3082 results about "Correlation analysis" patented technology

The invention discloses a modeling method based on shield tunneling data feature analysis and parameter relevance, and relates to the field of tunnel engineering data processing. The method comprises the steps that shield tunneling time sequence parameters are obtained, and a non-uniform time sequence is resampled into a space-aligned standardized footage domain sequence through state cleaning and coordinate domain transformation; by means of mixed variable rejection and lagging correlation analysis, environment common cause interference is stripped, physical response delay among parameters is recognized, and a time-delay directed correlation graph model is constructed; and inputting the footage domain sequence and the graph model into a graph neural network, performing feature learning by using a time delay compensation aggregation mechanism, and outputting a key parameter influence degree set with symbols based on a prediction gradient. According to the method, the problem of data space-time dislocation caused by propelling speed fluctuation and the problem of parameter relevance misjudgment caused by physical response lag are solved, and accurate identification and explanation of shield tunneling key parameters are achieved.

The invention discloses an intelligent substation communication link fault accurate positioning method and system, and the method comprises the steps: obtaining a configuration file and equipment state data, carrying out the processing of the configuration file and the equipment state data, and generating a standardized link feature vector and a marking data set; constructing a hybrid deep learning model, and optimizing parameter configuration of the hybrid deep learning model by adopting an optimization algorithm to obtain a parameter-optimized hybrid deep learning model; training by using a real fault sample in combination with a virtual fault sample generated by a generative adversarial network, optimizing a time sequence prediction capability through an echo state network, and generating a fault positioning model; in combination with the link state data, outputting a fault link positioning result and confidence evaluation through multi-stage confidence evaluation and topological correlation analysis; and carrying out virtual-real corresponding verification in combination with the configuration file, carrying out parameter optimization on the fault positioning model, and outputting a fault positioning system. The problems that the fault positioning precision is low, the response speed is low, and complex fault scenes cannot be processed are solved.

The invention discloses an intelligent early warning method for pipeline blockage of a slurry circulation system of a slurry shield, which relates to the field of intelligent early warning, and comprises the following steps of: performing spatial-temporal feature analysis on a standardized multi-dimensional data stream, constructing a blockage feature knowledge graph based on pipeline position and time sequence correlation analysis, and generating a blockage feature vector through a graph neural network; based on the blockage feature vector, analyzing the dynamic change trend of particle distribution through a long-short-term memory network and predicting the particle blockage risk in combination with an acoustic signal, then performing adaptive judgment by fusing geological conditions and construction stage information to obtain a risk assessment result, and inputting the risk assessment result and the blockage feature vector into digital twinborn simulation to obtain the particle blockage risk. A blockage scene is predicted based on fluid dynamics and a particle sedimentation model, early warning parameters are adjusted through Bayesian optimization, and graded early warning signals are generated; according to the invention, by generating the blockage feature vector, the recognition capability of the early local abnormal propagation trend is enhanced, and a reliable basis is provided for accurately predicting the blockage risk.

The invention provides a multi-source data fusion modeling method and system in an aeration process, and is applied to the field of intelligent aeration control in sewage treatment. The method comprises the steps that multi-source time sequence data such as dissolved oxygen, turbidity, flow, temperature, power and pool bottom pressure pulsation signals are collected, dissolved oxygen response lag is calculated through cross-correlation analysis with power change as the reference, time sequence alignment is carried out, and a dissolved oxygen reference interval is predicted by utilizing calibration data in combination with a physical constraint LSTM model; performing spectral analysis on the pressure pulsation signal to extract a gas-liquid coupling characteristic value, and generating a cooperative regulation instruction of the frequency of the blower and the rotating speed of the stirrer based on the information; by means of the scheme, control oscillation caused by lag of the dissolved oxygen sensor can be effectively overcome, online monitoring of bubble form distribution is achieved, the gas-liquid mass transfer efficiency is improved, invalid aeration is avoided, and system energy consumption is remarkably reduced on the premise that stable effluent quality is guaranteed.

The invention relates to an enterprise multi-source data intelligent association analysis method based on artificial intelligence and a large model, and the method comprises the steps: introducing time sequence dynamic analysis, a business rule base and statistical correlation test, carrying out the multi-dimensional and automatic cross verification and consistency test of an association pair outputted by a semantic association engine, and carrying out the analysis of the association pair. Screening out a high-confidence correlation set conforming to the business logic, the time sequence evolution rule and the statistical significance; and packaging to form a reusable business insight analysis model based on the enterprise data knowledge graph, receiving a business query request by the model, automatically generating a deep analysis report for business process optimization and potential risk early warning through graph reasoning, path discovery or an abnormal sub-graph detection algorithm, and pushing a result to a decision support system.

The invention provides a thermoelectric unit intelligent early warning method and system based on an artificial intelligence model. The method comprises the steps that firstly, multi-source operation monitoring information of different operation dimensions of core components such as a turbine, a boiler and a generator of a thermoelectric unit is obtained; performing time sequence correlation analysis on each modal monitoring data, constructing a dynamic correlation matrix, generating a modal conflict correction coefficient, and obtaining a time sequence dependence feature set; inputting the model into a hierarchical evolutionary early warning model, and obtaining an evolutionary fusion early warning feature vector through modal conflict correction and dynamic feature evolution modeling; generating a working condition self-adaptive dynamic early warning threshold value based on the current operation working condition; through comparison and abnormal traceability of the traceability verification module, an abnormal root component and a propagation path are determined, and a targeted traceability early warning instruction is output, so that the early warning accuracy and adaptability of the thermoelectric unit are improved, and safe and stable operation of the unit is guaranteed.

The present invention relates to the technical field of very-short-term photovoltaic power forecasting, and in particular to a very-short-term photovoltaic power forecasting method and system for real-time control, which intend to improve precision and real-time performance in photovoltaic power forecasting. The method comprises the following steps: performing normalization processing on meteorological data, and performing a feature correlation analysis; using a BP neural network to perform short-term photovoltaic power forecasting, inputting the meteorological data and historical output data, and outputting a short-term forecasting value with a resolution of 15 minutes; and performing spline interpolation and outlier removal on an upper-layer result of the BP neural network, and using same as a long short-term memory recurrent neural network input, so as to improve a temporal resolution of forecast data and obtain very-short-term photovoltaic power forecast data with a resolution of 1 minute. The method comprehensively considers meteorological factors and uses advanced neural network models and data processing techniques to achieve photovoltaic power forecasting on a very short temporal scale while ensuring forecasting precision, making the method suitable for the real-time control and optimized operation of photovoltaic power stations.

The invention relates to the technical field of equipment health monitoring. The method comprises the following steps: constructing a modal drift factor reflecting a modal time sequence change trend based on a multi-modal input feature set so as to obtain a static feature and a dynamic feature, the method comprises the steps of obtaining a modal availability evolution sequence, inputting the modal availability evolution sequence into a pre-trained modal availability prediction model to obtain a data modal type, obtaining feature distribution condition information based on the data modal type, constructing a modal credible distribution diagram according to the feature distribution condition information, and determining a fault based on the modal credible distribution diagram and a preset fault determination rule. And constructing a fusion feature representation vector, inputting the fusion feature representation vector into the target diagnosis model, executing feature correlation analysis and trend recognition processing, and outputting a health state judgment result of the power transformation equipment. The method has the effect of improving the health state intelligent monitoring capability of the power transformation equipment.

The invention discloses an optical fiber embankment underwater piping leakage event time-space correlation analysis method, and relates to the technical field of leakage event intelligent identification and risk assessment in embankment safety monitoring, and the method comprises the following steps: S1, obtaining continuous time-space monitoring data of an embankment underwater region obtained through monitoring by a distributed optical fiber sensing system; and S2, processing the continuous space-time monitoring data by adopting a feature recognition model based on a neural network, and recognizing a suspected leakage event. According to the time-space correlation analysis method for the underwater piping leakage event of the optical fiber embankment, by fusing multi-level data processing and self-adaptive feature learning, false alarms caused by environmental interference are effectively restrained, and the recognition accuracy of a real leakage event in a complex underwater scene is improved. An analysis framework combining space-time association diagram construction and physical mechanism verification is adopted, the internal relation between events can be deeply mined, and the space-time evolution rule of a seepage path is accurately restored.

The invention relates to the technical field of intelligent water affair and urban water supply system informatization, in particular to a water supply pipe network hydraulic model calibration and leakage locating method and system, and the method comprises the steps: obtaining the pressure and flow data of a plurality of monitoring points in a pipe network in real time; taking the model as a boundary condition to drive a hydraulic model to carry out real-time simulation, and calculating a theoretical value; calculating a residual error between a theoretical value and an actual value of the monitoring point; judging a triggering model calibration event or a leakage suspicion event based on a residual error abnormal mode; if calibration is triggered, inverting and updating global parameters of the model through an optimization algorithm to realize self-calibration; if leakage is triggered, a suspected area is determined by combining pressure space distribution analysis, and a leakage pipe section is accurately positioned through analog simulation and correlation analysis. Through closed-loop feedback of real-time data and the model, the model can adapt to changes of a pipe network system, dynamic self-calibration of the water supply pipe network hydraulic model is achieved, and the problem of precision attenuation of a traditional static model is solved.

The embodiment of the invention relates to the technical field of data analysis, in particular to a national secret log auditing system which is characterized in that firstly, an operation behavior record sequence generated in the running process of a national secret application system is collected by the national secret log auditing system, and the sequence is composed of log entries containing identity verification information, resource access path information and state transition description information; performing context semantic association analysis on the operation behavior record sequence to obtain a semantic element extraction result and a semantic dependency relationship; then performing multi-level compliance verification based on a national secret security audit rule system, and generating a compliance judgment conclusion of a log entry level and a cross-entry-level abnormal behavior pattern recognition report; and finally, according to the judgment conclusion and the identification report, constructing a national secret log security auditing result set which comprises a risk level evaluation result, a violation evidence chain association graph and a security reinforcement strategy suggestion list, thereby effectively improving the accuracy and comprehensiveness of national secret log auditing.

The invention provides an intelligent emergency decision support method and device based on multi-Agent cooperation. The method comprises a task planning module, an information acquisition module, a data fusion module and an execution monitoring module. The task planning module adopts a hierarchical decision-making mechanism, performs task decomposition in a plan making stage, generates a plurality of candidate execution paths by using thinking tree reasoning in a plan execution stage, and selects an optimal scheme. The information acquisition module acquires multi-source information such as network search, knowledge graph and geographic data through a plurality of professional Agents. And the data fusion module adopts a blackboard mode to manage heterogeneous information, and realizes intelligent abstract and correlation analysis through a large language model. And the execution monitoring module realizes dynamic optimization and fault self-recovery of the system through a multi-dimensional progress evaluation and cooperative monitoring mechanism. According to the invention, the problems of insufficient information processing capability, low decision-making efficiency and poor system stability of a traditional emergency decision-making system are solved. The information collection and processing efficiency is improved through large language model multi-Agent cooperation, the decision quality and accuracy are improved through a hierarchical decision mechanism, and long-term stable operation of the system is guaranteed through self-adaptive monitoring. The method is suitable for complex emergency decision-making scenes such as natural disasters, safety accidents and public health events.

The invention discloses an AI-based tendering and bidding full-process intelligent generation and review method and system. The method comprises the steps of tendering full-process intelligent generation, bidding full-process intelligent generation and bidding review. An AI scoring model is adopted for review, the technical scheme of each bidding document is deeply analyzed through a natural language processing technology, and real-time cross validation is carried out with a knowledge graph, so that the score of each sub-item is provided with detailed comments and a definite evidence chain, the objectivity, transparency and credibility of review are greatly improved, and the review efficiency is improved. The review quality is improved from experience judgment to a data and knowledge driven level; secondly, potential violation behaviors are identified through a surrounding bidding risk report generated through multi-dimensional correlation analysis, and the risk prevention and control capability is effectively enhanced; and finally, based on comprehensive data analysis, automatically generating a bid-winning candidate sorting and comprehensive review report, and ensuring that the calibration result is optimal.

Provided in the present invention are a method and system for real-time update of a medical knowledge base, and a medium and a device. The method comprises: acquiring medical literature data in real time; analyzing the medical literature data, so as to obtain first structured data; performing correlation analysis on the first structured data and second structured data in a medical knowledge base, so as to obtain an analysis result; on the basis of the analysis result, determining whether to update the medical knowledge base; identifying the differences of the medical knowledge base before and after the update, so as to obtain a difference identification result; assigning a unique version number to each difference in the difference identification result, so as to obtain version update records; and on the basis of the version update records, using incremental hash table technology to perform data storage for each difference. The present application has a higher update efficiency without the risk of omission; and by means of difference analysis and by using incremental hash table technology to store difference data, the present application significantly reduces data storage space requirements, and also improves data access speed.

The invention relates to a small sample fault prediction method based on physical information guidance and multi-source adaptive fusion. The method comprises the following steps: collecting real operation data of preprocessing target equipment; according to the physical model or domain knowledge of the target equipment, generating simulation sensor data conforming to a physical rule under various fault modes of different degrees; constructing diversified training samples in combination with real data and simulation data; for different types of sensor data, designing corresponding feature extraction branches, mining potential fault features in the data, and dynamically adjusting the weight of each data source fault feature for fusion based on an output result of a physical model and data-driven feature correlation analysis; inputting the obtained fusion features into a fault prediction model based on a small sample learning framework for training; comprehensively considering the fault prediction result, the real operation data and the analysis result of the physical model, and carrying out quantitative evaluation on the overall health state of the target equipment; and causal diagnosis and visual interpretation are carried out.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention relates to the field of power system planning, in particular to a micro-grid boundary quantitative evaluation method and system for multi-dimensional analysis and dynamic verification. The method comprises the steps that power distribution network and micro-grid scheme data are acquired, and scene recognition and decoupling modeling are carried out after standardization processing; the method comprises the following steps: extracting end supply-preserving scene data, analyzing cost elements, and generating a critical cost threshold table through normalization processing and a threshold approximation algorithm; performing multi-dimensional parameter correlation analysis and integrated learning training based on the table, and constructing a multi-dimensional boundary index model; performing benefit matching calculation and green value accounting according to the result to generate an economic benefit decomposition structure; real-time streaming data processing and stability verification are combined to generate an economical efficiency boundary index set; and finally, generating a standardized evaluation file through matrix mapping and weight dynamic adjustment. According to the method, dynamic quantitative evaluation of the economy boundary of the micro-grid is realized, the capacity substitution benefit and the green value are effectively integrated, and an accurate basis is provided for planning decision.

The invention relates to the technical field of storm surge disaster monitoring and intelligent forecasting, and provides a regional collaborative storm surge water increase forecasting method of a coupling graph attention and gated circulation network, which comprises the following steps of: obtaining storm surge water increase residual field data based on a mixed wind field driving two-dimensional hydrodynamic model formed by typhoon parameterization and reanalysis data fusion, registering the water-increasing residual field data and the observation time sequence in time and space, and taking the registered data as physical prior input; constructing a dynamic edge weight graph structure based on a space-time causal correlation analysis method, and adaptively updating the dependency relationship among multiple observation stations; physical priori and key features are used as node input, a graph attention mechanism of GATv2 and a time sequence feature extraction capability of GRU are fused, and time-space coupling modeling is carried out on storm surge water increase; and carrying out multi-station joint prediction through regional cooperation, carrying out model evaluation and result optimization, and outputting a storm surge water increase prediction sequence with 15-minute time steps and 15-4-hour advance.

The invention relates to the field of construction temperature regulation and control, in particular to an intelligent temperature control method and device for fan foundation construction in an alpine region. The method comprises the following steps: collecting real-time concrete temperature monitoring parameters based on a distributed temperature sensor, carrying out time sequence temperature discrete trend analysis and spatial temperature fluctuation distribution evolution, and constructing a temperature fluctuation distribution evolution diagram; concrete surface environment parameters are extracted, heat exchange simulation processing is carried out on the temperature fluctuation distribution evolution diagram, multivariate coupling correlation analysis is carried out, and a temperature-environment quantitative relation model is constructed; and obtaining historical meteorological logs and meteorological environment data of the construction area, carrying out similar meteorological condition matching calculation, carrying out short-term meteorological trend prediction, and generating short-term meteorological trend prediction features. According to the dynamic temperature regulation and control requirements, safe construction of the fan foundation concrete in the high and cold environment is guaranteed, and the construction quality is improved.

The invention provides an enterprise production safety monitoring and checking system based on multi-source data fusion, and the system is characterized in that the system comprises a data collection module which collects production environment data, processes the data, and generates a time-space alignment data set; the feature screening module is used for screening features highly related to security from the space-time alignment data set to generate a feature matrix; the model construction module is used for constructing a risk assessment model, calculating a dynamic risk value according to the multi-dimensional features, and performing risk grading on the dynamic risk value; the response control module is used for starting a corresponding response strategy according to the risk classification and outputting an early warning instruction set and an equipment control signal; the monitoring calculation module is used for monitoring control, comparing risk value changes before and after treatment and calculating response efficiency; and the adjusting and optimizing module is used for carrying out dynamic adjustment according to the response efficiency and optimizing the response strategy. And the limitation of traditional single-dimensional monitoring is broken through, accurate correlation analysis of equipment, environment and personnel risks is realized, and the composite hidden danger recognition capability is remarkably improved.

The invention discloses a graphical user interface test case generation method based on a multi-agent framework, and relates to the field of graphical user interface tests.The graphical user interface test case generation method based on the multi-agent framework comprises the following steps that S1, a user demand document is obtained, and file text content and file format information are analyzed and extracted; s2, performing paragraph segmentation on the file text content to form logic semantic units; s3, carrying out correlation analysis on the segmented paragraphs, and obtaining a ternary file group; s4, performing paragraph annotation addition on the ternary file group, and constructing a semantic annotation result set; s5, extracting the agent framework to generate a graphical user interface test case, performing test recording, and storing a test result; s6, the result is verified, and a test case list and a software test report are generated. By conducting multi-dimensional analysis on the text content and the format structure in the document, the reduction efficiency from natural language description to structural semantic construction is improved.

The invention discloses a sewage data management system and method based on kitchen garbage treatment, and relates to the technical field of data management. The system comprises a data acquisition and historical database construction module, a correlation analysis and dynamic response rule construction module, a sewage data trend pre-judgment module and an operation regulation and data updating module. The data acquisition and historical database construction module is used for acquiring garbage treatment load and sewage data and constructing a historical database covering time period fluctuation and seasonal periods; the correlation analysis and dynamic response rule construction module constructs a dynamic response rule and determines the time delay duration; the sewage data trend pre-judging module is used for acquiring load data in real time and pre-judging a future sewage change trend; and the operation regulation and data updating module adjusts parameters of the sewage treatment system, collects effluent data to construct a new sample, supplements a database and updates a dynamic response rule. According to the invention, accurate sewage data management is realized, and the operation efficiency and stability of the sewage treatment system are improved.

The invention relates to the technical field of network security, and provides an APT attack traceability and path restoration method and system, attack path validity is verified through a dynamic causal element path generator in combination with an anti-factual adversarial network, and fine-grained entity modeling is realized through a hierarchical multi-modal entity inference system. A causal attention mechanism is optimized to improve association analysis precision, a federal incremental learning framework is constructed to realize dynamic updating and privacy protection, and the problems of poor rule adaptability, coarse granularity of entity modeling, insufficient causal association distinguishing and low calculation efficiency in the traditional technology are effectively solved. The method has the technical effects of dynamically generating an effective attack path, improving attack traceability accuracy, reducing calculation overhead and enhancing privacy protection.

The invention relates to an intrusion detection method based on cross-domain security management and a shared behavior model. The method comprises the following steps: acquiring multi-dimensional original data according to a preset cross-domain data acquisition rule and multi-domain node deployment; performing compliance, integrity and format matching degree verification on the original data, shielding sensitive information by using a dynamic desensitization technology based on a verification result, converting a heterogeneous data format, filtering missing field abnormal data, and obtaining compliance data; the method comprises the following steps: extracting a multi-dimensional feature set of user cross-domain access, constructing a shared behavior feature vector through weighted calculation, constructing a reference behavior model library in combination with a cross-domain security policy, and screening out intrusion behaviors and feature deviation data through feature comparison and behavior deviation calculation; according to the method, cross-domain security audit logs are fused for correlation analysis, intrusion behavior types and risk levels are judged by means of a Bayesian network model, differential security response strategies are generated and executed, and cross-domain intrusion detection and protection are achieved.

The invention relates to the field of resource association analysis, and discloses a knowledge graph-based archive decision support and association analysis method, which comprises the following steps of: firstly, acquiring structured and unstructured archive data; preprocessing is carried out through data cleaning, duplicate removal and format conversion, semantic disambiguation is carried out on historical fuzzy words in unstructured data, and entity normalization processing is carried out on multi-category names of the same character; secondly, based on the preprocessed structured data, entities, relationships and attributes are extracted, and entity association evidences are supplemented through a cross-modal alignment technology; performing consistency verification, and storing the verified entity relationship network into a graph database to form an archive knowledge graph; then, in combination with a graph traversal and time sequence analysis algorithm, a hidden association path is mined, and trend prediction is carried out; and finally, generating a decision evidence chain according to an association mining result. And establishing a knowledge graph dynamic updating mechanism, and automatically reconstructing the entity association network when a file or a historical research result is newly added.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention provides a product defect tracing method based on knowledge graph reasoning. The method comprises the following steps: constructing a knowledge graph based on a product hierarchical structure of a bill of materials and a manufacturing process hierarchical structure of a process route; when it is detected that data of a certain node in the knowledge graph is changed, calculating an update priority weight of a change node based on the occurrence frequency of the change node in a historical defect path and the hierarchy depth in the double-hierarchy structure; determining an updating range according to the updating priority weight, and updating the knowledge graph; and positioning defect nodes in the updated knowledge graph according to system response, executing path search from the defect nodes, identifying potential root cause nodes, and realizing product defect tracing. According to the method, accurate and real-time defect traceability of the product is realized based on multi-level feature correlation analysis of causal reasoning and dynamic knowledge graph construction and enhanced reasoning.

The invention provides a call center dialogue sentiment analysis method and system fusing voice and text, and relates to the technical field of voice signal processing, and the method comprises the steps: obtaining a voice signal of a call center and a corresponding transliteration text, extracting intonation, speed, sound intensity and pause sentiment features from the voice signal, meanwhile, deep language analysis is carried out on the transliterated text, and semantic emotion features related to context are extracted; and performing cross-modal correlation analysis on the voice emotion features and the semantic emotion features, and generating a time sequence correction coefficient for feature alignment by constructing a corresponding relation analysis framework between feature sequences. According to the method, language emotion information in voice and text is integrated, more accurate and comprehensive recognition of conversation emotion of the call center is realized, customer satisfaction is improved, and reliable emotion analysis basis is provided for efficiently processing customer appeals.

The invention belongs to the technical field of environment regulation and control, and discloses a livestock breeding environment dynamic prediction and regulation and control method based on machine learning. Comprising the following six key steps: firstly, preprocessing multi-source data, and constructing a standardized feature data set; secondly, performing time sequence feature extraction and environment-biological response correlation analysis to obtain a multi-factor interaction network; then, constructing a dynamic coupling model of the environmental parameters and the production efficiency; a multi-target reinforcement learning regulation and control model is constructed based on the animal husbandry production efficiency prediction space; predictive environmental parameter regulation and risk assessment are carried out in combination with real-time monitoring data; and finally, intelligent environment intervention is executed, and a regulation and control strategy is iteratively optimized through production index feedback. According to the method, the breeding environment is converted from passive response to active prediction, complex correlation understanding between environmental factors and biological indexes is established, and multi-target balance optimization of production efficiency, energy consumption and animal welfare is achieved.

The invention discloses a power distribution area fluctuation load early warning method and system, and relates to the technical field of power distribution network intelligent monitoring and control. The method comprises the following steps: constructing a digital twinborn model synchronous with an operation state, carrying out time domain and frequency domain conjoint analysis on a digital twinborn data set, extracting a power amplitude change rate and a phase jump index, and generating a load fluctuation vector; determining an adaptive threshold based on the statistical characteristics of the load fluctuation vector and historical load distribution, and generating a primary early warning signal when the fluctuation exceeds the limit; using a distributed collaborative analysis algorithm to identify a dominant load source and an imbalance category; performing correlation analysis on the identification result of the dominant load source and the primary early warning signal to generate comprehensive early warning information; and the comprehensive early warning information is output to a power distribution area monitoring platform, so that dynamic visualization and intelligent regulation and control are realized. According to the method, real-time identification and active early warning of the fluctuation load of the power distribution area can be realized, and the safety and the intelligent level of system operation are improved.