109 results about "Long-term prediction" patented technology

A multi-energy load forecasting method, a multi-energy load forecasting system, an electronic device, a program, and a storage medium are provided that realize accurate long-term forecasting of multi-energy loads in a target integrated energy system under conditions where no historical load data is available. [Solution] A multi-energy load forecasting method for an integrated energy system without historical data involves obtaining the meteorological characteristics of a target complex and the cooling, heating, electricity, and gas historical data of a source domain group complex, preprocessing the obtained data, performing cross-correlation and generalization ability analysis of the complex on the preprocessed cooling, heating, electricity, and gas historical data of the source domain group complex, determining appropriate source domain data, constructing a multi-energy load forecasting model, training the model based on the source domain data according to the Metas training policy, obtaining a trained forecasting model, and inputting the preprocessed meteorological characteristics of the target complex into the forecasting model to obtain a forecast result.

The invention discloses a digital twin-driven bridge full life cycle damage prediction and evaluation method and system, and belongs to the field of bridge structure health monitoring, and the method comprises the steps: obtaining a monitoring data set of a bridge structure; an initial digital twinborn model embedded with a micro physical layer is constructed and trained, the micro physical layer constructs a damage evolution model applied with monotonic physical constraint based on multi-source monitoring data, and a damage evolution trajectory of the bridge in a future time period is predicted through the damage evolution model based on the physical parameter vector; in combination with an uncertainty quantification method, generating a time-varying reliability index of the bridge in a future time period; and based on the time-varying reliability index, constructing and solving a maintenance decision optimization model to generate a maintenance decision of the bridge. According to the invention, the physical authenticity and reliability of the long-term prediction result are ensured.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention belongs to the technical field of new energy electric power, and discloses a new energy bearing intelligent evaluation and regulation system and method, the system is composed of a data acquisition module, an intelligent evaluation module, a prediction module, a regulation strategy module and an execution and feedback module, and the data acquisition module acquires meteorological, historical operation, load and power grid state data in real time; the time sequence of the prediction module and a deep learning algorithm are combined, short-term to medium-and-long-term prediction is carried out on new energy output and load change, output change is captured in real time, regulation and control lag is avoided, power grid risks are quantified through a multi-model fusion algorithm, and node accessible capacity and a risk assessment result are output; and the regulation and control strategy module generates a regulation and control instruction by utilizing optimization algorithms such as a genetic algorithm based on the evaluation result and the prediction data, and realizes closed-loop control through the execution and feedback module, so that the evaluation data directly guides regulation and control actions.

The invention discloses an intelligent power grid load prediction and dynamic response coordinated scheduling method, and relates to the technical field of power system automation, and the method comprises the steps: accessing intelligent ammeters, distributed power controllers and other devices of Modbus, IEC61850 and DL / T645 protocols through a multi-protocol adaptive gateway, and achieving data standardization; time stamps are calibrated by means of Beidou time service and an IEEE1588PTP protocol, and it is ensured that multi-source data synchronization errors are controllable; deploying an edge computing node cluster, distributing high-priority tasks to low-load nodes through an edge coordinator in combination with a load fluctuation level and a greedy algorithm, and ensuring real-time processing efficiency; the edge nodes generate short-term load prediction, and the cloud platform outputs medium and long-term prediction based on a historical data training model; and finally, the coordinated scheduling decision module fuses the two types of prediction results and the real-time parameters of the power grid, and generates a dynamic instruction to control the output of the adjustable load and the distributed power supply.

The invention provides an electric quantity prediction method and system fusing physical constraint factors, and relates to the technical field of electric quantity prediction. Historical load, weather, electricity price and calendar data are collected, and a key feature set is constructed through preprocessing and feature selection; a prediction model with the physical information neural network as the core is constructed, the prediction model comprises a recursion sub-module used for short-term prediction and a trend sub-module used for long-term prediction, and a physical constraint loss item based on a physical rule is introduced into model training so as to enhance the generalization ability; a multi-time granularity modeling framework is adopted, uncertainty quantization is achieved through a Monte Carlo Dropout or Bayesian neural network, and a confidence interval of a predicted value is output; and finally, causal reasoning is carried out through a Shapley value algorithm and anti-fact simulation, and key influence factors are identified. According to the method, the precision, stability and interpretability of electric quantity prediction are effectively improved, and reliable support is provided for power grid dispatching and decision making.

The invention discloses an intelligent analysis system for the running state of a 3C vehicle-mounted contact network, and belongs to the technical field of crossing of intelligent operation and maintenance of rail transit and industrial artificial intelligence. And the system associates the multi-source heterogeneous observation data to a specific equipment unit through an equipment centralized data binding module. And the multi-modal feature extraction and state management unit processes the data and maintains a multi-dimensional state vector of the equipment by using the Kalman filtering updating unit. And the physical-data hybrid decision maker fuses the data driving rule and the simplified physical model to output a diagnosis result. The topology analyzer performs global verification based on a mechanical transfer rule. The system further comprises a long-term health state prediction and feedback module, early failure risks are predicted through a hidden Markov model, Kalman filtering process noise is dynamically fed back and adjusted, and cooperation of long-term prediction and short-term estimation is achieved. According to the method, the technical problems of multi-source data splitting, lack of physical basis in diagnosis and incapability of predictive maintenance are solved.

The invention discloses a global fracture conductivity ultra-long-term prediction method and system, belongs to the technical field of crossing of oil and gas reservoir engineering and artificial intelligence, and is used for solving the technical problem that the calculation cost is high when an existing physical model is used for predicting fracture ultra-long-term conductivity. The method comprises the following steps: generating a global fracture conductivity evolution data set covering multiple working conditions through a numerical simulation system; constructing a multi-channel input tensor containing dynamic fracture conductivity and static proppant concentration distribution; using the data set to train a plurality of Fourier neural operator models for different prediction time scales; and based on a hierarchical time sequence aggregation strategy, iteratively calling trained models of different time scales, and realizing rapid prediction of fracture conductivity in a few years to tens of years in the future. According to the method, on the premise of ensuring the prediction precision, the calculation time consumption of ultra-long-term prediction can be remarkably reduced, and decision support is provided for crack design and production strategy optimization.

The invention discloses a surface temperature medium and long term forecasting method based on a multi-scale TransFuse pyramid network. The surface temperature medium and long term forecasting method comprises the steps of S1, acquiring original LST time sequence data and performing preprocessing; s2, constructing a three-dimensional time delay embedded feature tensor for the data preprocessed in the S1 by adopting a stepped cross-node tensor feature reconstruction method; s3, on the basis of the three-dimensional time delay embedded feature tensor constructed in the S2, meteorological features of different time scales are extracted in parallel through a dynamic multi-resolution convolution kernel group; s4, performing five-stage cascade operation on the meteorological features of different time scales extracted in the S3 to realize deep fusion of cross-scale features, and performing high-level semantic information integration through a global feature pyramid; and S5, finally outputting a multi-step prediction result through a regression prediction framework based on the fusion features obtained in the S4. According to the invention, an extensible technical normal form is effectively provided for high-precision meteorological prediction in a limited computing power scene, and important application potential is shown in actual business deployment.

The invention discloses a long time sequence prediction method based on time-varying period coding and hierarchical channel fusion. Comprising the following steps: decomposing an input multivariate time sequence into a trend component and a season component; performing mapping processing on the trend component to obtain trend output; time-varying periodic coding processing and hierarchical channel fusion processing are carried out on the seasonal components to obtain seasonal output, the time-varying periodic coding processing is used for adaptively capturing time-varying periodic features in the seasonal components, and the hierarchical channel fusion processing is used for dynamically capturing correlation differences among different channels in the seasonal components; and generating a prediction result of the future time step based on the trend output and the seasonal output. According to the method, the time-varying periodic characteristics can be modeled in a self-adaptive manner, the strength correlation between the channels can be dynamically captured, and the method has excellent long-term prediction performance and generalization.

The invention discloses a multi-source time sequence decomposition and fusion hydraulic structure behavior prediction method, which comprises the steps of 1, acquiring historical time sequence monitoring data of a hydraulic structure, including behavior parameters and environmental quantity parameters, the behavior parameters including dam body settlement and seepage flow, and the environmental quantity parameters including dam body water level and rainfall; 2, preprocessing the acquired monitoring data, and constructing a hydraulic structure behavior model input data set; 3, determining an optimal decomposition period of an input data set of the hydraulic structure performance model; 4, according to the determined optimal decomposition period, decomposing the preprocessed hydraulic structure performance model input data set into three component parameters according to an original time sequence, wherein the three component parameters are respectively a trend component parameter, a periodic component parameter and a residual component parameter; and 5, superposing the predicted values of the three component parameters according to time points to obtain a final performance predicted value of the hydraulic structure. According to the method, the prediction precision is remarkably improved, and the stability and reliability of long-term prediction are remarkably improved.

The invention provides a multi-energy-flow real-time updating method based on a building integrated energy system, and belongs to the technical field of energy scheduling, and the method comprises the steps: obtaining an equipment set and a typical state vector, and constructing a building integrated energy mechanism model based on mechanism data and balance constraint data; collecting a plurality of historical operation data, and determining a plurality of period-mode historical data and a plurality of environment clustering data; determining a plurality of response clustering data, and constructing a data driving model of each environment label of each period-mode label; acquiring equipment parameter data, and determining fitting residual data of each environment label of each period-mode label; performing first optimization on each data driving model; and constructing a mechanism-data hybrid drive model and realizing adaptive optimization. The method can accurately adapt to a subdivided scene, improve deviation fitting precision, give consideration to explanatory and prediction precision, dynamically adapt to system time-varying characteristics, improve the adaptability of the model in a complex scene, and guarantee long-term prediction reliability.

The invention relates to the field of life prediction models and data analysis, and discloses a submarine cable operation life prediction method and device based on dynamic temperature characteristics and a product. The method comprises the following steps: when a target offshore wind field is a to-be-built wind field, acquiring a long-term prediction sequence of the output power of the target offshore wind field, and analyzing the temperature characteristics of a to-be-predicted submarine cable in combination with a submarine cable key parameter set; evaluating the aging speed of the to-be-predicted submarine cable by using the submarine cable dynamic aging model, and determining a first long-term attenuation rate sequence of the performance index of the to-be-predicted submarine cable; and based on a preset life end point, according to the first long-term attenuation rate sequence and the plurality of initial performance index values of the to-be-predicted submarine cable, predicting the operation life of the to-be-predicted submarine cable, and obtaining an operation life prediction value of the to-be-predicted submarine cable. According to the method, the dynamic temperature characteristics of the submarine cable are considered, and the precision of a submarine cable service life prediction technology is improved.

In order to solve the key problems of difficulty in multi-source information fusion, insufficient spatial topology modeling, attenuation of long-term prediction precision and the like in an existing aircraft ground taxiing trajectory prediction method, a parallel processing architecture of a historical trajectory encoder and a pavement path encoder is designed, and trajectory time sequence features are extracted by using a long-short-term memory network; modeling a spatial topological relation of a control path by adopting a graph attention network, and realizing effective integration of heterogeneous information through a feature fusion layer; a multi-component loss function fusing the position, the speed and the acceleration is provided, and the continuity and the smoothness of a prediction track are constrained; a lightweight data enhancement strategy and an adaptive residual connection mechanism are designed, the model generalization ability is improved, and long-term prediction error accumulation is relieved. According to the method, multi-source trajectory information can be effectively fused, and the accuracy and stability of aircraft ground taxiing trajectory prediction are remarkably improved.

The invention relates to the technical field of power equipment state monitoring, and discloses a switch cabinet partial discharge detection method, which comprises the following steps: synchronously collecting multi-source heterogeneous signals generated by partial discharge through a multi-sensor array, and carrying out preprocessing and feature extraction to obtain a multi-modal feature vector; inputting the multi-modal feature vector into a recognition and evaluation model for automatic recognition of discharge types, evaluation of discharge severity and sensing of abnormal discharge; real-time analysis and fusion decision making are carried out on an abnormal sensing result, a recognition result and an evaluation result through a collaborative architecture of edge computing nodes and a cloud platform; wherein the edge computing node utilizes a lightweight real-time sensing module to execute real-time abnormal discharge sensing and triggers local early warning when sensing abnormity, and the cloud platform utilizes a depth evaluation module to execute comprehensive state evaluation, recognition and long-term prediction. According to the invention, the limitation of a single detection method is overcome through multi-sensor synchronous acquisition and in combination with a D-S evidence theory information fusion method.

This invention belongs to the fields of new energy technology and artificial intelligence technology, and provides a high-resolution long-term photovoltaic power prediction method and system based on a dual-branch architecture. By constructing a parallel dual-branch architecture, the baseline branch extracts multi-scale features, and the ramp branch identifies power ramping events. The features from both branches are embedded through multiple channels and then fed into a BiGRU-Enhanced Transformer model with shared weights. By strategically deploying BiGRU layers at the input and between the encoder and decoder, local instantaneous dependencies and global long-term trends are captured simultaneously. A multi-task joint optimization loss function is designed for the ramp branch, using weighted cross-entropy and... L The probability, temporal location, and fluctuation amplitude of slope occurrence are jointly optimized using the L1 norm. Finally, nonlinear residual correction is applied to the outputs of the two branches to achieve the final prediction. This invention effectively overcomes the oversmoothing effect of deep learning models, demonstrating extremely high capture accuracy and robustness in long-term prediction of one month's worth of data at 1-minute resolution.

The invention discloses a virtual power plant collaborative optimization scheduling method and system based on multi-time scale prediction, and relates to the technical field of virtual power plant collaborative optimization scheduling, and the method comprises the steps: obtaining multi-dimensional operation data of a virtual power plant scheduling object; the scheduling object comprises a distributed renewable energy system, an energy storage system and a controllable load system; performing medium and long term and short term multi-time scale prediction according to the acquired data; performing preliminary scheduling planning on the basis of medium and long term prediction results, and generating a short-term optimization scheduling plan in a rolling manner by taking the preliminary planning result as a constraint and utilizing a short-term rolling optimization model introducing a reinforcement learning algorithm to continuously adjust the weight of an objective function of the optimization model according to the short-term prediction data; and taking the short-term optimization scheduling plan as a reference, performing calculation by using a real-time control model based on measured data, and issuing a real-time control instruction to each scheduling object. According to the invention, adaptive optimization scheduling of the virtual power plant can be realized, and comprehensive benefits are guaranteed.

The invention relates to the technical field of industrial big data analysis and energy management, and provides a factory load dynamic prediction method and system, and the method comprises the following steps: collecting the operation state data of key equipment in a factory in real time, predicting the remaining service life of the equipment, and calculating the health index of a current time point; dynamically correcting the reference load predicted value of the equipment to obtain a real load predicted value; knowledge migration is carried out on the target plant area to construct a load prediction model of the target plant area; performing real-time compensation on a load prediction result based on random event simulation; and when an event is detected, triggering a Gaussian process regression model to calculate a load deviation delta P, and dynamically adjusting an event sensitivity coefficient by utilizing reinforcement learning. According to the method, the accuracy problem of long-term prediction is solved through equipment recession modeling, the problem of data scarcity of a new plant area is solved through transfer learning, and the problem of short-term sudden disturbance is solved through random event simulation.

The present application relates to the technical field of marine data processing and spatio-temporal prediction, and specifically discloses a sea surface core variable prediction method and system based on a wavelet-enhanced graph neural network.The method comprises the following steps: obtaining multivariate graph sequence data of a target sea surface; performing multilevel wavelet decomposition and gated fusion on each variable through a variable-level multiscale wavelet gated fusion module, and outputting enhanced multiscale time series features; inputting the enhanced multiscale time series features into a KAN-LSTM encoder module, performing recursive updating through gated fusion of a conventional convolution and a KAN convolution branch, and outputting encoder spatio-temporal features; inputting the encoder spatio-temporal features into a signed adaptive spatial graph convolution module, learning a signed sparse adaptive adjacency matrix and performing multi-order diffusion aggregation, and outputting a prediction result.The present application realizes long-term prediction of a target sea surface with high precision and high stability.

The invention belongs to the technical field of electric power prediction, and particularly relates to an electric power load prediction system which obtains a load parameter sequence through an acquisition module and extracts correlation coupling characteristics representing influence degrees of seasons, meteorology and electricity price factors; using a first prediction module to obtain a short-term load prediction result based on the sliding time window and the short-term prediction sub-model, and using a second prediction module to obtain a long-term load prediction result based on the longer time window and the long-term prediction sub-model; a coupling loss function is innovatively constructed through an online coupling module for cooperative training, and deep correlation analysis of fluctuation mode types corresponding to temperature, electricity price and load fluctuation sequences is realized by adopting fluctuation mode clustering, multi-dimensional feature extraction and load mode coupling weight matrix technologies. The technical problem of mutual isolation of long-term and short-term prediction is effectively solved, and the accuracy and adaptive capacity of load prediction are remarkably improved.

The invention relates to the technical field of load prediction, and discloses a medium and long term power load prediction method and system based on ensemble learning. The method comprises the following steps: determining a plurality of power load prediction models as candidate base learners of integrated learning; based on Spearman correlation analysis, a power load prediction model meeting the integration requirement is screened out from the candidate base learners to serve as a base learner of integrated learning, and a pre-trained full-connection neural network serves as a meta learner of integrated learning; each base learner carries out prediction to obtain an initial load prediction result of the corresponding base learner corresponding to a future medium and long term prediction period; and performing fusion processing on each initial load prediction result through a meta-learner to obtain a target load prediction result of the target region corresponding to the future medium and long term prediction period. The prediction strategy based on Stacking ensemble learning is superior to a single model or a traditional integration scheme in prediction precision, stability and complex scene adaptability.

The invention relates to the technical field of ship trajectory prediction, in particular to a cross-scene ship trajectory prediction fusion method based on a Transform algorithm. The method comprises the steps that a multi-source data input layer integrates basic navigation, port, sea transportation, inland river exclusive and environment and interaction data; the data preprocessing and link filtering layer cleans data noise and screens high-probability OD pairs; the multi-dimensional feature engineering layer constructs 15-dimensional feature vectors including sea transportation, inland river and interaction features; the core model is based on a Transform encoder-decoder architecture, integrates multi-head attention, time-space diagram attention and multi-mode bias, and adopts double-branch output and mixed loss function optimization; and the result post-processing and application output layer adopts a differentiation strategy for short-term and long-term prediction, and supports multi-ship collision avoidance path generation and derivative application at the same time. According to the invention, multi-scene coverage of sea transportation and inland waterway is realized, short-term and long-term prediction precision is well improved, and the method can be applied to tasks such as maritime affair safety scheduling and inland river intelligent shipping.

The application discloses a traffic sequence prediction method based on multivariate time series data analysis, adopts a random graph diffusion attention mechanism to extract global and local spatial features of a traffic sequence, uses time attention to extract time features, improves prediction accuracy, reduces memory usage of a model, and improves the effect of the model on long-term prediction. The traffic sequence prediction method based on multivariate time series data analysis solves the problems of insufficient short-term prediction accuracy, high calculation complexity, large memory occupation, and insufficient lightweight of the existing model while maintaining the accuracy of long-term prediction.

The invention belongs to the technical field of atmospheric density prediction, and discloses a cross-source data fusion thermal layer atmospheric density hierarchical progressive full-scale prediction system and method, which utilize respective advantages of different gradient data sources to construct a reference density field-corrected density field-instantaneous refined density field three-level hierarchical fusion refinement architecture. Cross-source data calibration and physical constraint modeling are combined to realize month-year scale long-term prediction, day-week scale medium-short-term prediction and short-term prediction of the thermal layer atmospheric density. According to the method, through a three-level layered refinement architecture, the long-term coverage advantage of TLE data, the mesoscale variable rate description advantage of precise orbit data and the high-frequency and high-precision advantages of accelerometer data are fully mined, the defect of a single data source is avoided, full-scale and full-area density precise description is achieved, the thermal layer atmospheric density refinement precision and prediction suitability can be improved, and the method is suitable for large-scale and large-scale measurement. And data cost is reduced.

The application discloses a regional thermal power generation capacity medium and long term prediction method and system, the method comprises the following steps: based on the preset correlation analysis algorithm, the correlation analysis of regional power historical data and related factor historical data is carried out, and a preset number of key influence factors are determined; sample data is generated according to the regional power historical data and the historical data of the key influence factors, and the sample data is divided into a training set, a test set and a verification set; a prediction model based on space-time attention mechanism is constructed according to a space attention module, a time attention module and a prediction module; the prediction model based on space-time attention mechanism is trained based on the sample data, and the target prediction model is obtained after the training is completed; the thermal power generation capacity prediction value of the future preset time length is predicted based on the target prediction model, so that the medium and long term prediction of the regional thermal power generation capacity is realized reliably, and the operation risk of the thermal power enterprise is reduced.

The application relates to a nuclear power plant accident early warning method and system, and the method comprises the following steps: acquiring real-time operation data of a nuclear power plant; predicting operation data in a future first set time according to the real-time operation data to obtain short-term future data; performing accident diagnosis according to the short-term operation data to obtain an accident prediction type; generating operation data in a future second set time according to the accident prediction type and the real-time operation data to obtain long-term future data; and the second set time is greater than the first set time. The application can realize early prediction of an accident risk, guarantee the confidence of long-term prediction results, and enable staff to realize early intervention by using long-term future data.

The invention discloses a cellular network traffic prediction method based on a Mama model and a graph convolutional network, which belongs to the field of deep learning and cellular network traffic prediction, and comprises the following steps: (1) carrying out fast Fourier transform on original traffic data, converting a time domain into a frequency domain, screening a main frequency component and estimating a period; (2) constructing a graph structure by taking the preprocessed features as nodes through a periodic graph convolutional network, and extracting periodic features through aggregation of the graph convolutional network; (3) dividing regional homogeneous / heterogeneous neighbors by using a Pearson coefficient through a variable correlation graph convolutional network, and aggregating and extracting regional correlation features through the graph convolutional network; (4) a bidirectional Mama module performs time-dependent modeling on the flow data according to positive and negative time sequences, and fuses bidirectional features; and (5) the output layer outputs a prediction result through linear transformation. According to the method, flow periodicity, regional association and long-term dependence are considered, accurate long-term prediction is realized, and support is provided for network resource optimization.

This invention discloses a method for predicting the cooling load of public buildings based on spatiotemporal attention transfer learning, comprising the following steps: collecting multi-source heterogeneous data of the target building and constructing a multimodal spatiotemporal dataset; then filtering and classifying the multimodal spatiotemporal dataset to obtain core features of different building types; calculating the subjective and objective weights corresponding to each core feature, and combining the subjective weights to calculate the dynamic weight vector of each core feature; then weighting the dynamic weight vectors of each core feature, and outputting the temporal features affecting the cooling load value based on the temporal dependency of the cooling load; finally, optimizing the weights and enhancing the features of the temporal features, and predicting the enhanced temporal features through a fully connected output layer to output the predicted cooling load value. This invention can achieve universal adaptation for predicting the cooling load of buildings of various types and improve the prediction stability of building cooling load in long-term prediction.

This invention discloses a long-term prediction method for deep-sea creep based on in-situ pore pressure observation using Bayesian-LSTM, relating to the fields of sediment dynamics and marine engineering. The method includes: deploying pore pressure sensors to collect pore water pressure data in real time, performing standardization processing, and calculating the pore pressure change rate; constructing a creep rate calculation model based on the effective stress principle and power law; building a time-series feature dataset using sliding window technology, constructing and training a Bayesian-LSTM network model, and introducing a Monte Carlo Dropout layer to assess prediction uncertainty; outputting a high-precision creep rate using a multiple sampling averaging strategy; calculating the cumulative creep distance through numerical integration, and constructing confidence intervals to achieve medium- and long-term trend prediction and risk analysis. This invention integrates physical mechanisms and data-driven approaches, effectively handling data noise and prediction uncertainty in the complex environment of the deep sea, significantly improving the accuracy and reliability of long-term creep prediction, and providing a scientific basis for deep-sea engineering safety assessment.

The present application relates to the field of real-time monitoring and coordinated control of power grid, in particular to a kind of distributed photovoltaic grid-connected regional power grid real-time monitoring and coordinated control system, system includes sensing acquisition, multi-time scale prediction, self-evolution calibration and distributed coordination control module;Sensing acquisition module is collected wide frequency domain electrical quantity and multidimensional meteorological quantity by double-layer sensing network, and the repair data is obtained by abnormal detection;Multi-time scale prediction module integrates ultra-short-term, short-term and medium and long-term prediction, realizes cross-scale cooperation based on joint state space model etc.;Self-evolution calibration module is based on deep reinforcement learning to compensate the residual error of multi-scale prediction online;Distributed coordination control module combines improved alternating direction multiplier method and non-dominated sorting genetic algorithm, obtains optimal power instruction that meets voltage, frequency and harmonic ternary constraint;The present application improves the stability of power grid operation under high penetration rate photovoltaic grid-connected scene.