287 results about "Dynamic charging" patented technology

The invention discloses an optical storage charging and discharging integrated power station vehicle network interaction method considering demand side response, and aims to improve the bidirectional interaction capability of a power station and a power grid and realize dynamic matching between the demand side response of the power grid and user charging and discharging behaviors. A high-precision photovoltaic output prediction model, an energy storage system SOC dynamic model and an electric vehicle charging load probability distribution model are constructed, and a joint output feature library is formed. A dynamic charging and discharging priority division method is put forward, and the charging and discharging power distribution weight is dynamically adjusted in combination with the vehicle state, the user electricity price sensitivity and the power grid load curve. And designing a composite demand side response excitation mechanism combining time-of-use electricity price and capacity compensation, and guiding the user to participate in peak regulation and valley filling of the power grid. An edge computing and cloud cooperative control framework is adopted, and a dynamic security check module based on model predictive control is adopted, so that potential out-of-limit risks are evaluated in advance, and the security of a power station and a power grid is guaranteed. And efficient operation of the light storage charging and discharging integrated power station and efficient utilization of new energy are realized.

The invention relates to the technical field of electric vehicle battery charging detection, and discloses an intelligent charging pile system and method integrating real-time battery state detection. A data fusion module; a battery health state prediction module; a charging optimization control module; the fault early warning module is connected with the cloud platform and edge computing cooperative processing module; the method comprises the following steps: acquiring battery data through multiple sensors, and constructing a data frame; extracting electrical characteristics, and generating a battery state vector; a digital twin model is constructed, and health state prediction is carried out; formulating a dynamic charging power regulation strategy; comparing the health trend of the battery with an expected behavior, and generating a fault early warning signal; and uploading the data and the strategy to the cloud platform, and updating the control strategy. According to the invention, a multi-sensor data fusion technology is adopted, twin modeling and a deep learning algorithm are combined, the health state of the battery is monitored in real time, the health state of the battery is comprehensively evaluated, and the fault risk of the battery is accurately predicted.

The invention relates to the technical field of electric vehicle charging management, and discloses an electric vehicle charging optimization management system. The system comprises a dynamic charging demand prediction module which constructs a model based on historical data, collects voltage fluctuation, current phase deviation and charging power pulse characteristics of a charging pile in real time, and outputs a theoretical charging load value; the multi-dimensional demand difference analysis module is used for performing three-dimensional comparison of time accumulation deviation, frequency response difference and pulse characteristic similarity on theoretical and actually measured load values to generate a charging pile group level difference characteristic tensor; the hot spot region association positioning module inputs the difference feature tensor into a spatial topology mapping network, generates a fault probability thermal distribution diagram in combination with power grid node capacity and charging station position information, and positions a charging bottleneck region; and the self-adaptive regulation and control strategy generation module is used for starting millisecond-level monitoring on a high fault probability region according to the thermal distribution diagram and applying a power disturbance test on adjacent nodes. The system can improve the accuracy and effectiveness of charging management.

The invention relates to the field of power battery energy management, in particular to a power battery pack charging and discharging efficiency dynamic optimization system and method, and the system comprises a central control unit, a multi-dimensional sensing module, a dynamic charging and discharging control module, a working condition-heat dissipation cooperation module, a parallel cell equalization module and an aging adaptation optimization module. The multi-dimensional sensing module is used for collecting basic parameters, polarization characteristics, parallel state, aging degree and environmental parameters of a battery pack, the dynamic charging and discharging control module is used for collecting data, the working condition-heat dissipation coordination module adopts a three-stage heat dissipation framework, and the parallel battery cell balancing module is used for balancing the battery pack through a dynamic current distribution and voltage active balancing double-coordination mechanism. The aging adaptation optimization module is used for grading and differentially optimizing charge and discharge parameters and heat dissipation strategies based on the aging degree of the battery cell, so that the problems of low charge and discharge efficiency, poor wide temperature range adaptability, weak parallel consistency, fast aging attenuation and multi-factor coupling deterioration of the power battery pack are solved.

The invention relates to the technical field of charging parking space guiding control, and particularly discloses a parking space charging reservation and intelligent guiding method, device, equipment and medium, and the method comprises the steps: obtaining vehicle battery state data and a user reservation time window through an Internet of Things terminal; synchronously acquiring charging pile state data, real-time traffic flow data and power grid load information in the parking lot; fusing the obtained multi-source heterogeneous data based on a federated learning framework, constructing a dynamic charging demand prediction model, and predicting charging pile occupancy rate distribution in a preset time period by using a space-time diagram convolutional network; through the combination of the dynamic charging demand prediction model and the space-time diagram convolutional network, the occupancy rate distribution of the charging piles is accurately predicted, the scheduling strategy of the charging piles is adjusted in real time, the problem of unreasonable resource allocation is avoided, and the waiting time of a user is shortened; the application of the quantum genetic algorithm optimizes the matching process of the charging pile through global optimal solution search, thereby ensuring the balance of the power grid load and the rationality of the charging strategy.

The invention relates to the technical field of energy storage systems, in particular to a dynamic charging and discharging strategy collaborative optimization method for prolonging the service life of an energy storage system, and the method comprises the steps: inputting historical electricity price time sequence data based on a preset long-short-term memory network model, so as to generate an electricity price prediction sequence; constructing a state vector, wherein the state vector comprises an internal state of the energy storage system at the current moment t and an electricity price prediction sequence; the state vector is input into a pre-trained deep reinforcement learning agent, and the deep reinforcement learning agent outputs a selected specific action based on a predefined discrete charging and discharging action set according to the input state vector; and converting the output specific action into a charging and discharging power control instruction, and issuing the charging and discharging power control instruction to the energy storage converter for execution. According to the method, through deep reinforcement learning, electricity price accurate prediction and battery health dynamic modeling are combined, and charge and discharge strategy collaborative optimization of the energy storage system in a complex market environment is realized.

The invention discloses an autonomous charging dynamic control system for an inspection robot, and relates to the technical field of inspection robot management, and the system comprises a condition triggering module, a robot sensing module, a task decision module, a dynamic charging control module, a safety monitoring module and a log recording module. When a new inspection task packet is issued to a waiting or charging robot, a condition triggering module starts judgment, and a robot sensing module collects real-time electric quantity, task attributes and a charging pile state and encapsulates data; the task decision module calculates a task emergency degree and constructs a priority, and compares a charging basic priority to trigger a temporary or safe charging strategy; the dynamic charging control module dynamically adjusts charging parameters or calls safety parameters according to the dynamic charging parameters; the safety monitoring module monitors and intervenes in the charging process in real time, the log recording module records key operation and abnormity, and the system can dynamically adapt to task requirements, improves response efficiency and safety and is suitable for complex inspection scenes.

The invention relates to the technical field of energy storage battery control, in particular to a high-reliability low-attenuation energy storage battery management system based on intelligent control, and the system comprises a data collection module which is used for collecting battery temperature, environment temperature, SOC (state of charge), SOH (state of health) and external mechanical pressure information; the model construction module is used for constructing a battery attenuation risk dynamic coupling evaluation model and generating a battery safety and attenuation risk dynamic evaluation diagram; the self-adaptive adjustment module adopts a cross-dimension multi-stage cooperative control strategy algorithm, temperature compensation current control and SOH grading current limiting linkage are combined, and SOC dynamic charging optimization and mechanical pressure early warning are coupled; and the evaluation report module adjusts control parameters through a dynamic optimization algorithm, generates a battery health state evaluation report, synchronously uploads the battery health state evaluation report to a cloud database, and establishes a full-life-cycle battery attenuation risk file. Therefore, the problems that in the prior art, a risk assessment model is large in limitation, a control strategy lacks collaboration, and attenuation risk prevention and control lags are solved.

The invention relates to the technical field of power systems, provides an electricity price and energy storage joint optimization method and system in an electricity market environment, and aims to solve the problem that in the prior art, deep cooperation between electricity price and energy storage and a market mechanism is difficult to achieve. The method comprises the steps of obtaining actual and predicted load data of a power grid, and generating load prediction error distribution of a power market; according to load prediction error distribution, actual and predicted load data of the power grid and available rotation kinetic energy of the composite flywheel array, active power compensation is carried out on power shortage of the power grid; acquiring a real-time electricity price signal of each transaction period in the power grid, and generating a dynamic charging and discharging threshold value; generating a dynamic charging and discharging strategy of the energy storage equipment based on the dynamic charging and discharging threshold value; and based on the compensated power and a dynamic charging and discharging strategy, suppressing the electricity price fluctuation of the power vacancy generation node. According to the invention, suppression of instantaneous electricity price suddenly rising and market fluctuation closed-loop control are realized, and the problems of physical response delay and market strategy separation are synchronously solved.

The invention provides a multi-mode vehicle charging and parking cooperative management method based on the Internet of Things, and relates to the technical field of intelligent parking. The application steps of the main control system are multi-modal data acquisition and dynamic parking and charging constraint initialization, multi-scale parallel parking and charging model construction and preliminary distribution, multi-scale error feedback and cross-node collaborative evaluation, and adaptive constraint adjustment and asynchronous distributed iterative optimization. Global parking and charging scheme integration, expandability verification, output optimization scheme and industrial application packaging are carried out, multi-mode data such as vehicle electric quantity, position and parking lot state are collected in real time through the Internet of Things, changes of charging and parking demands can be found in time, the problems of congestion and waiting caused by resource allocation lag are effectively avoided, and the service life of a vehicle is prolonged. The real-time performance and efficiency of management are improved, and dynamic parking and charging constraints are generated by adopting a deep reinforcement learning algorithm.

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 discloses a battery energy storage health state detection and evaluation method and system, and relates to the technical field of battery energy storage state monitoring and evaluation, and the method comprises the steps: employing an SOC estimation method, combining an unscented Kalman filter and a Thevenin model to calculate the state of charge of a battery in real time, integrating the multi-dimensional feature data of the battery, inputting the data into an isolation forest algorithm, and carrying out the detection and evaluation of the energy storage health state of the battery. And carrying out deep anomaly detection on the battery sample with the abnormal frequency, and carrying out battery health state evaluation through a multi-stage anomaly detection result. SOC estimation is combined with an unscented Kalman filter, a Thevenin circuit model and an isolation forest algorithm to construct a self-adaptive and high-robustness multi-stage health assessment and anomaly detection system, the unscented Kalman filter performs high-precision prediction on nonlinear state transition, and the robustness of the health assessment and anomaly detection system is improved. The method effectively improves the estimation precision of the SOC under dynamic charging and discharging conditions and the accuracy of anomaly detection, and is suitable for monitoring and safety management of a battery system under complex environments and working conditions.

The invention relates to the field of power simulation optimization, in particular to a new energy charging simulation optimization method and device based on a charging and discharging test platform. The method comprises the following steps of performing new energy charging pile charging simulation based on a charging and discharging test platform, performing adjustable power time period analysis, and generating a plurality of adjustable power windows; performing timing sequence energy flow trend change mining and dynamic power distribution fitting according to the plurality of adjustable power windows, and constructing a dynamic power path distribution map; performing minimum power drift point detection according to the dynamic power path distribution map, and performing power fluctuation compensation inversion to obtain power fluctuation compensation configuration parameters; and performing local dynamic power regulation according to the power fluctuation compensation configuration parameters, and constructing a local window dynamic power regulation strategy. Through dynamic charging simulation, the power stability of new energy charging is improved, and the power response balance is optimized.

The invention discloses a parking lot dynamic charging task distribution method for a multi-robot system, and relates to the technical field of parking lot charging task distribution, and the method comprises the following steps: generating a path interference relation matrix between charging task paths based on the charging achievable degree and the path passing state of each robot, determining a task suppression coefficient for path conflict judgment; according to the determined task suppression coefficient and path interference relation matrix, target distribution and path sequence rearrangement of the charging piles are carried out, and a passing priority sequence between tasks is established; and predicting a change interval of path passing resistance based on the historical path state and the real-time passing data, and updating the effective remaining time of each robot to correct the task priority. According to the method, the problem of charging priority failure of multiple robots under the condition of path congestion is solved, collaborative optimization of task allocation and path scheduling is realized, and the system stability and the task completion rate are improved.

The invention provides a battery car dynamic charging optimization management system based on battery health state evaluation, and belongs to the technical field of battery car charging management. The system comprises a data acquisition module which is connected with an environment sensing device and a battery management system and acquires environment and battery parameters in real time; the data storage module is used for storing an initial training data set, a charging and discharging data set and a battery aging feature library; the model building module is used for building a battery health state evaluation model through a migration strategy and building a response model through a modeling strategy; the health state evaluation module is used for extracting associated features with a critical damage threshold value as a core through a mapping algorithm based on the battery parameters, the environmental parameters and an aging feature library; and the charging strategy generation module comprises a strategy generation unit, a reference parameter generation unit and a dynamic correction unit. The system effectively improves the adaptability of battery car charging, guarantees the efficient and stable operation of the battery, and remarkably improves the safety and prolongs the service life of the battery.

The invention discloses a new energy automobile dynamic charging power adjustment method considering a power grid load, and aims to solve the problems of power grid overload and non-fine regulation and control caused by concentrated charging of new energy automobiles. The method is executed by a regional charging coordination server and comprises the following steps: acquiring a real-time load value and a safety threshold value of a target line of the power distribution network; collecting real-time charging aggregation data of each charging station in the jurisdiction, wherein the real-time charging aggregation data comprises the power of each charging pile and the state of charge of a vehicle battery; calculating the load margin of the power grid and determining the total charging power needing to be adjusted; the charging urgency degree coefficient of each charging pile is calculated based on the state of charge of the vehicle battery, the total adjustment amount is distributed to each charging station according to the coefficient and the total power of the charging stations, and the initial power adjustment amount is generated; according to the invention, the real-time, collaborative and refined regulation and control of the charging load from the global perspective of the power grid is realized, under the premise of ensuring the safe operation of the power grid, the highly urgent charging demand is preferably met, and the resource allocation and user experience are optimized.

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 a collaborative optimization method for an optical storage and charging integrated power station, and the method comprises the steps: employing a CNN-BiLSTM-KAN cascade feature extraction architecture and a prediction method of an enhanced queuing model based on K-means for the obtained basic data of the power station, and obtaining an intra-day photovoltaic output prediction result and a dynamic charging load prediction result respectively; establishing a power station optimization economic operation model considering the energy storage life loss, taking the prediction result as the input of the power station optimization economic operation model, constructing a rolling optimization mechanism through behavior constraint conditions of the model and a model prediction control framework, and solving the model to obtain a power station optimization economic operation strategy; and finally, according to the strategy, regulating and controlling the charging and discharging of the electric quantity energy storage in each time period in the dispatching period of the photovoltaic storage and charging integrated power station. According to the method, the photovoltaic power generation and charging load prediction precision is improved, collaborative optimization of energy storage economy and service life is realized, and the problems of inaccurate prediction, extensive scheduling and the like of a photovoltaic power station with integrated storage and charging are solved.

The invention provides a dynamic impedance matching charger based on battery aging, a method, electronic equipment and a medium. The charger comprises a master control unit, and a battery state monitoring module, an aging grade evaluation module, a dynamic charging control module, a battery cell balancing module, an optimization control module and an anti-interference module which are connected with the master control unit to cooperatively realize charging closed-loop control. The battery state monitoring module injects a preset frequency alternating current signal and outputs an impedance parameter, a charge state and a temperature parameter; the aging grade evaluation module outputs a battery aging grade in combination with a preset model; the dynamic charging control module is matched with an adaptive charging mode instruction; the battery cell balancing module calculates the battery cell impedance difference, and balancing operation is executed if the battery cell impedance difference reaches the standard; the optimization control module generates an optimized current instruction through a preset algorithm according to the related parameters, and superposes the optimized current instruction to the reference current; the anti-interference module improves the signal-to-noise ratio of collected signals to a preset standard through isolation and shielding design.

The invention discloses an energy storage battery pack self-discharge abnormity diagnosis method based on short-time charging data, and belongs to the technical field of battery safety monitoring. In order to overcome the defects that an existing method depends on a complete charging and discharging period and is difficult to adapt to a multi-stage variable working condition scene, multi-stage charging data features are extracted, self-adaptive extraction of multi-working condition features is achieved in combination with an auto-encoder, a data driving model fusing GCN-BiLSTM (Graph Convolution-Bidirectional Long Short-Term Memory) is constructed, and a multi-stage variable working condition scene is obtained. According to the method, the RCC (Remaining Charge Capability) of the single battery is accurately estimated, and the RCC of the single battery is accurately estimated. And finally, on the basis of the size, the distribution and the change condition of each single RCC in the battery pack, the overall inconsistency of the battery pack, the SOC inconsistency of the single batteries and the self-discharge fault can be diagnosed. And a guarantee is provided for safe operation of the energy storage system.

The invention relates to the technical field of power bank control systems, in particular to an AI voice-based power bank control system, which comprises a voice interaction module, an equipment recognition module, an environment sensing module, a dynamic charging management module and a control center module, the voice interaction module is used for receiving a voice instruction of a user and analyzing the instruction content through a natural language processing technology; the device identification module is used for detecting the type of a connected device and a charging protocol, and obtaining the real-time charging demand of the device. The environment sensing module is used for acquiring an environment noise value # imgabs0 # and a temperature value # imgabs1 # of a charging environment; and the dynamic charging management module is used for dynamically adjusting the output power according to the equipment type, the environmental parameters and the historical charging data of the user. According to the invention, through environment perception and dynamic threshold determination, the voice interaction robustness is optimized, real-time adaptive adjustment of the charging strategy is realized, and the method is especially suitable for complex scenes such as travel, outdoor and the like.

The invention relates to the technical field of intelligent parking management, and discloses a curb parking dynamic charging and fee evasion prevention and control method based on multi-dimensional data fusion, which comprises the following steps: a data fusion reconstruction step: receiving a video stream, sensing a parking space state and reporting data through mobile inspection, performing fusion processing through space-time alignment and a confidence decision algorithm, and obtaining a data fusion result; outputting a standard parking event sequence; a dynamic charging decision-making step of matching a charging strategy model including time-phased elastic rate parameters according to the time-space attribute of the event sequence, calculating the cost and verifying the parking rights and interests in real time, and generating a parking order; and a fee evasion risk real-time prevention and control step: constructing a vehicle behavior portrait feature vector based on the event sequence and the historical order, obtaining a real-time risk level through a pre-trained risk grading model, and triggering a cascade response disposal action according to the real-time risk level, including exit interception and recovery of a high-risk vehicle. According to the invention, the charging accuracy is improved, and the rate dynamic adjustment and the active and accurate prevention and control of the fee evasion behavior are realized.

The invention discloses a 5G base station micro-photovoltaic multi-source data dynamic charging method and system, and belongs to the technical field of 5G base station charging. Multi-source data (including a base station operation state, weather forecast and a regional power consumption task) are integrated, an LSTM and XGBoost fused fault prediction model, time convolutional network power consumption demand prediction and a multi-target optimization model (optimizing power grid power purchase cost, energy storage loss and photovoltaic utilization rate) are adopted, a charging strategy is dynamically generated in combination with a cloud, and the power consumption of the power grid is optimized. And the strategy is adjusted in real time based on the fault prediction result. According to the method, the optimized strategy is executed by remotely controlling the micro-photovoltaic equipment, global energy scheduling driven by multi-dimensional data is realized, the photovoltaic utilization rate is remarkably improved, meanwhile, through a fault feedforward mechanism and dynamic weight adjustment, the operation cost is reduced on the basis of guaranteeing the power supply stability, and the power supply efficiency is improved. The problems of data island, response lag, fault vulnerability and the like in a traditional scheme are solved.

The invention discloses a high-reliability storage unit fault-tolerant control system, which relates to the technical field of storage unit fault-tolerant control and comprises a dynamic charge mapping model module, a potential inversion driving link module, a multi-channel phase conjugate regulation and control module, a nonlinear charge turn-back channel module and a self-adaptive capacitance balance control module. And the dynamic charge mapping model module is used for constructing a dynamic charge mapping model, extracting a potential transition curve in a high-frequency read-write switching stage by taking a parasitic capacitance real-time response parameter of a parallel bit line in the storage unit as an input variable, and generating a dynamic coupling map for identifying a pseudo-synchronous charge accumulation region based on the potential transition curve. According to the invention, real-time suppression and energy balance of parasitic capacitance are realized through dynamic charge mapping and potential inversion, and potential stability of a bit line is maintained; and energy closed-loop control is realized through nonlinear charge turn-back and self-adaptive capacitance regulation and control, charge regeneration is prevented, so that the stability and the reliability of the storage unit are improved, and the service life of the storage unit is prolonged.

An electric vehicle charging control device includes an electronic dynamic charging schedule generator, a non-transitory computer readable medium and an electronic communicator. The non-transitory computer readable medium stores vehicle profile data and building profile data. The electronic communicator is configured to receive profile updates to the vehicle profile data from an electronic user interface. The electronic controller is programmed to generate a driver charging profile based on the vehicle profile data and the building profile data. The electronic controller is programmed to update the driver charging profile based on the electric vehicle user's behavior. The electronic controller is programmed to generate a charging schedule for the electric vehicle based on the updated driver charging profile. The electronic controller is programmed to controlling the charging port in accordance with the charging schedule upon the electric vehicle user confirming acceptance of the charging schedule on the electronic user interface.

The invention belongs to the technical field of charging trend analysis, and relates to an electric vehicle charging amount trend analysis method based on time variation, which comprises the following steps: collecting charging pile terminal, power grid system and environment data and user behavior log data, extracting basic features and derivative features, and then adopting an STL decomposition method and wavelet transform to obtain a charging amount trend of an electric vehicle; performing trend, period and residual term decomposition on the time sequence data, combining geographic information and space-time correlation modeling, and adopting spatial clustering analysis and covariable analysis to obtain a space-time correlation matrix of the regional charging demand, thereby revealing the change rule of the charging demand at different time and places; and finally, capturing a seasonal effect by using a Prophet model, capturing a long-term dependency relationship in combination with an LSTM model, and obtaining a more accurate dynamic charging amount prediction result through weighted fusion of output of the two. According to the method, high-precision, dynamic and robust prediction of the charging amount is realized, and the defects of a traditional method in processing complex space-time dynamic changes and environmental factors are overcome.

Embodiments are provided for dynamic management of charge. The techniques may involve charging, using a first charging rate, an energy storage element to an intermediate state of charge, wherein the energy storage element is interchangeable with an in-use energy storage element. The techniques may further involve responsive to at least an identification of an anomalous condition of a device powered by the in-use energy storage element, the anomalous condition resulting in the in-use energy storage element being discontinued from use, charging, using a second charging rate, the energy storage element to a target state of charge, wherein the second charging rate is different from the first charging rate.

An indication that a ride associated with an electric vehicle has ended is received. An availability state of the electric vehicle is determined based at least on a current battery charge level of the electric vehicle and a dynamic charge threshold. Information reflecting the availability state of the electric vehicle in a set of geographic location and availability state information of vehicles comprising a fleet of electric vehicles available to be displayed is included via a user interface based at least in part on the respective availability state of the respective electric vehicles comprising the fleet of electric vehicles.

The invention relates to the technical field of charging pile operation management, and discloses a charging pile intelligent operation management method based on the Internet of Things, and the method comprises the steps: setting a chargeable time period at a user terminal by a user; executing a dynamic charging interval strategy, obtaining battery parameters and historical mileage data of the electric vehicle, and generating a charging electric quantity interval in combination with the chargeable time period; executing a dynamic charging scheduling strategy, matching the number of users of each charging pile according to the charging electric quantity interval and the chargeable time period, and calculating a user priority by using the charging electric quantity interval to process a matching conflict; acquiring priorities of all users, and selecting the users in sequence from small to large according to the user priorities; screening standby scheduling users under a cross-regional scheduling distance threshold according to positioning; and executing a grading standby scheduling strategy, and determining a standby charging pile for the user in the chargeable time period according to the target areas of the user and the standby scheduling user, thereby enhancing the operation management level and the user experience consistency.

The invention relates to the technical field of photovoltaic energy storage, in particular to a photovoltaic energy storage direct current intelligent microgrid monitoring management system and method, and the method comprises the steps: predicting the predicted photovoltaic power generation amount of a parking shed in a period of time in the future based on weather forecast parameters and historical power generation data; predicting a charging curve based on historical data; calculating an insufficient generating capacity curve based on the charging curve and the predicted photovoltaic generating capacity of the parking shed; the energy storage equipment is divided into a standby state and a charging state, and when the value of the insufficient generating capacity curve is a positive value, the working states of the two kinds of energy storage equipment are switched; when the insufficient power generation curve is a positive value, generating a power grid supply curve based on the insufficient power generation curve; calculating a dynamic charging price based on the power grid supply curve; and sending the dynamic charging price to the user side. Therefore, the generating capacity and the power consumption can be predicted, corresponding energy planning is performed in advance, and the working efficiency is improved.