253 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 relates to the technical field of power management, in particular to a charging pile group energy management method, which comprises the following steps: S1, predicting the power demand of each charging pile in a short period and the available amount of renewable energy; s2, adjusting the charging power of each charging pile through a dynamic allocation algorithm; s3, encouraging a user to charge in an off-peak period based on a preset pricing algorithm; s4, predicting a user charging demand mode; s5, monitoring electric energy quality in a charging process in real time, and automatically adjusting parameters of charging equipment by using a preset electric energy adjusting technology; and S6, automatically activating a preset standby energy supply scheme under the emergency condition of the power grid fault. According to the invention, energy distribution is optimized through real-time monitoring and intelligent adjustment of the charging strategy, the power grid efficiency is improved, the cost is reduced, and the continuity and safety of the charging service are ensured through the standby energy system, so that the operation efficiency of the charging infrastructure and the user satisfaction are remarkably improved.

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 embodiment of the invention discloses a charging cost analysis method and system for a liquid cooling over-charging terminal, and belongs to the technical field of data analysis. According to the method, through multi-dimensional data acquisition and deep behavior analysis, accurate description of user charging behavior characteristics can be realized, the accuracy of charging demand prediction and resource scheduling is effectively improved, and the user behavior characteristics are converted into quantifiable parameters, so that the user experience is improved. According to the invention, the formulation of differentiated service strategies can be realized to enhance the personalized adaptation capability of the charging service. Besides, the generation and execution mechanism of the cross-terminal dynamic scheduling strategy can optimize the distribution efficiency of the power resources in the space-time dimension and relieve the local overload problem in the peak period, and the cooperative adjustment function of the real-time power supply parameters and the cost calculation logic can guarantee the charging efficiency and balance the operation stability of the power grid at the same time. In conclusion, the method can significantly improve the resource utilization efficiency of the charging facility.

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.

A method for estimating the state of health of lithium-ion batteries considering user charging behavior is provided. Firstly, data during the cycle charging process of the actual vehicles are collected, and it is analyzed. Then, the charging process data is applied to extract health features. The obtained health features are used to establish a state of health estimation model, and the established model is used to estimate the state of health of the battery. It involves reading the charging data, analyzing the frequency and occurrence of the start charging voltage and stop charging voltage, integrating them into a heat map, and drawing the IC curve of the charging and discharging process. This enables any charging behavior to extract the health features of battery aging, and predicts the health values of the battery, thus completing the estimation of the state of health for lithium-ion batteries.

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 discloses a charging pile monitoring system based on artificial intelligence, which relates to the field of charging pile monitoring and comprises a data acquisition module, a user demand analysis module, a charging index analysis module, a temperature monitoring and adjusting module, a charging state monitoring and early warning module, a man-machine interaction platform and a database. A charging demand alternative set, a temperature state value and a charging state value of a charging pile corresponding to a charging pile user are obtained by judging and analyzing a charging pile user behavior characteristic demand parameter and a charging pile user charging association demand parameter and then counting and analyzing a charging pile temperature state parameter and a charging pile charging state parameter; the charging pile can be ensured to operate in a proper temperature range interval, the charging pile can immediately remind a worker in an abnormal working state, the working pressure and heating of internal elements of the charging pile are reduced in a low charging peak period, the stability of a power grid of the charging pile is ensured in a high charging peak period, and the service life of the charging pile is prolonged.

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 a self-adaptive charging power supply control method and system, and relates to the field of power supply control, and the method comprises the steps: capturing a voltage and temperature time sequence signal of a battery in real time through a high-precision sensor, carrying out the multi-scale time sequence modeling of the dynamic characteristics of the battery through a time convolution network in combination with the charging behavior data of a user, and obtaining a multi-scale time sequence model; and extracting high-frequency nonlinear fluctuation characteristics of the battery state. Meanwhile, semantic association coding is performed on the user behavior data, and a mapping relation between the user demand and the battery health state is established. Then, a cross-modal feature interaction mechanism is introduced, dynamic coupling analysis of the battery state and the user requirement is achieved, finally, a charging voltage regulation and control instruction which is optimally matched with the current battery state and meets the user scene requirement is generated through a decoder, charging parameters are dynamically adjusted, and collaborative optimization of the charging efficiency, the battery life and the personalized scene is achieved.

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 discloses an electric vehicle charging scheduling method based on an improved optimization algorithm and a terminal. The method comprises the steps that a user side terminal system needs to judge whether a user sends a charging request or not according to the state of charge of an electric vehicle battery, if yes, a data acquisition unit obtains the daily charging demand, position information, power distribution network operation conditions, regional charging station information and related data information of a regional traffic network of the electric vehicle, and if not, the user side terminal system sends the charging request; if yes, returning to the previous step for cyclic judgment; the data processing and algorithm calculation unit establishes an optimization model with the minimum power grid load fluctuation and the minimum economic time cost of user charging as targets, inputs the obtained data into the model, and solves a target function by adopting a non-dominated sorting whale optimization algorithm with an elitist strategy; the optimal charging scheme of the electric vehicle is obtained; and the transmission and application unit sends charging scheme suggestions to users, so that ordered charging scheduling of the electric vehicles in the region is realized.

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.