16001results about How to "Guaranteed reliability" patented technology

The invention discloses a decision-making method and model for off-line reinforcement learning and continuous on-line fine tuning, and the method comprises the steps: obtaining original text-image pair data and historical track data, and obtaining an enhanced state sequence through feature matching verification and standardization processing; performing sequence prediction by using a multi-level attention mechanism, and obtaining pre-training model parameters through knowledge distillation and task adaptability enhancement; on the basis of stability evaluation and plasticity control, online knowledge migration is carried out on the model, and optimized model parameters are obtained by combining experience playback optimization; and finally, performing multi-dimensional performance evaluation and resource optimization. According to the method, through multi-modal feature fusion, dynamic knowledge migration and an adaptive optimization mechanism, the problems of insufficient feature representation, disastrous forgetting, low optimization efficiency and the like are effectively solved, and the learning efficiency and generalization ability of the model are improved.

The invention relates to a control method and device for an intelligent agent with a body and a readable storage medium. The method comprises the steps of obtaining an instruction input by a user; performing intention recognition on the instruction to generate a current task; calling a large language model, and generating a planned path conforming to the current task based on the large language model and the knowledge graph; decomposing the current task to generate an operation instruction comprising a series of executable action sequences; calling a rule base to ensure that the generated action sequence conforms to various preset rules and operation standards included in the rule base; in the task execution process, environment data fed back by different sensors in the multi-mode perception sensor are collected in real time; and inputting the environment data into the large language model, understanding and processing the environment data by adopting the large language model, and adjusting an operation instruction or planning a path in real time according to a current task and an analysis result of the environment data. The control method provided by the invention is more intelligent, safer and highly adaptive, and can adapt to a more complex task environment.

The invention discloses an underwater detector cluster adaptive detection method and system based on distributed reinforcement learning, and the method comprises the steps: obtaining detector state data and environment parameter data, obtaining a standardized state matrix through adaptive standardization processing, building a group topological feature matrix based on spatial relation quantification, and obtaining a group topological feature matrix; generating a final feature representation; constructing an action prototype matrix and an action coupling tensor, optimizing an action space based on constraint conditions, and obtaining a feasible action set through multilayer evaluation and screening; a comprehensive reward function is constructed based on a multi-dimensional reward mechanism, strategy optimization and experience optimization are carried out, and a final optimization strategy is obtained; a multi-layer decision evaluation system and a real-time monitoring mechanism are established, and an experience knowledge base is constructed and continuously optimized. According to the invention, through multilevel feature extraction, dynamic action optimization, adaptive strategy learning and knowledge accumulation, the cooperative performance and environment adaptability of the underwater detector cluster are improved.

The invention provides a medical data privacy protection method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the feature analysis of medical data, building a scoring matrix, generating a privacy risk assessment result, dividing a security domain, and building a distributed cooperation mechanism through federal learning; preprocessing data at a local node, constructing a deep neural network model, encrypting gradient information by using a dynamic key matrix, and performing distributed aggregation operation and verification; forming a data privacy protection scheme; and performing identity verification and anomaly detection on the access request, and dynamically adjusting the access granularity. According to the method, the security and the flexibility of medical data privacy protection are improved, and meanwhile, the availability of the data is ensured.

The invention discloses a power distribution network power transmission line intelligent control and measurement system and method based on an AI algorithm, and the system comprises a data collection module, a data preprocessing module, a multi-modal data fusion module, an AI fault diagnosis module, a control execution module and a communication module. Comprehensive monitoring of the running state of the power transmission line is achieved, historical and current data are analyzed in real time based on an improved dynamic multi-mode adaptive optimization model DMAOM, and the self-learning capability is achieved. A flexible adaptive control strategy is introduced, and a control scheme is dynamically adjusted according to real-time data. Under the abnormal condition, the system can rapidly execute current limiting, load transferring or circuit breaking operation, and through a feedback mechanism, the system can continuously optimize monitoring and response strategies after each fault processing. According to the method, the safety, stability and operation efficiency of the power transmission line of the power distribution network are remarkably improved, and the method has important practical application value and wide market prospects.

The invention relates to a multi-objective optimization scheduling method and system for a multi-AGV flexible job shop. The method comprises the steps of collecting job shop layout data, AGV operation parameters and production task data, and constructing a scheduling system basic model; performing recurrent neural network training and physical constraint model integration to obtain a hybrid scheduling prediction model; subtask decomposition and path search processing are carried out, and a dual-objective optimization model is obtained through constraint linearization conversion; performing particle swarm optimization solution on the dual-objective optimization model, and obtaining a non-dominated solution set through two-stage iterative calculation of an adaptive weight strategy and constraint conversion; performing multi-dimensional evaluation calculation on the non-dominated solution set to obtain an optimal scheduling execution scheme; and performing model feedback updating on the hybrid scheduling prediction model to obtain a target scheduling prediction model. According to the invention, multi-target balance of task completion time and energy consumption is realized, and the real-time response capability and operation stability of the system are improved.

The invention relates to the technical field of logistics transportation scheduling, in particular to a logistics management system based on intelligent scheduling, which comprises a data acquisition module, a demand analysis module, an intelligent scheduling module, a path planning module, a monitoring and feedback module, an optimization module and the like. The data acquisition module is used for collecting information such as goods, vehicles, storage, customer demands and traffic road conditions; the demand analysis module predicts logistics demands by using a time sequence analysis or machine learning algorithm; the intelligent scheduling module adopts a greedy and dynamic programming mixed algorithm to allocate resources; the path planning module calculates an optimal path according to road conditions and starting and ending points; the monitoring and feedback module monitors logistics parameters in real time and feeds back abnormity; and the optimization module optimizes the scheduling scheme according to the fitness function based on the genetic algorithm. According to the invention, data and resources of all links of logistics are effectively integrated, accurate demand prediction, efficient resource configuration, transportation path optimization, real-time monitoring feedback and overall performance improvement of the system are realized, the logistics cost is significantly reduced, and the transportation efficiency and the service quality are improved.

The invention relates to the technical field of optical detection, in particular to a workpiece surface defect detection method and system based on the optical technology, and the method comprises the following steps: S1, generating a curvature model of a workpiece; s2, dynamically adjusting the irradiation angle and the light spot shape of a light source according to the curvature model in the S1; s3, adjusting the focal length of the detection equipment in real time; s4, imaging acquisition is carried out on the workpiece at the same time through multiple angles; s5, correcting image distortion caused by surface curvature change, and fusing the multi-angle images into a high-precision workpiece surface image; s6, defect features of the surface of the workpiece are extracted, defects are classified, positions are marked, and a defect detection report is generated; according to the invention, by dynamically adjusting the light source and the focal length and combining multi-view imaging and advanced image processing technologies, efficient and accurate defect detection is realized, and the product quality and the production efficiency are remarkably improved.

The invention relates to the technical field of knowledge question answering, in particular to an intelligent question answering method and system based on multi-module collaborative optimization, and the method comprises the steps: inputting a question to be answered into a knowledge question answering model, and outputting a knowledge question answering result by the knowledge question answering model; a knowledge range judgment module in the model judges whether the problem can be solved by relying on own knowledge, and if not, a dynamic retrieval module is entered; the dynamic retrieval module performs similarity retrieval on the content of the memory knowledge base according to the question to be answered, and if the retrieval result does not meet the requirement, the multi-level question rewriting module is entered; the multi-level question rewriting module rewrites questions to be answered and inputs the rewritten questions into the knowledge screening module; the knowledge screening module outputs the screened documents according to the rewritten questions, the auto-reflection optimization module generates preliminary answers according to the documents and the questions and judges whether the preliminary answers are reasonable or not, auto-reflection optimization is carried out if the preliminary answers are not reasonable, and a new solution is provided for development of the intelligent question and answer technology.

The invention discloses an intelligent real-time emotion evaluation method for social media and online text data based on a multi-modal knowledge graph, and the method comprises the steps: firstly obtaining and preprocessing multi-modal original data; secondly, designing single-mode feature extraction of dialogue texts, voices and facial expressions based on BERT-BiLSTM, AST and ResNet-50 pre-training models; furthermore, sharing among multiple modes and private emotion semantic information are considered, and complementation of cross-mode emotion information is promoted by enhancing repeated emotion information and utilizing uniqueness of private information. Meanwhile, a multi-mode knowledge graph method is provided from bottom to top to carry out emotion real-time evaluation. Analyzing user behaviors by using DeepFM and generating user features, and capturing external knowledge layer features in combination with K-BERT; finally, on the basis of a TransformerEncoder structure, cross-modal emotion information interaction and feature fusion are achieved, real-time recognition and classification of multi-modal emotion perception are completed through a Softmax layer, and therefore the purpose of intelligent real-time emotion evaluation is achieved.

The invention discloses a building deformation analysis method and system based on three-dimensional laser scanning and deep learning, and the method comprises the steps: obtaining original point cloud data, carrying out the hierarchical processing through a feature decomposition method, obtaining the hierarchical feature data, carrying out the fusion analysis in combination with environment parameter data, and eliminating the environment influence. Obtaining high-fidelity point cloud data through quality evaluation optimization and spatial-temporal feature fusion; component segmentation and deformation feature extraction are carried out on the high-fidelity point cloud data, and fusion deformation feature data are obtained through multi-scale analysis; inputting the deformation feature data into a hybrid neural network for feature enhancement and deformation evolution analysis to generate deformation prediction data; and performing physical rule verification, multi-source data cross verification and space-time continuity optimization on the prediction result, and outputting final optimization data and a reliability report. According to the invention, through a multi-level data processing and verification mechanism, the accuracy and reliability of building deformation monitoring are improved.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention relates to the technical field of data processing, and discloses an industrial one-stop multi-source data acquisition and monitoring system and equipment, and the system comprises a data acquisition module which is used for obtaining multi-source industrial monitoring data and generating a data stream input node; the data preprocessing module is used for processing the input data in the data flow model to generate a standardized data flow; the data conversion module is used for dynamically converting data nodes in the standardized data stream according to different data protocols to generate a compatible data stream; the task scheduling module is used for optimizing the data flow and distributing the data flow to a target node according to the compatible data flow in combination with a node priority and a dynamic scheduling strategy in the data flow model; and the exception detection module is used for carrying out exception state analysis on the optimized data stream and judging whether an exception exists or not according to a preset rule. According to the technical scheme, the compatibility and reliability of the monitoring system can be improved.

The invention discloses a self-adaptive communication method and system for ubiquitous electric power Internet of Things intelligent metering equipment, relates to the technical field of Internet of Things communication, and solves the problems of inaccurate quantitative communication, insufficient communication efficiency and poor flexibility in existing intelligent metering equipment communication. According to the invention, the accuracy and reliability of data transmission are improved through a dynamic network self-organizing algorithm and a multi-hop communication mechanism, the communication efficiency is optimized through multi-path transmission and dynamic spectrum management, and the flexibility and adaptability of the network are enhanced through an adaptive modulation coding model and a real-time optimization link selection strategy. And secure transmission of the data is further guaranteed through a multi-layer security mechanism, and the communication performance and flexibility of the intelligent metering equipment are remarkably improved.

The invention discloses a substation network security defense system based on artificial intelligence. The system comprises a data collection module for collecting transformer substation network data; the anomaly detection module is used for identifying an abnormal mode and an intrusion behavior; the threat intelligence analysis module is used for identifying potential threat and vulnerability information; the intelligent security monitoring and response module is used for evaluating the network security state; the malicious code detection module is used for detecting, analyzing and isolating malicious software; the intelligent security prediction module is used for predicting a network security event and carrying out early warning prompt; the self-adaptive security policy module is used for automatically adjusting or updating an optimal security policy; the system updating and maintaining module is used for updating and maintaining system modules and functions; the user interface and alarm module is used for providing safety state display and alarm for a user; the data backup and recovery module is used for backing up and recovering key data; and a compliance auditing module which makes operation and configuration accord with security standards. According to the invention, hierarchical, intelligent and active protection of the transformer substation network is realized.

The invention provides a marine pollution tracing and monitoring method based on satellite remote sensing and an unmanned aerial vehicle, and relates to the technical field of marine pollution tracing and monitoring, and the method comprises the steps: configuring a pollution diffusion path prediction model and a data driving model based on range pollution data, precise pollution data and deepwater data after multi-modal data fusion, and obtaining a pollution prediction result and pollution error data, carrying out hybrid drive fusion based on the pollution prediction result and the pollution error data, and tracking an initial source of pollutants to obtain pollution source tracing data. Through preprocessing and fusion of multi-modal data, in combination with range pollution data, precise pollution data and deep water data, a pollution diffusion path prediction model and a data driving model are dynamically adjusted, so that a more precise pollution prediction result and pollution error data are generated, and meanwhile, through reverse diffusion simulation and pollution traceability analysis, a more precise pollution prediction result is obtained. The high efficiency of pollution source positioning and decision support is improved, and an accurate and decision basis is provided for coping with marine pollution events.

The invention discloses a multi-modal data fusion-based high dam underwater defect measurement method, which comprises the following steps of: obtaining water flow velocity data, water pressure data and water quality turbidity data, performing preprocessing by adopting a self-adaptive median filtering algorithm, and constructing a hydrodynamic field feature model; then generating an initial trajectory based on the hydrodynamic field feature model and detection target area data, and performing trajectory optimization and stability evaluation in combination with dynamic constraint conditions; adjusting sonar parameters according to the turbidity influence and the trajectory parameters to perform data acquisition; carrying out space-time alignment through extended Kalman filtering, and carrying out feature fusion and optimization by adopting an attention mechanism; and finally, performing multi-scale consistency verification and accurate positioning mapping to obtain a final defect detection result. According to the invention, the problems of defect detection precision and reliability in a complex underwater environment are solved.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention discloses a building measurement method and system based on three-dimensional scanning, a medium and a product, and relates to the field of exploration, and the method comprises the steps: obtaining BIM model data and a measurement parameter threshold value of a target building, and determining a subarea scanning path according to the BIM model data; scanning the target building according to the subarea scanning path to obtain a point cloud data set of the target building; generating a three-dimensional digital model of the target building according to the point cloud data set, and dividing the three-dimensional digital model into a plurality of feature regions; each feature region comprises at least one feature component; extracting a feature point set in the feature region, and establishing a mapping relationship with a corresponding point set of a corresponding region in the BIM model data; calculating actual measurement parameters of the feature region based on the mapping relation; and comparing the actual measurement parameter with the measurement parameter threshold, determining an abnormal area and generating a measurement technical report. By implementing the method, the building data can be more comprehensively acquired, and the precision of building measurement is improved.

The invention belongs to the technical field of learner cognitive state interpretation, and particularly discloses a brain-language-vision large model-based cognitive state interpretable method, which comprises the following steps of performing multi-modal data collection for the cognitive state of a learner; performing learner cognitive state characterization based on a multi-scale map convolutional network; brain-vision-language mixed multi-modal time sequence feature fusion based on hierarchical mutual information maximization is carried out; embedding knowledge graph nodes for enhancing the complex task understanding ability of the multi-modal large model; training a brain-vision-language multi-modal large model guided by knowledge; recognizing a learner cognitive state based on a multi-modal large model; the cognitive state of the learner can be explained and analyzed under the support of multi-mode owned reasoning. According to the cognitive state interpretable method based on the brain-language-vision large model, the input multi-modal fusion features are converted into high-quality prompts so as to guide the large language model to extract implicit knowledge, and important support is provided for reasoning generation.

The invention discloses a multi-dimensional time sequence photovoltaic grid-connected harmonic current prediction control method, and particularly relates to the technical field of power system control. Grid-connected original multi-dimensional electrical signals are collected and decomposed into multi-dimensional frequency signals through Fourier transform, the characteristic components of an independent harmonic source are separated through the blind source separation and non-negative matrix factorization technology, and the frequency and amplitude characteristics of the characteristic components are extracted; then, a multi-dimensional time sequence prediction model is constructed through a recurrent neural network, dynamic behaviors of multiple harmonic sources are predicted at the same time, and frequency domain data are reconstructed and frequency aliasing errors are corrected in combination with high-frequency compensation and multi-scale analysis; and finally, inputting a prediction result into a photovoltaic inverter control system, dynamically generating a compensation current opposite to a target harmonic wave, realizing effective offset of the harmonic wave, accurately processing the multi-source harmonic wave problem in a complex power grid environment, and greatly improving the harmonic wave suppression capability, the electric energy quality and the system operation stability of a photovoltaic grid-connected system.

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 relates to the technical field of intelligent robots, in particular to a service robot intelligent emotion interaction method, which comprises the following steps: acquiring emotion related data of a user through a multi-modal sensor on a service robot, analyzing the acquired data based on a deep learning model, and extracting emotion features of the user; future emotion changes are predicted through a time sequence analysis algorithm, a service robot generates a personalized interaction strategy through an emotion decision-making system according to the current and predicted emotion states, and the system adopts a self-adaptive algorithm based on reinforcement learning, so that the interaction strategy can be continuously optimized along with the emotion changes of a user; the robot performs modeling and optimization on long-term interaction data by utilizing a self-learning mechanism in combination with emotional feedback of the user, so that the precision and interaction effect of emotional response are gradually improved; according to the method, the problems of inaccurate emotion recognition, inflexible interaction strategy and lack of adaptive optimization in the prior art are effectively solved, and the interaction experience of the service robot and the user is remarkably improved.

The invention relates to the technical field of banking business marketing data processing, in particular to a multi-channel-based banking business marketing data processing method and system, and aims to guarantee the accuracy and reliability of a marketing strategy by integrating multi-channel data and acquiring and verifying a banking business marketing data source in real time. Then, a machine learning algorithm is used for generating a customer portrait, analyzing a behavior pattern and potential requirements of the customer portrait, generating a personalized marketing strategy, a natural language processing technology is combined, real-time performance and pertinence of marketing content are ensured, pushing is carried out according to customer preferences during pushing, the pushing opportunity is optimized, and the system monitors feedback behaviors in real time after pushing, so that the marketing quality is improved. A strategy effect is evaluated, marketing activities are optimized by using an A / B test, closed-loop feedback is formed, a strategy is continuously improved, and high efficiency and accuracy of a marketing scheme are ensured. According to the method, automatic strategy generation and optimization are realized through the AI technology, the marketing efficiency and customer experience of banking business are greatly improved, and the method is suitable for a multi-channel data processing scene.

The invention relates to the technical field of equipment remote control, and discloses a remote control method and system for sewage treatment equipment and a storage medium. The method comprises the following steps: performing communication encryption and identity authentication processing on a standard data packet of a sewage treatment system, and constructing an equipment communication link; performing intelligent analysis and state diagnosis processing on the data in the equipment communication link, and generating an equipment operation state report; performing control strategy generation and parameter matching processing according to the equipment operation state report, and outputting an equipment control instruction; performing hierarchical analysis and execution state monitoring on the equipment control instruction, and establishing a control feedback data link; and performing remote optimization treatment according to the data in the control feedback data link so as to realize remote intelligent control of the sewage treatment equipment. Under the condition that the sewage treatment equipment is dispersedly arranged, the real-time monitoring of the running state of the equipment and the intelligent adjustment of the process parameters are realized, and the stability of the treatment effect is ensured.

The invention discloses a remote regulation and control LED lighting system based on the Internet of Things, and relates to the technical field of LED lighting systems, comprising an LED lighting equipment module which comprises an efficient lamp, a microcontroller, an Internet of Things communication module, a remote control terminal module supporting multi-protocol and encryption, having Mesh networking, a multi-terminal control, having multiple functions and innovation formulas, and a sensor module; the intelligent LED lighting control system has the advantages that environment information is collected, the data management and analysis module stores and analyzes data, energy-saving management and the like are achieved, all the modules work cooperatively, and the functions of remote intelligent regulation and control, fault self-inspection, butt joint with other systems and the like of LED lighting are achieved. Through collaborative innovation of multiple modules, remote intelligent regulation and control of LED lighting are realized, the lighting quality and user experience are improved, energy is saved, consumption is reduced, the system has the functions of fault self-checking and alarming and the like, the reliability and compatibility of the system are improved, and the diversified requirements of modern lighting systems are met.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

The invention provides a chip overheating automatic protection control method and system, and relates to the technical field of chip protection, and the method comprises the steps: arranging distributed temperature sensors on a chip, obtaining the real-time temperature data of different regions in the chip, dynamically adjusting the data collection frequency and precision, and generating an optimized temperature data flow; and inputting the optimized temperature data stream into a pre-trained temperature anomaly detection model, detecting the temperature anomaly condition of each region in the chip in real time, outputting the temperature anomaly level of the abnormal region, and triggering thermal emergency processing of different intensities according to the temperature anomaly level. The temperature and power consumption information of the abnormal area is sent to the global manager; the method comprises the following steps: generating an initial chip protection strategy, predicting a future temperature change trend of a chip, identifying a potential high-temperature abnormal region, optimizing the initial chip protection strategy by combining a prediction result, generating an optimal chip protection strategy, and realizing chip overheating automatic protection control according to the optimal chip protection strategy.

The invention provides an OracIeRAC-based CDC synchronization method and system, and relates to the technical field of OracleRAC, and the method comprises the steps: deploying an incremental log analyzer, a session state collector and a resource monitor, and respectively collecting atomic operation, concurrent transaction information and node performance indexes; and identifying data stream features by using an intelligent classification model, constructing a priority matrix, and performing fine-grained acquisition on high-priority data based on an adaptive sampling strategy. According to the method, multi-dimensional evaluation indexes are further utilized, a time sequence prediction model is constructed to predict node loads, data synchronization tasks are distributed to low-load nodes based on a load balancing matrix, and a two-stage submission protocol is adopted to ensure data consistency. By monitoring the synchronization state in real time and dynamically adjusting the allocation strategy, the CDC synchronization efficiency and stability in the Oracle RAC environment are effectively improved, and the synchronization delay is reduced.

The invention relates to the technical field of power systems, and discloses a digital twinning-based power grid support performance evaluation system and method. Comprising a data acquisition and processing module, a digital twin model construction and integration module, a multi-scene simulation and prediction evaluation module, an intelligent scheduling and fault prediction module, a multi-source data fusion and big data analysis module, a performance evaluation and optimization decision module, and a visualization and man-machine interaction module. The data acquisition module is used for acquiring real-time states, load data and external environment data of equipment in a physical power grid. Through an intelligent scheduling algorithm, a digital twinborn model and a fault prediction technology, the system can monitor and optimize a new energy access process in real time, solves the challenge of new energy power generation fluctuation on power grid stability, and the algorithm can dynamically adjust power grid load distribution and predict potential faults in advance under the condition of centralized access or fluctuation of new energy. And the stability of power transmission and the safety of equipment are ensured.