborative optimization capability of the charging facility in the residential area.

The invention relates to the technical field of electric vehicle intelligent energy management, and discloses a charging management method and system for an electric vehicle, and the method comprises the steps: obtaining vehicle data, user charging demand data and charging station data; according to the charging station data and historical data of the vehicle data, calculation is carried out to obtain load data; comparing the load data with the available upper limit data, and generating priority charging sequence data in combination with the charging demand data; the idle charging piles are grouped, and an integer programming algorithm is adopted to obtain an optimal power distribution scheme; generating a charging plan according to the optimal power distribution scheme; in the plan execution process, collecting real-time charging data and updating the charging plan; and according to the real-time charging data, in combination with historical operation data of the charging station, a deep learning algorithm is adopted, an intelligent scheduling strategy is optimized, and intelligent distribution of resources of the charging station is completed. According to the method, historical data and real-time charging data can be fused to realize dynamic optimization of the charging plan of the electric vehicle.

The invention relates to the technical field of electric vehicle charging pile management and distribution, and discloses a new energy charging pile management and distribution method and system, and the method comprises the steps: collecting charging user data, position data and environment data, and carrying out the preprocessing; and analyzing the preprocessed data, and constructing a prediction model of the charging demand of the user. And calculating the load condition of each charging pile according to a demand result predicted by the model, and adjusting a charging pile distribution strategy. According to the invention, through the steps of data preprocessing, charging demand prediction, load calculation and distribution strategy optimization, real-time monitoring and feedback and the like, intelligent management of charging pile resources is realized. The method plays a key role in improving the efficiency of the charging service, reducing overload and idle phenomena, optimizing the user experience and the like. A more intelligent, efficient and personalized charging pile management and distribution system is provided for electric vehicle users, the utilization rate of charging pile resources is effectively improved, the waiting time of the users is shortened, and the flexibility and stability of the system are enhanced.

The invention provides an electric vehicle charging resource planning and optimizing method, and aims to solve the problems of static performance, single target optimization, one-sided attraction evaluation, disjunction with traffic jam management and the like in existing planning. The method comprises the following steps: firstly, acquiring regional basic data, and establishing a charging station composite attraction evaluation model which comprehensively considers POI value, plot cost, estimated available charging pile quantity proportion and reputation parameters determined based on historical service data, so as to predict the probability of selecting each charging station by a user; and optimizing charging pile capacity configuration according to the predicted user selection probability and the total charging demand so as to minimize the combined cost of the construction cost and the average queuing waiting time of the users. The optimal charging site is screened and recommended through weighted aggregation of multiple indexes such as composite attraction, construction cost, queuing waiting time and the like. Scientific site selection and reasonable capacity configuration of the charging stations are achieved, urban traffic congestion is effectively relieved, balanced distribution of the charging flow is promoted, and the charging experience of users is improved.

The invention discloses a multi-type charging facility planning method and device based on user charging behavior decision, and the method comprises the steps: building an electric vehicle multi-type charging demand prediction model based on Monte Carlo stochastic simulation and origin-destination OD analysis; performing prediction processing through the electric vehicle multi-type charging demand prediction model to obtain spatial and temporal distribution of electric vehicle multi-type charging demands; constructing a multi-type charging facility collaborative optimization planning model according to the spatial-temporal distribution in combination with set constraints; based on second-order cone constraints, converting the multi-type charging facility collaborative optimization planning model into a mixed integer second-order cone planning model; a solver is set to solve the mixed integer second-order cone programming model, and the site selection position of a charging station, the configuration number of slow charging / fast charging / overcharging facilities and a power distribution scheme are obtained. According to the method, the difference of charging facilities in the station can be fully considered while the charging demand of the user is fully considered, and the optimal scheme is output.

The invention relates to the technical field of new energy vehicles, and particularly discloses a new energy vehicle charging pile layout optimization method, which comprises the steps of data acquisition and preprocessing, demand prediction, feature extraction, layout optimization, model training and testing, and result application and feedback. According to the scheme, the multi-dimensional charging data are divided according to categories and are processed respectively, the characteristics are extracted comprehensively by evaluating the traffic pressure, the user behavior and the power grid load, the computability of the data is improved, the charging pile layout is guided more accurately, and resource allocation is optimized; a layout optimization model is designed by adopting deep reinforcement learning, charging demand changes of different areas are dynamically adapted, and a dynamic data set is constructed by combining user charging demand feedback and user satisfaction score through iterative learning optimization of a layout strategy, so that the load of a power grid is balanced, and the charging experience of the user is effectively improved.

The invention relates to the technical field of electric vehicle charging, in particular to a non-inductive guidance intelligent charging system and method for electric vehicle users, and the method comprises the steps: researching the charging behavior time-varying characteristics of private vehicles and commercial vehicles and a prediction model based on a user charging rule and a decision mechanism; the selection preference, waiting time, unit time cost and price sensitivity of the user to the charging pile are analyzed, and a charging behavior guiding difficulty and evaluation model is established; combining different service charges and load data to construct a service charge-load elastic model; a multi-period pricing mechanism facing peak load shifting and considering user space-time guidance is provided; establishing a power grid interaction analysis and dynamic grouping recommendation method under multiple scenes of the power grid; and in combination with SOC and real-time position information, a response capability prediction model is constructed, and the multi-element regulation and control requirements are met. According to the invention, new requirements can be met in time, and efficient operation of the charging service is ensured.

The invention relates to the technical field of charging management, and discloses an intelligent charging method and system based on a charging pile, and the method comprises the steps: collecting an identifier of a charging object connected with the charging pile, calling the historical charging data and safety evaluation information, obtaining a user charging instruction, and combining with a charging pile performance evaluation model, performing gradient analysis on the charging instruction to generate a test plan and a subsequent plan, performing testable charging according to the test plan, collecting test data to construct a test information flow, analyzing the test information flow to form a feedback factor matrix, obtaining guide information in combination with safety evaluation information, and substituting the guide information into the subsequent plan to realize final stable charging operation. According to the method, by introducing multi-dimensional data analysis and gradient control, a personalized and dynamic charging strategy is realized, the charging efficiency and safety are improved, the service life of the battery is prolonged, the risks of overcharging, overheating and the like are reduced, and the problem that in the prior art, a charging pile lacks individual difference adjustment of a charging object is solved.

The invention discloses an electric vehicle coordinated charging control method and system based on charging demand grading, and the method comprises the steps: S01, detecting whether there is an electric vehicle connected to a charging pile or not in real time, and collecting the connection state parameters of the electric vehicle; s02, the type of the charging pile is judged, if the charging pile is a household charging pile, a corresponding charging power demand index value is calculated, and if the charging pile is a public charging pile, an occupation duration penalty coefficient is calculated; s03, determining the upper limit value of the public charging power according to the occupation duration penalty coefficient of the electric vehicle at the historical moment; and step S04, setting an objective function and constraint conditions by minimizing the peak-valley difference of the total load of the micro-grid and maximizing the load smoothness, solving to obtain charging power control parameters of all the currently accessed electric vehicles, and constructing and forming an ordered charging model. According to different charging requirements of users, the user charging behavior can be dynamically optimized, the load peak-valley difference is reduced, and the power grid operation stability is improved.

The invention relates to the technical field of intelligent power grid and new energy automobile charging control, and discloses a low-carbon economical charging pile power dynamic regulation and control method, system and device and a medium, and the method comprises the steps: obtaining user charging parameters and preference weights, combining historical travel data to predict a departure probability, and screening an adjustable and controllable time period; collecting electricity price, carbon intensity and power grid state data in real time to calculate a dynamic regulation and control coefficient; performing standardized processing on external data and fusing user weights to construct a time period scoring function; selecting high-quality time periods to form a candidate set according to score sorting; solving an optimal power distribution scheme meeting target electric quantity and power constraint in the candidate set by taking minimization of carbon emission and power cost as a target; and finally, converting the scheme into a control instruction sequence to execute dynamic power regulation and control in real time. The method is used for optimizing electricity-carbon collaboration and user experience in the charging process.

The invention relates to the technical field of the Internet of Things, and discloses a charging pile management method and system based on the Internet of Things. The method comprises the steps that an Internet of Things sensor collects charging pile parameters to obtain original data; user identity authentication allocates an authorization level; carrying out cable temperature anomaly detection and calculating an early warning signal; user charging behavior clustering analysis is carried out; carrying out LSTM prediction load optimization scheduling; and performing multi-period dynamic electricity price calculation to generate a bill. According to the invention, real-time monitoring and anomaly detection are carried out on the temperature of the cable in the charging pile through the Internet of Things technology, potential safety hazards are identified in time, and intelligent charging load prediction and scheduling are realized in combination with user behavior characteristics.

The invention relates to an electric vehicle space-time dual-scale optimization scheduling method and device considering user satisfaction. The method comprises the following steps: collecting charging demand data, power grid historical load data, user satisfaction related data and charging station related information of an electric vehicle; based on the collected data, establishing an electric vehicle time layer optimization scheduling model considering user satisfaction and load fluctuation; the optimal charging strategy of the electric vehicle is optimized by taking the maximization of the user charging cost satisfaction degree and the minimization of the power grid load fluctuation as optimization objectives; constructing an electric vehicle space layer optimization scheduling model considering user satisfaction and load fluctuation; and a target function is constructed by taking user travel satisfaction maximization as an optimization target, and a space layer electric vehicle charging station distribution condition is obtained through optimization according to a time layer optimal strategy. Compared with the prior art, the method can guide automobile charging and select a proper charging station while improving the user satisfaction, and achieves the balance between the high efficiency of charging scheduling and the load of a power grid.

The invention discloses an electric vehicle travel scene ordered charging method under the guidance of dynamic electricity price, and the method comprises the steps: carrying out the charging load space-time distribution prediction through an EV charging load space-time distribution prediction model, and obtaining the load space-time distribution characteristics of each charging station; generating an optimized electricity price strategy through a master-slave game model, and making a dynamic electricity price scheme by an electric vehicle aggregator in combination with a station charging load condition and a charging responsivity function; and simulating user travel behaviors according to the road network traffic flow data, and making a user charging plan based on the corrected electricity price information. According to the method, an ordered charging regulation and control strategy based on dynamic electricity price response is provided, a dynamic electricity price model based on charging load real-time change is constructed, meanwhile, a double-layer game model of a load aggregator and a user is established in combination with user travel space-time distribution characteristics, and an ordered charging optimization strategy giving consideration to multi-party benefits is formed; and finally, dual goals of power grid operation optimization and user economic benefits are realized.

The invention discloses an electric vehicle charging management method based on an HA-SAC algorithm, and the method comprises the steps: firstly, carrying out the modeling of an electric vehicle charging management optimization model through fully considering the life loss of a transformer and the charging preference of a user; modeling a charging management optimization problem as Markov Game (MG for short), taking each electric vehicle as an independent intelligent agent, constructing a state space according to information such as the charging state, the electricity price and the user preference, and taking the charging power as an action variable; then, on the basis of an HA-SAC algorithm, a local action network and a centralized evaluation network are constructed for each agent, and centralized training is carried out through global information; and finally, the trained strategy is deployed to each electric vehicle agent node, so that the electric vehicle agent node can autonomously decide a charging behavior only by depending on local information in actual operation, and distributed and privacy-protected cooperative charging control is realized.

The invention relates to the technical field of power system optimization, and discloses a charging load estimation and charging mode optimization method based on an electric vehicle. According to the method, a source load storage-medium voltage distribution network collaborative planning framework is initialized, and charging station geographical distribution and user charging behavior modes are integrated; collecting charging load sequence data in real time, and judging a charging load control state through a volatility analysis algorithm; when the charging load control is in an unstable state, synchronously analyzing voltage fluctuation characteristics and charging power change characteristics of the power distribution network, and generating a charging efficiency evaluation value; dividing the charging effect into three levels of high efficiency, conventional and low efficiency based on the evaluation value; for a conventional grade charging effect, a time sequence decomposition technology is adopted to extract a load trend component, and an overall load stability index is calculated; and fusing the charging efficiency evaluation value and the overall load stability index, and outputting a charging power adjustment instruction. The method effectively improves the operation stability and the charging efficiency of the power distribution network.

The invention provides a charging pile income estimation and evaluation method, system and device and a medium, and belongs to the technical field of charging facility management. The method comprises the following steps: collecting historical data of a user charging behavior through a charging pile terminal, and constructing a user feature vector; based on a clustering algorithm, dividing the users into different user groups by using the user feature vectors, and determining a user set of each type and a clustering center; introducing a time period convenience factor, and establishing a time period selection probability model considering user price sensitivity by using an improved Logit model to calculate a time period selection probability; constructing a dynamic income prediction function containing an electricity price and user behavior nonlinear coupling item, and calculating a single-day total income based on a user feature vector, each type of user set and a time period selection probability; and based on a gradient descent algorithm, an optimal electricity price strategy for maximizing the total income is solved according to the dynamic income prediction function. According to the invention, through real-time data updating and model iteration, the income prediction precision is significantly improved.

The invention discloses a method for a charging station operator to participate in market bidding based on artificial intelligence, and the method comprises the following steps: S1, data collection and preprocessing: collecting historical charging data, real-time electricity price information, weather forecast, power grid load information, electric power capacity of a charging station and user charging habit data, cleaning the data, processing missing values, and storing the missing values in a database; and converting a data format so that the data format is suitable for input of a machine learning model. The method has the advantage of low-cost electricity purchase, and solves the problems that in the using process of an existing method that a charging station operator participates in market bidding, it is inconvenient for the charging station operator to purchase electricity at the lowest cost, if the charging station cannot purchase electricity at the lowest cost, the electricity purchase cost is directly increased, and then the overall operation cost is affected; and the charging station needs to increase the charging service price to maintain the profit level due to the relatively high purchase cost, so that the charging cost of the user is indirectly influenced.

The invention discloses a bidirectional OBC intelligent charging and discharging scheduling method and system capable of supporting a V2X function, and the method comprises the steps: carrying out data collection, which comprises the collection of power grid demand information and the collection of user charging habits; s2, power grid load prediction is carried out, and the system predicts the load change trend of the power grid in a future time period by using historical load data and real-time monitoring data of the power grid and combining a prediction model of machine learning; s3, optimizing a charging and discharging strategy, and generating an optimal charging and discharging plan by the system through a scheduling algorithm according to a load prediction result; and S4, the system has a dynamic response capability, monitors the load fluctuation of the power grid and the user demand change in real time through a V2X module, and adjusts the charging and discharging plan in time. According to the invention, by introducing the load prediction and dynamic response algorithm, the system can perform intelligent scheduling according to the real-time load of the power grid, thereby greatly improving the intelligence and real-time response capability of the system.

An electric vehicle charging station recommendation method and system, applied to the related field of charging station recommendation. The method comprises: acquiring user charging features and influence degree values of user charging evaluation features on user charging preferences, wherein the user charging evaluation features are constructed on the basis of the user's evaluation information on charging stations (S1); calculating the user's comprehensive scores for different charging stations on the basis of the influence degree values, and constructing a user preference matrix by using the user's comprehensive scores for different charging stations as elements (S2); and decomposing the user preference matrix by means of a singular value decomposition method, acquiring potential matching relationships between the user and charging station features, and recommending a charging station to the user on the basis of the potential matching relationships (S3). The method solves the problem in the prior art that charging stations cannot be personalizedly recommended for different users, thereby improving user experience.

The invention discloses an electric vehicle individual user charging demand prediction method and device, and the method comprises the steps: constructing a multi-source data set based on the charging behavior data, weather data and holiday and festival data of EV users in a community; cleaning and standardizing the multi-source data set through a multi-source data preprocessing model to obtain a processed multi-source data set; performing clustering processing on the processed multi-source data set through a double-layer clustering model to obtain charging type features and user type features; extracting input features from the processed multi-source data set; and inputting the charging type characteristics, the user type characteristics and the input characteristics into a charging demand prediction model, and performing prediction processing through a LightGBM algorithm to obtain a user charging demand. According to the method, interference of exogenous variables can be reasonably incorporated, the prediction precision is improved, and the problems in the prior art are solved.

The invention discloses an artificial intelligence Emsp super charging prediction and management system, and relates to the technical field of Emsp super charging, the charging demand and the power grid load change are predicted by obtaining the charging station equipment state, the user behavior, the power grid load and the environment data, and an optimized charging task distribution plan is calculated; the charging power switching rate is smoothly adjusted by the load scheduling module, and the instantaneous load change rate is reduced; the synchronous control module is used for realizing synchronization of frequency and phase of the charging equipment and the power grid; a coordination index is monitored in real time by means of an evaluation and dynamic regulation and control module, power distribution is dynamically adjusted under the incoordination condition, and the power of a high-load station is dispersed; real-time data and historical data are integrated through the control and storage module, an artificial intelligence model and a scheduling strategy are optimized, the risk that a power grid is unstable due to instantaneous load surge and harmonic interference is effectively reduced, the operation efficiency and safety of a charging network are improved, and meanwhile the charging experience of a user is improved.

The invention provides a control method and device based on charging pile V2G, equipment and a medium. The method comprises the steps that power grid dynamic supply and demand parameters, vehicle state information and use plan information are collected in real time; constructing a multi-objective optimization model according to the dynamic supply and demand parameters of the power grid, the vehicle state information and the use plan information; setting constraint conditions in the multi-objective optimization model; wherein the constraint conditions comprise a vehicle state constraint, a time constraint, an electric quantity constraint, an equipment constraint and a power grid constraint; and solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating an optimal charging and discharging strategy meeting constraint conditions, and issuing the optimal charging and discharging strategy to the V2G charging pile for execution. According to the invention, power grid peak and valley load distribution can be obviously improved, user charging cost is reduced, the service life of a battery is prolonged, stable operation of the power distribution network is ensured, efficient response and safe controllability of vehicle-network cooperative scheduling are realized, and bidirectional requirements of the power grid and the user are considered while energy management efficiency is improved.

The invention provides a charging revenue improvement intelligent regulation and control method and system fusing user behaviors and operation data, and the method comprises the steps: carrying out the load prediction modeling of the charging time period distribution of each user group through a long-short-term memory network, and obtaining the charging revenue improvement intelligent regulation and control information of each user group according to the user group label and the time sequence data; obtaining an expected load distribution curve of each charging pile in the next 24 hours, and judging the load difference degree of the peak period and the valley period; real-time charging pile state information and user charging request data are obtained, if the predicted load distribution shows that the supply and demand mismatching degree in a certain time period exceeds a preset threshold value, a dynamic resource configuration mechanism is started, and the number of users needing load transfer and a target time period are determined; and according to the flexible user characteristics in the user group tag, a personalized off-peak charging suggestion is generated by adopting a differential incentive strategy, and an optimal charging period recommendation scheme for each user is obtained by calculating electricity price discounts and charging point rewards in different periods.