6538 results about "Neutral network" patented technology

The invention relates to the technical field of flow measurement calibration, in particular to a flow instrument intelligent calibration system based on multi-sensor fusion, which comprises a data acquisition unit, a deep coupling compensation unit and a closed loop verification unit, a data acquisition unit obtains a differential pressure value, an environment temperature, a pipeline pressure, a vibration frequency spectrum and sensor accumulated working time, a depth coupling compensation unit constructs an aging prediction model based on a Weibull distribution life model, and a temperature-pressure coupling equation and a vibration compensation mechanism are combined to obtain an aging real-time value and a time sequence deviation. Multi-parameter coupling characteristics are extracted through a neural network, an environment disturbance compensation coefficient matrix is constructed, a joint compensation amount is generated through dynamic weight distribution, a closed-loop verification unit optimizes model parameters, the problems that multi-source disturbance coupling analysis is insufficient and calibration precision is low in the prior art are solved, accurate calibration of a flow instrument under complex working conditions is achieved, and the calibration precision is improved. The metering stability is improved.

The invention relates to the technical field of battery thermal management, in particular to a battery thermal management control method and system, and the method comprises the steps: obtaining the basic operation parameter data of an automobile battery in real time, and carrying out the multi-dimensional data fusion, and obtaining a multi-dimensional data set; based on the thermodynamic model and the multi-dimensional data set, the temperature distribution condition in the battery is calculated in real time, and real-time temperature distribution information is obtained; based on the historical data and the operation state, the future temperature change trend of the battery is predicted through a recurrent neural network model; dynamically adjusting a control strategy of the battery thermal management system according to the real-time temperature distribution information and the prediction result; and analyzing the temperature difference condition of the battery monomers, detecting abnormal temperature rise, and performing temperature difference balance adjustment and triggering a multi-stage protection mechanism when the abnormal temperature rise exceeds a threshold value. According to the invention, the problems of inaccurate temperature prediction and response lag in the prior art are effectively solved, the control strategy is dynamically adjusted through real-time data fusion and recurrent neural network prediction, temperature difference balance and abnormal temperature rise detection are realized, the safety of the battery is improved, and the service life of the battery is prolonged.

The invention provides a digital twinborn-based park pipe network monitoring and early warning method and system, and relates to the technical field of pipe network monitoring and early warning, and the method comprises the steps: constructing a park pipe network domain knowledge graph, collecting real-time monitoring data and historical operation and maintenance data, and forming a knowledge enhanced pipe network data set; extracting pipe network performance attenuation characteristics by using a bidirectional gating recurrent neural network, and deeply fusing the pipe network performance attenuation characteristics with expert empirical rules in the domain knowledge graph to construct a performance attenuation analysis model; a performance degradation curve and remaining useful life are calculated based on the model, potential fault risks are predicted, and maintenance suggestions are generated. According to the invention, precise evaluation of pipe network performance degradation, early warning of fault risks and intelligent generation of maintenance suggestions are realized, and the intelligent level and reliability of operation and maintenance of the park pipe network are improved.

The invention relates to the technical field of monitoring and early warning of power equipment, in particular to a monitoring and early warning method and system for substation power equipment. Comprising a data acquisition module, an intelligent diagnosis module, a risk assessment module, a collaborative early warning module and an interaction control module. The data acquisition module acquires equipment operation data including current, voltage, temperature and humidity and vibration signals. And the intelligent diagnosis module identifies equipment abnormity through the generative adversarial network and the graph neural network. The risk assessment module generates a risk assessment curve based on a fractional order differential model and a reinforcement learning algorithm. And the collaborative early warning module generates a graded early warning scheme by utilizing edge calculation. And the interaction control module displays a state and optimizes a control strategy through a human-computer interface. The intelligent monitoring and management level of the power equipment of the transformer substation is improved, the service life of the equipment is prolonged, and the operation and maintenance cost is reduced.

The invention discloses a power distribution network line fault positioning detection system, and relates to the technical field of power distribution network fault detection. The system comprises a mixed information acquisition layer, a fault feature extraction layer, an intelligent diagnosis layer and a fault positioning layer. The mixed signal acquisition layer comprises a high-frequency transient wave recording unit, a power frequency measurement unit, a wireless pulse sensor and a distributed optical fiber temperature measurement unit; the fault feature extraction layer comprises a time-frequency analysis module, a preprocessing module and a three-dimensional feature vector module; the intelligent diagnosis layer comprises a convolutional attention network, a space-time diagram neural network and a transfer learning module; the fault positioning layer comprises a particle swarm module and a fuzzy reasoning module. According to the invention, data information of the cable is acquired through the mixed information acquisition layer, a video analysis window function is dynamically matched with signal characteristics, a time domain graph scale, a frequency domain resonance component and a space field intensity gradient are constructed, fault diagnosis and positioning are carried out by using the intelligent diagnosis layer, and the fault positioning detection efficiency of the power distribution network is improved.

The invention relates to the technical field of medicine quality management, in particular to a medicine quality management method and system based on big data and artificial intelligence, and the method comprises the steps: obtaining the quality influence parameters of a medicine product in the production, transportation and storage processes through a multi-modal data collection device; noise is removed through data cleaning, data dimension reduction is carried out through principal component analysis and an auto-encoder, the analysis efficiency is improved, and the efficiency problem of mass data processing of a traditional method is solved. Time sequence analysis is carried out through the long-short-term memory network, abnormal nodes are recognized in time, manual sampling inspection is replaced, and accurate early warning is achieved. The graph neural network constructs a path tracking model, analyzes the relevance between nodes, traces the quality problem, and reckons the batch of affected drugs. Based on a traceability result, an XGBoost model generates quality risk scores of all links by analyzing historical and real-time data and sets a threshold value, and quality risks are early warned in real time.

The invention relates to the technical field of data processing, in particular to an intelligent analysis system for electric energy quality and read data, which comprises a data fusion module, a modeling module, an analysis module, a detection module, an optimization module and a control module. The data fusion module aligns data of an electric meter terminal, power grid monitoring equipment and an environment sensor through a sliding time window, and the analysis module calculates a power grid node loss transfer coefficient based on a graph neural network and fuses transformer no-load loss and line contact resistance parameters to generate a dynamic line loss evaluation matrix. And the detection module identifies the power consumption characteristic deviation degree through a random forest classifier, and generates a priority management strategy in combination with a multi-dimensional abnormal scoring model. The control module adaptively selects a communication protocol to execute a regulation and control instruction according to a network state, a heartbeat detection mechanism feeds back operation data of a governance device in real time, model parameters are driven to be iteratively updated, and dynamic cooperation of power supply quality optimization and line loss governance is achieved.

The invention discloses a close planting farmland growth vigor assessment method and system based on image processing, and relates to the field of agricultural information, and the method comprises the following steps: S1, multi-source data collection and preprocessing; s2, improving image segmentation, and extracting crop features; and S3, multi-dimensional growth vigor evaluation. According to the method, the field block level, the plant level and the whole growth period are covered through multi-source data collection, a generative adversarial network is used for repairing and shielding the plant image and restoring complete form information, the segmentation problem in a close planting scene is solved, the accuracy of close planting crop image analysis is improved, accurate registration of multi-modal data is achieved by means of feature point matching, and the accuracy of close planting crop image analysis is improved. The graph neural network optimizes image segmentation, effectively distinguishes overlapped leaves and stalks, deeply fuses multi-modal features and dynamically selects a fusion strategy, improves feature distinguishability, constructs a dynamic adaptive evaluation model, improves generalization ability and evaluation precision, identifies and intervenes abnormities in real time, and improves crop anti-risk ability and yield prediction accuracy.

The invention relates to the field of shale oil and gas, and discloses a method for identifying a shale oil and gas reservoir dessert, which comprises the following steps: acquiring a core CT image, three-dimensional seismic data and production dynamic data, and carrying out cross-scale preprocessing; constructing a fractional-order non-local seepage field model to represent nano-to-kilometer-level flow characteristics; predicting seepage field parameters through a Lie group symmetry constrained neural network; establishing a cross-scale coupling model of the quantum adsorption effect and the macroscopic seepage law; dynamically updating model parameters based on real-time monitoring data; and executing multi-target collaborative optimization to generate a sweet spot three-dimensional distribution and development scheme. According to the method, a fractal dimension dynamic constraint cross-scale data fusion technology is adopted, the effect of accurate mapping of nanopore and macroscopic fracture network parameters is achieved, and the problem of misalignment of CT scanning and seismic inversion data space registration is solved through pore communication fractal analysis.

The invention relates to the technical field of power distribution cabinet temperature control, in particular to an intelligent temperature and humidity control system for a power distribution cabinet. Comprising an environmental parameter acquisition module, a data analysis processing module, a temperature and humidity regulation and control module, an energy consumption recovery optimization module and a monitoring alarm module. The environmental parameter acquisition module monitors environmental data such as temperature, humidity and voltage through a sensor, and the data analysis processing module analyzes a trend based on a time convolutional neural network. The temperature and humidity regulation and control module achieves optimal control through an optimization algorithm, and the energy consumption recovery optimization module recovers and optimizes energy through a genetic algorithm. And the monitoring alarm module adopts a Bayesian network to be combined with a self-adaptive sliding window mechanism to monitor environmental fluctuation in real time, and gives an alarm when the environmental fluctuation is abnormal. Efficient operation, safety and stability of the power distribution cabinet in a complex environment are ensured, the safety and reliability of the power distribution cabinet are improved, the operation and maintenance cost can be effectively reduced, and energy conservation and environmental protection are achieved.

The invention relates to the technical field of light source controllers, and discloses a simulation light source controller parameter adaptive adjustment method and device, and the method comprises the steps: collecting an input voltage signal, an output current signal, an environment temperature signal and a channel power signal of a simulation light source controller, and inputting a neural network model for parameter mapping, obtaining a light source parameter dynamic response matrix; carrying out feature dimension reduction, and constructing a target optimization model; grid division is carried out on the parameter space, a steepest descent direction vector and a vertical search direction vector are respectively generated on divided grid points, and a parameter optimization direction set is obtained; iteratively updating each control parameter to obtain a parameter iteration sequence set; performing a voltage stabilization output test to obtain a plurality of candidate control parameter combinations; and the parallel verification test is carried out, and the optimal control parameter combination is determined, so that the inter-channel crosstalk of the simulation light source controller is effectively inhibited, and the problem of mutual interference when multiple channels work in parallel is solved.

The invention discloses a brain disease classification method and system. Precise diagnosis is realized through multi-modal data fusion and dynamic modeling. The method comprises the following steps: collecting multi-modal brain image information and cognitive behavior information of a user; performing dynamic function connection analysis on the resting state functional magnetic resonance time sequence signal to obtain a time-varying brain network feature matrix, and performing white matter fiber bundle topology reconstruction on a structure connection matrix; constructing a four-dimensional correlation tensor by using the time-varying network features, the structural connection weights and the anatomical features through a neurodynamic model; performing multi-task learning on the four-dimensional correlation tensor based on a time-varying graph neural network model, and outputting a quantitative diagnosis result; and finally generating a clinical classification report integrating the individualized brain network remodeling target, the disease progress risk layering and the treatment response prediction. By dynamically fusing the structure and functional features, comprehensive characterization of the pathological mechanism of the brain disease is realized, and decision support with both accuracy and interpretation is provided for clinical diagnosis.

The invention relates to the technical field of data storage and processing, in particular to an RAID card static cache management method and device based on data popularity, and the method comprises the steps that the physical position of a data block needing to be accessed in an SSD array is acquired according to a read-write request; historical data access information of the RAID card is collected, a historical data set is generated, and a neural network model is trained by using the historical data set to obtain a cache management model; predicting and outputting data blocks which are possibly accessed in the future and the access probability of the data blocks through the cache management model; the RAID controller dynamically adjusts a cache strategy in combination with an LRU strategy according to a prediction result output by the cache management model and a read-write request of a file system, and optimizes a storage position and an updating mechanism of a data block in a cache space; and when the hit rate or the data access efficiency does not reach the preset value, adjusting the parameters of the cache strategy and updating the cache strategy. According to the method, cache resources can be more effectively distributed, the cache hit rate is improved, the cache replacement overhead is reduced, and therefore the performance of a storage system is optimized.

The invention relates to the technical field of town sewage pipe network detection, and discloses a town sewage pipe network leakage detection system and method. The system comprises a data acquisition module which uses a multi-source sensor to acquire real-time operation data such as pipe network pressure, flow and the like; the feature extraction module extracts spatio-temporal features based on the multi-scale convolutional neural network, and generates a pipe network state feature matrix; the anomaly detection module inputs the feature matrix into a pre-training model and marks a potential leakage area; the optimization analysis module constructs a multi-constraint dynamic optimization model, and pipe network pressure parameters are optimized by using an adaptive particle swarm algorithm; the hierarchical execution module generates a global regulation and control sequence, dynamically matches local pressure parameters and adjusts the valve opening and the pump station power through a decision layer, a region coordination layer and an execution layer. The system and the method are accurate in detection and reasonable in regulation and control optimization, leakage risks can be effectively reduced, the operation management level of a pipe network is improved, and water resource waste and environmental pollution are reduced.

The invention relates to the technical field of intelligent water conservancy, and discloses a cross-regional water transfer project intelligent scheduling method and system, and the method comprises the steps: building a digital twin system based on a geographic information system, hydrological monitoring data and a spatial topological structure, and integrating a meteorological evolution prediction model, a basin hydrological response model and a water demand prediction model; predicting a water demand and an adjustable water amount by using a space-time convolutional neural network and a gating circulation unit; establishing a multi-objective optimization model taking water supply benefit, ecological influence and energy consumption cost as optimization objectives; an optimal water transfer scheme is generated through a Markov decision process and multi-agent cooperation; and carrying out robustness evaluation on the scheme and generating an emergency scheduling plan. According to the method, the scheduling efficiency and adaptability of the water transfer project are remarkably improved, and efficient configuration of water resources and quick response under extreme situations are achieved.

A 100-meter gridded spatialization method for carbon emissions of different land use types based on multi-source heterogeneous data is provided, including the following steps: S1, fitting Luojia-1A Satellite nighttime light data year by year with defense meteorological satellite program-operational linescan system (DMSP-OLS) and national polar-orbiting partnership-visible infrared imaging radiometer (NPP-VIIRS) fused 1-kilometer gridded nighttime light data, performing additive fusion on previous light data and current light data, introducing a time inertia weight factor to improve particle swarm optimization-back propagation (BP) neural network algorithm, and forming 100-meter nighttime light data correctable on a long time series; and S2, simulating a complex nonlinear dynamic changing relationship between multilevel 100-meter gridded data of different land use types in different industries and energy carbon emissions in different industries based on the 100-meter nighttime light data, and establishing a 100-meter spatialization inversion model for energy carbon emissions of different land use types in different industries.

The invention provides a multi-kitchen-appliance remote control method and system based on context awareness, and relates to the technical field of smart home, and the method comprises the steps: collecting kitchen environment data and user historical use data, and carrying out the wavelet multi-resolution decomposition and density gradient matrix recognition of abnormal data; the method comprises the following steps: extracting data time sequence features by combining a bidirectional recurrent neural network and performing hierarchical coding, inputting a fused feature vector and a density distribution graph into an improved long-short-term memory network, performing feature propagation by combining a Fisher information matrix and a time-varying association graph, and predicting a kitchen appliance use trend by utilizing a prophet model. And based on a risk assessment model and a Monte Carlo tree search method, in combination with an adaptive exploration mechanism and a multi-target evaluation system, generating and assessing a candidate control strategy, and outputting an optimal kitchen electricity cooperative control strategy.

The invention discloses a power efficiency optimization method based on intelligent load management. The method comprises the following steps: S1, generating an initial load characteristic matrix; s2, decomposing the initial load characteristic matrix into a short-term behavior characteristic matrix and a long-term trend characteristic matrix through pseudo-symmetry decoupling; s3, multi-level load demand prediction is carried out through a fusion structure of the multi-scale convolution and the recurrent neural network; s4, coding a cognitive gene sequence, triggering gene mutation and recombination operation when significant change occurs, and generating a load distribution strategy; s5, a load distribution strategy is transmitted into a causal reasoning and self-supervision feedback control module, and if the deviation between feedback data and a prediction result exceeds a threshold value, a self-learning mechanism is triggered; s6, dynamically distributing the load priority, and generating a decision path of load management; and S7, performing adaptive optimization through a multi-layer feature feedback closed-loop optimization system. The intelligent load management method is adopted, and the power efficiency is optimized through multi-layer feature decoupling and closed loop.

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

The invention relates to the technical field of skin disease health evaluation, in particular to a skin disease health evaluation system integrating multiple clinical evaluation tools. Comprising a patient information management and dynamic risk assessment module, a skin microbiome analysis module, a symptom quantification and assessment module, a physiological index monitoring module, an environmental adaptability monitoring module, an intelligent decision support module and a feedback and optimization module. The method comprises the steps of performing dynamic risk assessment in combination with a machine learning algorithm, performing quantitative assessment on the skin health state of a patient in combination with microbiome analysis and real-time physiological index monitoring, performing quantitative assessment on symptoms through an image analysis technology and a standardized questionnaire, and generating a personalized health assessment report and a treatment scheme by using a deep neural network and an optimization algorithm. Continuously improved closed-loop management is realized through a feedback optimization module; according to the invention, not only is a scientific evaluation basis provided for doctors, but also a patient can obtain customized treatment suggestions.

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

The invention provides a self-adaptive polarization modulation-based inter-satellite laser communication and signal scheduling method, which relates to the field of laser communication, and comprises the following steps of: acquiring polarization state information of emitted and received light beams, calculating a mismatch degree, generating a compensation parameter, constructing a polarization state prediction model by using a deep learning neural network for pre-correction, and calculating the mismatch degree; a polarization modulator is adopted to perform real-time modulation and establish a link quality evaluation model, carrier synchronization and channel compensation are realized based on an adaptive optical carrier recovery technology of coherent detection, a link state is monitored in real time, and a training data set is updated.

The invention relates to the technical field of lithium battery management, in particular to a lithium battery pack dynamic balancing method, device and equipment, a storage medium and a computer program product. The method comprises the following steps: predicting battery cell state parameters of each battery cell based on a preset long-short-term memory neural network model to obtain a capacity attenuation trend of each battery cell; building a health state evaluation index based on the capacity attenuation trend; based on the health state evaluation indexes, performing health grade classification on each battery cell by adopting a preset clustering algorithm; obtaining the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell, and determining the balance priority of each battery cell based on the health grade category division result, the temperature gradient, the state of charge deviation and the charge-discharge rate of each battery cell; and on the basis of the equalization priority, a target dynamic equalization adjustment strategy is generated, and each battery cell is adjusted according to the target dynamic equalization adjustment strategy, so that the energy scheduling accuracy of the lithium battery pack is improved.

The invention discloses a TSV packaging defect detection method and system, and the method comprises the steps: carrying out the multi-dimensional excitation scanning of a TSV packaging structure through employing an eddy current pulse thermal imaging device, and generating a three-dimensional eddy current field distribution feature matrix; inputting the three-dimensional eddy current field distribution characteristic matrix into a topological manifold decomposition module for defect characteristic separation, and constructing three characteristic components of a structure deformation topological ring, an interface fracture chain and a thermal stress abnormal curved surface based on a continuous coherence analysis method; performing cross-domain fusion processing of a dynamic heterogeneous neural network on the three feature components, and generating a defect topology fingerprint spectrum through a double-path attention gating mechanism; and inputting the defect topology fingerprint into a multi-scale entropy evaluation module for defect evolution simulation, and outputting a quantitative detection report containing defect geometric parameters, failure probability and reliability threshold. According to the embodiment of the invention, a comprehensive defect detection system can be constructed, and the accuracy and efficiency of defect identification are improved.

The invention discloses an optical transmission network hidden danger detection method and device and a computer program product, which can realize dynamic adjustment of differentiated bandwidth requirements by acquiring optical network state information in real time, evaluating service priority and establishing an optical channel resource allocation model. And when a topology anomaly is detected, constructing a service quality evaluation model by using a long-short-term memory neural network, and quantitatively analyzing the influence of the anomaly on the service. When a fault occurs, distributed link fault detection and a fault diagnosis engine based on a decision tree are adopted, and fault hidden dangers are quickly positioned and classified. And then, through an elastic recovery mechanism oriented to service sensitivity, the service quality level of a standby optical path is adjusted, and an optical layer topology connection relationship is optimized. According to the method, the resource utilization efficiency, the fault recovery capability and the service quality of the optical network are remarkably improved, and an innovative solution is provided for intelligent optical network management.

The invention relates to a corn disease intelligent grading evaluation and renovation recommendation method fusing multi-modal information, and relates to a corn diversity disease identification and multi-stage evaluation model based on position coding. The method comprises the following steps: introducing a position attention mechanism, and carrying out multi-layer stacking by taking an inverse movement residual module integrated with position coding as an element movement block; the capabilities of complex image feature extraction and spatial information analysis are enhanced, and a scab region is segmented, so that disease type distinguishing and severity evaluation are realized; constructing a model based on a corn disease entity relationship triple library: performing deep context coding on a corn disease text, realizing classification and prediction of complex relationships among disease entities through a multi-layer structure, and analyzing complex links and influence factors among diseases; and a corn disease treatment method recommendation model combined with an implicit relationship: according to a disease identification and grading result and a disease triple, mining an optimal implicit relationship among different disease entities through complex number embedding and a graph neural network, and giving an accurate disease treatment method recommendation.

The invention relates to the technical field of intelligent traffic management, and discloses a road traffic signal lamp management system based on big data, which comprises a data acquisition and fusion module for acquiring traffic sensor data, visual data, GPS track data and external environment data, generating unified traffic state data through space-time alignment, data filtering and multi-modal data fusion; and the flow prediction module is used for predicting the traffic flow of a single intersection and a region by using a time sequence model and a graph neural network based on the traffic state data generated by the data acquisition and fusion module. Through multi-modal data acquisition and space-time alignment, Kalman filtering, Bayesian inference and other technologies, deep fusion of different data sources is realized, the comprehensiveness and accuracy of data are significantly improved, dynamic changes of a complex traffic scene can be truly reflected, high-quality basic data are provided for subsequent flow prediction and optimization control, and a core technical support is formed.

The invention relates to the technical field of new energy power generation prediction, in particular to a new energy power prediction method fusing typhoon meteorological information and a micrometeorological prediction result, which comprises the following steps: collecting multi-source observations such as a satellite scatterometer, a radar wind profile and laser wind measurement, and unifying coordinates; constructing a mesoscale wind field by adopting four-dimensional variational assimilation, inferring a micrometeorological field by utilizing a spectrum embedding diffusion network, and generating a multi-scale meteorological field by frequency domain phase consistency fusion; probabilistic wind speed is sampled in the countercurrent model meeting the condition of mass and momentum conservation, an energy conservation graph neural network is input, the wake effect is coupled, and a unit power quantile value is obtained; the power probability is sent to a risk weighting model, the weight is adjusted in real time according to the peak load, the reserve capacity and the climbing rate risk, a dispatching power curve and uncertainty are output, and grid-connected power errors are used for periodically updating the weight and the parameters of the last layer of the reversible model. According to the invention, the safety acceptance margin of the power grid to new energy is obviously improved.

The invention discloses an energy storage frequency modulation instruction prediction method and system based on a signal sequence folding rate. An original frequency modulation instruction signal sequence is obtained; obtaining optimization parameters for each signal in the original frequency modulation instruction signal sequence, wherein the optimization parameters comprise a first folding rate Z1, a first average value Q1 and a first variance C1; generating an auxiliary signal sequence based on the optimization parameters, and obtaining a combined signal sequence; predicting by using a neural network based on the auxiliary signal sequence and the combined signal sequence to obtain an intermediate prediction result; performing decoupling operation on the intermediate prediction result to obtain a final prediction result; the prediction result of the frequency modulation instruction is more accurate, the size of the frequency modulation instruction is predicted in advance for energy storage regulation, and the response accuracy and the frequency modulation income are improved.

The invention relates to the technical field of virtual power plant operation monitoring, and discloses a virtual power plant distributed resource cluster anomaly monitoring system, method and equipment, and the system comprises a resource node data collection module, an anomaly trend score calculation module, an association graph model construction module, a single node influence calculation module and a comprehensive anomaly early warning output module. In the prior art, a single-point alarm mode depends on a fixed threshold value or a rule base, and especially under the conditions that distributed resources in a virtual power plant are complex in type and space coupling and controller sharing exist between nodes, accurate identification and dynamic early warning of trend anomalies, cooperative faults and potential propagation paths are difficult to realize. According to the method and the device, the multi-dimensional parameter modeling and the graph neural network anomaly propagation analysis are fused, so that the anomaly in the distributed resource cluster is identified and dynamically evaluated, and the early warning accuracy and the intelligent operation and maintenance efficiency of the virtual power plant are improved.