10577 results about "Power station" patented technology

In an intelligent pollution and carbon reduction method based on combustion control and load distribution, a data processing layer, a multi-unit load distribution and operation optimization layer and a single-unit boiler multi-objective combustion optimization layer are used. The data processing layer, the multi-unit load distribution and operation optimization layer and the single-unit boiler multi-objective combustion optimization layer are embedded in a power plant information system in the form of modules. A load distribution and operation optimization method for a multi-source fuel blending combustion unit with economy as an objective is provided to overcome the operation optimization difficulty of the key production process of a multi-source fuel blending combustion cogeneration unit, such as sludge drying-steam distribution.

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 discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention relates to the technical field of geometric quantity precision measurement based on satellite positioning, in particular to a pumped storage power station dam safety monitoring method based on Beidou positioning, and the method comprises the steps: arranging a Beidou monitoring station and base station array in a dam body and a slope region, synchronously receiving multi-band satellite signals, and carrying out the edge calculation processing; and the effective frequency band data are screened and fused by using a signal-to-noise ratio threshold to generate preprocessed observation data, and the preprocessed observation data are uploaded to a cloud server in real time through a 4G network and a Beidou short message dual-channel transmission link. A cloud server constructs a local enhanced network, ionosphere and troposphere errors are corrected by adopting a multi-base-station joint error modeling method, a closed-loop mechanism dynamically optimizes monitoring point distribution density, signal processing parameters and atmospheric refraction compensation coefficients, and data integrity is guaranteed in combination with a dual-channel redundancy check and interpolation completion technology. According to the invention, full-domain millimeter-level deformation dynamic monitoring of the dam under the complex terrain is realized, and the space-time reference uniformity, the data real-time performance and the structure safety evaluation precision are improved.

The invention relates to the technical field of pumped storage power station multi-sensor monitoring network time-space synchronous measurement, in particular to a pumped storage power station multi-sensor safety monitoring system, which comprises a sensor data acquisition module, a time-space synchronous network module, a data calibration module, a dynamic association model construction module, a multi-dimensional data fusion module and a graded early warning module. The dynamic correlation model building module simulates dynamic response of seepage pressure and surrounding rock strain through a fluid-structure interaction equation and a discrete element digital twinborn model, updates fracture permeability boundary conditions in real time, and extracts cross-sensor space-time correlation characteristics by adopting a convolutional long-short-term memory network improved by a multi-head attention mechanism. According to the method, the real-time early warning precision of abnormal events and the reliability of structural health assessment in the complex hydraulic and mechanical coupling environment are improved.

The invention relates to the technical field of electric energy storage systems, in particular to an energy storage power station operation monitoring management system and method. The method comprises the following steps: obtaining voltage data of each energy storage node in a standing state, and carrying out periodic voltage sampling within a preset standing time, thereby obtaining voltage springback time sequence data; performing temperature correction processing on the voltage springback time sequence data so as to obtain standardized voltage springback characteristic data; acquiring charge state data of each energy storage node before and after operation; calculating a charge-discharge depth change value of each node according to the charge state data; and constructing a node health state evaluation model according to the standardized voltage rebound characteristic data and the charge and discharge depth change value, and obtaining node aging differentiation parameters. According to the method, the node charging and discharging power allocation model is constructed based on the node aging differentiation parameters, differentiation management of energy storage nodes in different states is achieved, and the problem that the aging degree between the nodes is further expanded in a traditional method is solved.

The invention discloses a multi-user energy storage power station charging and discharging control method and system based on electricity price, and the method comprises the steps: arranging a monitoring terminal at energy storage equipment, and collecting the load data of each energy storage equipment; carrying out data preprocessing on the collected load data; constructing a cascade neural network prediction model, training the model by using the preprocessed load data, and generating a regional total load demand prediction value in a future time period; designing a deep reinforcement learning model DRL by using the regional total load demand predicted value to obtain a preliminary power adjustment amount; designing a target function considering the battery aging cost by using the initial power adjustment amount, and determining an initial value range of a coefficient in the target function of the optimization model according to a constraint condition; and a multi-objective optimization problem is considered, a Pareto optimal solution set is obtained based on the initial value range, and a final power distribution scheme is determined. The method can accurately predict the load, optimize the charging and discharging strategy, reduce the power consumption and operation cost, balance the service life of the battery and the economic benefit, and improve the intelligent and efficient level of the energy storage power station.

The present application relates to the technical field of power plant optimization, and discloses a thermal power plant auxiliary power system optimized dispatching method and system considering a wind-solar-storage system, and a device and a storage medium. The method specifically comprises: collecting thermal power plant auxiliary power system data, and establishing an auxiliary power system multi-objective function on the basis of the thermal power plant auxiliary power system data and a wind-solar power generation cluster model in an auxiliary power system; introducing constraint penalties and constraint conditions to the auxiliary power system multi-objective function, and constructing a thermal power plant auxiliary power system optimized dispatching model; and processing the thermal power plant auxiliary power system optimized dispatching model by using a dynamic learning factor-based particle swarm algorithm to obtain an optimized dispatching result, and completing thermal power plant auxiliary power system optimized dispatching on the basis of the optimized dispatching result. According to the present application, the optimal interactive output among wind turbine units, photovoltaic units, energy storage units, and a generating set can be determined on the basis of the optimized dispatching result, auxiliary power system low-carbon optimized dispatching is implemented, and the problem in the prior art of lacking dispatching in which new energy and thermal power plant auxiliary loads are integrated for analysis is solved.

The invention relates to the technical field of thermal power generation, in particular to a thermal power plant fuel intelligent supervision system and method based on big data analysis and early warning, and the system comprises a multi-modal data sensing module, a hierarchical enhanced decision module, a real-time early warning and evaluation unit, and a digital twinborn decision center. Wherein the multi-modal data sensing module is used for constructing a fuel digital twinborn body; the hierarchical enhanced decision module is used for constructing a double-ring intelligent decision system and performing hierarchical optimization and full life cycle management; the real-time early warning and evaluation unit is used for acquiring data, performing deep mining, risk identification and dynamic adjustment of an early warning threshold in combination with a reinforcement learning algorithm, and grading the risks; the digital twinborn decision center performs virtual deduction by means of a digital twinborn model, generates a target strategy through a multi-target optimization algorithm, ensures instruction traceability, and dynamically adjusts the strategy according to real-time data. Therefore, the problems of limited data processing capability, low model adaptability and the like in the prior art are solved.

The present invention relates to the detection of the error in photovoltaic panels on a panel cell basis by thermal inspection from unmanned aerial vehicle images with artificial intelligence technology, performing temperature measurement, model conversion of images from two dimensions to three dimensions, real-time solar simulation and field navigation analysis.

The invention relates to the technical field of power station operation and maintenance, and discloses a power generation equipment intelligent operation and maintenance method and system based on model autonomous selection, and the method comprises the steps: obtaining the multi-mode operation and maintenance data of a photovoltaic power station, and generating a multi-mode operation and maintenance data set; inputting each modal data of the multi-modal operation and maintenance data set into a corresponding module for feature extraction; inputting the extracted feature vectors into a contrast learning network for cross-modal alignment, and outputting an executable decision result by combining a retrieval enhancement generation technology with a knowledge graph; and constructing a privacy protection training framework through federated learning, inputting an executable decision result into a digital twin system for strategy verification, and generating a trained multi-modal large model for the photovoltaic power station to select a corresponding module in the trained multi-modal large model based on the feature data for real-time monitoring. According to the invention, the problems of poor accuracy and untimely reaction in the traditional operation and maintenance process of the photovoltaic power station are solved, and the operation and maintenance of the power station can be carried out timely and accurately.

The disclosure provides an energy-saving control system and method for a boiler in a thermal power plant. The system includes: a prediction module, used for predicting current boiler operating efficiency; an optimization module, used for optimizing boiler input parameters based on a particle swarm algorithm, determining an optimal operating condition parameter combination, and setting adjustment steps of the optimal operating condition parameter combination; an emission detection module, used for obtaining NOx emission data of the boiler in thermal power plant and calculating a NOx emission fluctuation degree according to the NOx emission data; a parameter correction module, used for setting emission weight according to the fluctuation degree and setting a correction value of the optimal operating condition parameter combination according to the emission weight; a regulating and controlling module, used for regulating and controlling the boiler of the thermal power plant.

The invention discloses an intelligent management platform suitable for a power plant, and relates to the technical field of power plant management, and the platform employs an intelligent scheduling system and a DDPG algorithm, and dynamically adjusts a power generation strategy according to the real-time equipment health score, residual life prediction, and the change of energy price and load demand. The power plant can quickly make adjustments when the load demand changes sharply, preferentially dispatch efficient and low-emission equipment, improve the overall energy efficiency and reduce energy waste, and through integration of a sensor network and edge computing, key operation parameters of the equipment are collected in real time, and a dynamic maintenance plan is generated based on health scores and residual life prediction of the equipment. The equipment about to break down is preferentially processed, the random forest model is used for health state evaluation and fault probability prediction, the health score of the equipment can accurately reflect the running state of the equipment, the maintenance time is dynamically adjusted in combination with the load condition of the equipment, and excessive maintenance or maintenance lag is avoided.

The invention discloses a new energy power station intelligent charging and discharging control method based on a battery health state, and relates to the technical field of new energy electric power, and the method comprises the steps: collecting the energy storage data of a battery, training a battery health state evaluation model, taking the energy storage data of the battery as input data, and outputting an SOH value; the SOH value, the charge-discharge rate, the temperature and the discharge depth of the battery in the charge-discharge cycle process are collected, a battery life prediction model is trained, and the residual cycle life of the battery is predicted; defining an optimization target and constraint conditions, and solving an optimal balanced charging and discharging strategy; the SOH value of the battery is predicted in real time, the SOH difference is calculated, and when the SOH difference is larger than a preset difference threshold value, optimization of a next round of balanced charging and discharging strategy is triggered; the batteries with the SOH values smaller than the decommissioning threshold value are listed as decommissioning batteries, standing and charging and discharging experiment testing are conducted on each decommissioning battery, the available capacity percentage of each decommissioning battery is calculated, and the echelon utilization scene is matched for each decommissioning battery.

The invention provides a power plant heat tracing system intelligent optimization method based on environment temperature fluctuation, and relates to the technical field of temperature control of a power plant heat tracing system, and the method comprises the following steps: S1, data collection and preprocessing: deploying a sensor array, collecting an operation data signal # imgabs0 # of a power plant heat tracing pipeline, carrying out the preprocessing of the operation data signal # imgabs1 #, and storing the preprocessed operation data signal # imgabs0 #; outputting a standardized feature vector; s2, model establishment and parameter optimization: constructing a physical-data dual-drive heat transfer model, and inputting standardized feature vector optimization initial parameters; compared with the prior art, the method has the following beneficial effects: firstly, control parameters can be automatically adjusted according to factors such as thermal load change, external environment temperature fluctuation and equipment aging; and secondly, the temperature trend in the future short time can be predicted by constructing a heat transfer dynamic model, the control strategy is adjusted in advance, the temperature is prevented from being out of control, heat dissipation can be increased in advance or heat source input can be adjusted in advance, and safe and efficient operation of the system is effectively guaranteed.

The invention provides an intelligent optimization method and system for a revenue mode of a commercial energy storage power station, and relates to the technical field of energy storage power stations. Establishing a multi-dimensional life loss coefficient matrix by analyzing the health state data of the energy storage equipment; a candidate charging and discharging scheduling strategy is generated based on a deep reinforcement learning model, and a double-constraint optimization charging and discharging strategy with predicted income and life loss as a reward function is adopted; and finally generating a control instruction to realize optimized operation of the energy storage power station. According to the method, equipment life loss and economic benefits are comprehensively considered, and long-term benefit maximization of the energy storage power station is realized.

An early warning method for battery health degree and residual life of a medium-and-large-sized electrochemical energy storage power station belongs to the field of battery health assessment, and comprises the following steps: firstly, establishing a battery health state grading system, determining threshold ranges of key parameters such as voltage, internal resistance and charge and discharge capacity, training historical data through various algorithms such as a support vector machine to construct a discrimination model, and calculating the battery health degree and residual life of the medium-and-large-sized electrochemical energy storage power station; and the health level is accurately evaluated. The health detection system monitors data in real time, an evaluation model is constructed through preprocessing and correlation analysis, and the health level is judged and a report is generated according to the evaluation model. And for a sub-health battery unit, determining an isolation priority according to fuzzy comprehensive evaluation, unloading and predicting a deterioration trend to formulate a maintenance plan. And during discharging, the battery is selected to discharge and power is adjusted according to the health level. According to the method, historical data are clustered and analyzed by using a machine learning algorithm to construct an evaluation model to predict a new data level, and an energy storage system operation strategy is dynamically adjusted based on the health level, so that economy and reliability are improved.

The invention relates to the technical field of new energy power station control, and particularly discloses a new energy power station perception control method and system, and the method comprises the steps: S1, data collection, S2, state perception, S3, monitoring and early warning, S4, decision and optimization control, S5, execution and feedback, S6, intelligent operation and maintenance, and S7, self-healing scheduling. The deep neural network DNN is utilized to process a large amount of sensor data, so that the system can learn modes from complex historical data, accurately identify conditions such as power fluctuation and voltage abnormity, and timely discover potential abnormity through real-time updating of the model, so that the possibility of misinformation and missing report is reduced, and the reliability of the system is improved. The system monitors the operation state of the power station in real time, can quickly recognize and classify abnormal conditions, provides instant data support for decision making, can take measures in advance through abnormal prediction, prevents problems from being further expanded, and can recognize equipment needing to be maintained and prevents faults from being expanded through patrol inspection and prediction analysis of the unmanned aerial vehicle.

The invention provides an intelligent scheduling control method for an optical storage charging and discharging integrated power station, and the method constructs a multi-time-scale prediction-optimization-correction three-stage control architecture, and achieves the real-time scheduling of the optical storage charging and discharging integrated power station through the coordinated operation in the upper-layer 24-hour rolling optimization, the middle-layer real-time scheduling and the lower-layer equipment control. And multi-target and multi-time-scale coordinated control optimization of maximization of the photovoltaic consumption rate, minimization of the power grid electricity purchase cost and prolonging of the service life of the energy storage system is realized. And a model prediction control algorithm is combined to dynamically adjust photovoltaic power generation output, an energy storage charging and discharging plan and a charging pile scheduling strategy. In addition, an adaptive parameter adjustment mechanism is introduced, and MPC parameters are adjusted online by using a reinforcement learning algorithm so as to cope with dynamic changes of environmental parameters and equipment states. The method can effectively improve the operation efficiency and economy of the optical storage charging and discharging integrated power station, prolongs the service life of an energy storage system, and is suitable for intelligent scheduling control of various optical storage charging and discharging integrated power stations.

The invention discloses a power station photovoltaic field area automatic inspection unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle inspection, and the unmanned aerial vehicle comprises the following components: a meteorological self-adaptive intelligent inspection flight system, a power supply module, a rotor wing device and a communication module; the power supply module is used for providing power support for each component of the unmanned aerial vehicle; the communication module is used for realizing data interaction between the unmanned aerial vehicle and the ground control station and between the unmanned aerial vehicle and the photovoltaic field area monitoring equipment; by carrying the meteorological self-adaptive intelligent inspection flight integrated system, meteorological conditions of a photovoltaic field area can be perceived and analyzed in real time, and an inspection path and a flight attitude are dynamically optimized according to the meteorological conditions, so that the unmanned aerial vehicle can preferentially inspect an area where a problem is most likely to occur, and the inspection efficiency is remarkably improved; meanwhile, the unmanned aerial vehicle can keep stable flight under complex meteorological conditions such as strong wind, flight accidents are avoided, and the accuracy of inspection data is ensured.

The invention discloses a photovoltaic power station intelligent remote operation and maintenance method and system based on relay protection, and the method comprises the steps: collecting the multi-mode characteristic quantity of a photovoltaic array region relay protection device in real time through a distributed optical fiber sensing network, so as to construct a dynamic protection characteristic matrix; performing feature decoupling on the dynamic protection feature matrix by adopting a space-time convolution adversarial encoder to generate a fault feature vector set; inputting the fault feature vector set into a physically constrained LSTM-GAN hybrid prediction model, and predicting a multi-dimensional failure probability tensor of the key relay protection node in a future preset duration; generating an optimal operation and maintenance strategy decision tree based on the multi-dimensional failure probability tensor; and analyzing the operation and maintenance strategy decision tree through a remote execution gateway driven by the digital twinborn, and synchronously updating the topological connection relationship of the digital twinborn bodies of the power station. According to the embodiment of the invention, the operation and maintenance efficiency and reliability of photovoltaic power station protection can be improved.

The invention provides a photovoltaic energy storage power station battery pack temperature intelligent regulation and control and performance optimization method and system, and relates to the technical field of energy storage battery packs, and the method comprises the steps: obtaining temperature sensor array data, working state data and environment parameter data, and predicting the temperature field distribution through a deep neural network model; and constructing a temperature field topological relation graph by adopting a graph neural network, constructing a hierarchical progressive temperature control strategy based on the temperature field feature vector, generating a cooling system control instruction, and realizing temperature field intelligent optimization control of the battery pack. The method can accurately predict the temperature field distribution, improves the working efficiency and service life of the battery pack, and reduces the energy consumption.

The invention relates to the technical field of energy storage power station combined operation and maintenance management, and provides a virtual power plant-oriented energy storage power station combined operation and maintenance management system, which comprises a dynamic sensing module for acquiring battery cluster temperature difference distribution, power converter switch transient characteristics and grid-connected point harmonic spectrum data of an energy storage power station in real time through a multi-dimensional sensor network; the collaborative decision-making module is used for dynamically generating a cross-station power interaction constraint rule based on the virtual power plant topological structure; the impedance reconstruction module adjusts the equivalent output impedance of the energy storage converter in real time according to the impedance characteristics of the power grid; the state balancing module is used for realizing charge state dynamic balance and power margin collaborative distribution among the multiple energy storage power stations; and the secure communication module is used for ensuring secure transmission of control instructions among the modules by adopting a layered encryption protocol. The operation stability, the power distribution flexibility and the equipment safety of the virtual power plant can be improved, and the service life of the energy storage equipment is prolonged.

The invention discloses a green data center scheduling optimization method under computing power and electric power coupling, data load types of a data center comprise a transferable load, a reducible load and a rigid load, and the method comprises the following steps: constructing a double-target scheduling optimization model by taking the minimum power consumption cost of the data center and the maximum green power fitting degree as targets; the power consumption cost of the data center is used for representing the sum of expenses generated by the data center for purchasing power from a power grid in order to meet the power required for processing the data load in each time period, and the green power fitting degree is used for evaluating the matching degree between the power consumption demand of the data center and the renewable energy source supply; solving the dual-target scheduling optimization model by using a multi-target dogvessel swarm algorithm to obtain a Pareto frontier; and determining an optimal scheduling scheme based on the Pareto frontier, wherein the optimal scheduling scheme comprises a data center power consumption curve, an energy storage power station charging and discharging curve and a transferable load scheduling strategy. According to the invention, the power consumption cost of the data center can be reduced, and the renewable energy consumption capability can be improved.

The invention provides a multi-mode cooperative sensing power station high-risk operation inspection method and system, and relates to the technical field of video recognition, and the method comprises the steps: activating an unmanned plane and a quadruped robot after a power station operation task is started; starting a video acquisition unit, and establishing a synchronous video stream; carrying out fusion alignment with the global reference coordinate system through an external synchronization signal; inputting the video sequence of the fused view angle into a multi-view angle action behavior recognition network, and establishing a dangerous behavior grade score; auditory data and olfactory data of the quadruped robot are obtained, and linkage abnormity is established; and polling abnormity is reported according to linkage abnormity and dangerous behavior grade scores. Through the method and the device, the technical problem of low inspection efficiency caused by difficulty in comprehensively identifying potential risks in a dynamic environment due to limitation of a single sensing mode is solved, and the inspection efficiency of a power station is improved by fusing multi-mode data, timely finding and processing the potential risks and improving the accuracy of video and audio identification.

The present application relates to the field of electric power, and provides a knowledge graph-based energy storage power station fault identification method and apparatus. The knowledge graph-based energy storage power station fault identification method comprises: acquiring state data of an energy storage power station; inputting the state data into a pre-constructed energy storage power station fault identification model, and predicting a fault condition and an evolution path of the energy storage power station, wherein the energy storage power station fault identification model is constructed on the basis of a thermal runaway knowledge graph, and the thermal runaway knowledge graph is used for representing an association relationship between the state data of the energy storage power station and a thermal runaway process; and on the basis of the fault condition and the evolution path, determining a safety risk level of the energy storage power station. The present application can comprehensively and accurately evaluate the operation condition of the energy storage power station, and achieve long-time fault early-warning.

The invention provides a photovoltaic power station design system and method based on ecological collaborative optimization, and relates to the technical field of data processing. The system comprises an initial configuration module which is used for generating an initial layout structure of a photovoltaic module, performing energy-saving and consumption-reducing calculation and economic analysis according to generating capacity data, and obtaining initial energy-saving and consumption-reducing data and initial economic analysis data; the secondary optimization module is used for optimizing the parameters of the photovoltaic module based on the economical efficiency analysis data and determining energy-saving and consumption-reducing data and economical efficiency analysis data through the generating capacity optimization data; the ecological evaluation module is used for determining ecological benefit data of the photovoltaic power station according to the ecological influence parameters; and the collaborative optimization module is used for setting a power generation income weight and an ecological benefit weight so as to optimize the photovoltaic module layout structure and generate a photovoltaic power station design scheme based on ecological collaborative optimization. The method is used for realizing collaborative optimization of power generation benefits and ecological benefits of the photovoltaic power station, so that the power station gives consideration to ecological protection while improving the power generation efficiency.

The invention provides a power grid evolution behavior modeling method and system based on dynamic digital twinning, and relates to the technical field of intelligent power grid digital twinning. According to the method, interaction characteristics of power station output, user load response and an energy storage scheduling strategy are captured in real time through a digital twin interface, behavior coupling factors containing complementarity and conflict weight are formed, and a power grid response relation and a multi-scale demand elastic curved surface are constructed by using an implicit tensor fusion technology and nonlinear projection. And through the steps of virtual behavior anchor point disturbance and the like, offset is separated to identify an unbalance region, an evolution trajectory is generated, an optimal collaborative path is extracted, and the weight of a behavior coupling factor is dynamically adjusted, so that rapid convergence of a global collaborative stability domain is realized. The method covers a behavior coupling factor generation module and the like, supports various operations, effectively solves many problems in power grid resource interaction, and improves dynamic adaptability and cooperation efficiency of power grid evolution modeling.

The invention provides an intelligent alarm system and method for monitoring power plant equipment, and the method comprises the steps: collecting the temperature, pressure and vibration data of the equipment through a sensor, and generating a holographic image through the scanning of a laser radar, so as to complete the data collection and environment modeling. A space-time atlas is constructed and analyzed by means of a space-time network, faults are analyzed and positioned by means of topological data, and data fusion and deep analysis are achieved. And constructing a Bayesian network by adopting intelligent learning and adaptive optimization of evolutionary strategy optimization and memory enhanced neural network prediction, and carrying out fault diagnosis and causal inference in combination with anti-factual reasoning. And the group intelligent collaborative diagnosis and emotion calculation aided decision is used for intelligent decision and collaborative operation, and finally, an operator is assisted to work based on a man-machine interaction function. The system and the method can comprehensively and accurately monitor equipment, efficiently diagnose faults and assist in decision making, and improve operation safety and reliability of a power plant.