50 results about "Temporal correlation analysis" patented technology

The invention relates to the technical field of pollutant tracing, and discloses a method and a system for tracing new pollutants in various environmental media based on machine learning. The method comprises the following steps: collecting multivariate environment data to construct a pollutant fingerprint database; performing space-time correlation analysis on the fingerprint database and the environment data, and reconstructing a transmission path; converting the data, the fingerprint database and the path diagram into source node fusion to construct an analytical model, and forming a contribution rate matrix; and positioning the pollution source through the three-dimensional features based on the matrix. According to the method and the device, an intelligent decision-making system based on machine learning can be constructed by integrating multi-source heterogeneous data under a complex environment background, automatic, standardized and precise traceability of new pollutants is realized, and the traceability efficiency and accuracy are improved.

The invention provides a crop monitoring system and method based on multispectral remote sensing and deep learning, and the system comprises a data preprocessing module which is used for carrying out the data preprocessing of a multispectral remote sensing image, and generating a standard reflectivity data set; the feature extraction module is used for extracting a high-dimensional spectral feature vector from the standard reflectivity data set through a multi-scale convolutional neural network; the time sequence dynamic analysis module is used for performing time sequence correlation analysis on the high-dimensional spectral feature vector through a long short-term memory network to generate a weighted time sequence feature vector; the physiological parameter quantification module is used for mapping the weighted time sequence feature vectors into quantitative indexes of crop physiological parameters; and the monitoring result generation module is used for performing dynamic deduction according to the quantitative index and generating dynamic trend prediction data of the crop growth state. The system can dynamically sense the growth stage characteristics of crops and adaptively adjust the spectral feature extraction strategy, thereby improving the crop monitoring precision in a complex agricultural environment.

The invention provides a network security protection method and system based on information fusion, and the method comprises the steps: firstly obtaining a multi-source heterogeneous data set, which comprises a traffic interaction data unit, an equipment log data unit and a protocol analysis data unit, of a target network, then carrying out the time sequence correlation analysis of the traffic interaction data unit, and generating a traffic behavior feature set; and executing state mode analysis on the equipment log data unit to generate an equipment operation feature set, and executing semantic recognition on the protocol analysis data unit to generate a protocol analysis feature set. Then, on the basis of a multi-dimensional feature fusion rule, cross-dimensional feature fusion processing is carried out on the feature set, and a network situation feature set is generated; and calling a threat identification model to carry out threat identification on the set, generating a threat identification result set containing threat type identifiers and influence range parameters, and finally generating a security response strategy set according to the threat identification result and issuing the security response strategy set to a security control node to execute protection operation, thereby effectively improving the network security protection capability.

The invention relates to the field of monitoring and early warning, in particular to a dynamic beam radar monitoring and linkage early warning method and system for a layered side slope of an expressway, and the method comprises the steps: carrying out the layered scanning of a layered geologic structure of the side slope, synchronously obtaining a phase coherent echo signal, carrying out the multi-threshold scattering point extraction, and carrying out the multi-threshold scattering point extraction; through analysis of a prior constraint model and a geological vegetation recognition network, interference signals caused by vehicle passing are eliminated to obtain a space-time coherent scattering point set, the space-time coherent scattering point set is input to a deformation calculation assembly line, differential interference measurement, adaptive atmospheric disturbance correction and slope structure parameter inversion are executed, and a displacement data stream is generated. And carrying out space-time correlation analysis on the layered inclination angle time sequence data and the meteorological and hydrological data, updating a slope stability evaluation index through a dynamic baseline, and generating an early warning instruction. According to the invention, a technical system from interference suppression and precise calculation to decision closed loop is formed, high reliability of road slope monitoring results is ensured, and intelligent support is provided for traffic safety and emergency management and control.

The invention provides a humanoid robot inertial navigation and vision fusion positioning method, device and equipment. The method comprises the following steps: firstly, pre-evaluating the quality of IMU inertial data and visual SLAM data to generate a data credibility identifier; establishing a causal relationship chain between the data sources through time sequence correlation analysis; constructing a multi-strategy superposition state based on data credibility, and determining an adaptive fusion strategy through collapse judgment; constructing a data evolution path by using a causal relationship, and generating an expected behavior feature and a causal deviation feature; the fusion parameters are optimized according to the deviation characteristics, and a plurality of fusion positioning estimations with different weights are generated; and through space aggregation analysis and confidence weight calculation, selecting optimal positioning estimation and outputting a high-precision fusion positioning result. According to the method, deep understanding of sensor data, intelligent selection of fusion strategies and robust output of positioning results are realized, and the positioning performance of the humanoid robot in a complex environment is improved.

The invention discloses a cloud network fusion data processing method and system, and particularly relates to the technical field of data processing. The method comprises the following steps: constructing a link semantic dynamic graph based on space-time correlation analysis by extracting dynamic semantic parameters of transmission links in a heterogeneous network; and meanwhile, performing multi-dimensional semantic analysis on the cloud data stream to generate a data stream semantic fingerprint. The method comprises the following steps: identifying a semantic conflict type through matching degree analysis, reversely analyzing a node switching sequence and bandwidth fluctuation characteristics of a transmission path for a fragmentation strategy mismatch scene, evaluating the matching degree of a link and data processing logic, and generating a semantic compatibility matrix based on a fragment recombination rule and aggregated context characteristics; and the matching degree and the compatibility matrix are input into a dynamic compensation model, a fragment recombination strategy and a priority remapping rule are generated, whether real-time reconstruction of a data processing engine is triggered or not is judged, dynamic adaptation of network transmission semantics and cloud computing logic is achieved, and the real-time performance of data processing and the resource scheduling efficiency in the heterogeneous environment are improved.

The invention relates to the technical field of image processing, and discloses a smart campus security monitoring method and system based on visual understanding. The method comprises the following steps: carrying out adaptive semantic segmentation on a campus scene image through priori knowledge of a campus functional region to obtain a campus functional region segmentation map; performing multi-dimensional identity feature extraction on the campus personnel according to the segmentation map to obtain a personnel identity detection result; the identity detection result is input into a campus behavior semantic perception network for abnormal behavior recognition, and an abnormal behavior recognition result is obtained; and according to the segmentation map, the identity detection result and the abnormal behavior identification result, performing comprehensive evaluation through a campus scene context sensing fusion algorithm to obtain a campus security monitoring decision result. The abnormal behavior detection method and device solve the technical problem that abnormal behavior detection in an existing campus security monitoring technology lacks spatial semantic correction and time sequence correlation analysis.

The invention relates to the technical field of learning optimization, in particular to a big data analysis-oriented deep reinforcement learning optimization algorithm, which comprises the steps of dynamic feature topological graph generation: performing time sequence correlation analysis on a big data stream input in real time to generate a dynamic feature topological graph; an enhanced state space is reconstructed, wherein the enhanced state space with space-time correlation characteristics is reconstructed through potential path mining; hierarchical decoupling training: asynchronous parameter updating is carried out by adopting a gradient isolation mechanism, and a strategy gradient flow and a value gradient flow are generated; multi-dimensional decision fusion: generating a multi-objective optimization decision through dynamic weight fusion; and feedback driving adjustment: according to the actual execution effect of the multi-objective optimization decision, reversely adjusting the construction threshold of the dynamic feature topological graph and the dimension parameter of the enhanced state space. According to the method, by constructing the dynamic feature topological graph, the time sequence dependency relationship in the big data flow can be captured in real time, the dynamic evolution process between the features is effectively reflected, and the flexibility and precision of feature modeling are remarkably improved.

The invention discloses an AI-based urban traffic flow prediction and dynamic signal optimization method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining real-time traffic data, historical flow data and external environment data of a target region, obtaining multi-source data, and constructing a space-time matrix corresponding to the target region according to the multi-source data; learning the space-time matrix by using a preset space-time fusion model, and outputting a prediction result of the traffic flow in a future preset time period through the space-time fusion model obtained by learning; wherein the space-time fusion model is a combined architecture of a graph convolutional network and a Transform network; and dynamically adjusting traffic signal control parameters in the target area through a multi-agent learning algorithm based on a prediction result. According to the method, closed-loop regulation and control of'data perception-prediction modeling-autonomous decision 'are formed, so that the limitation of spatial feature modeling staticization and time correlation analysis fragmentation of a traditional method is broken through, and the problem of regional imbalance caused by single-point optimization is effectively solved.

The invention relates to the technical field of medical image information processing, and discloses a medical image information management system based on wisdom medical treatment, which comprises a scene perception preprocessing module, a cross-modal attention fusion module, a weak supervision annotation module, a comparative analysis module, a collaborative report generation module, a knowledge graph reasoning decision module and a consultation collaborative platform module. The image quality is improved through scene adaptive preprocessing; a hierarchical cross-modal attention mechanism is adopted to realize multi-modal feature alignment and fusion; lesion labeling is carried out based on a cascade weak supervised learning strategy; obtaining a focus evolution trend through time sequence correlation analysis; generating a standardized diagnosis report by using a visual language collaborative network; providing treatment scheme recommendation based on individualized knowledge graph reasoning; and a multidisciplinary consultation cooperation platform is constructed. According to the method, the scene adaptability of image quality evaluation is improved, the multi-modal semantic alignment capability is enhanced, the dependence of focus labeling on a fine sample is reduced, and dynamic disease monitoring and individualized diagnosis and treatment decision support are realized.

The invention discloses an event-visible light combined video enhancement method for cross-modal spatial-temporal correlation analysis, and the method comprises the steps: 1), generating a corresponding dynamic event stream frame through an event camera simulation model, and dividing a data set; (2) a Feature Extractor module based on UNet is constructed, and feature extraction is carried out; 3) constructing a cross-modal reconstruction unit based on BCPS, and recovering contour information of the visible light video frame; 4) constructing a space domain noise filtering network VSNF and a time domain noise filtering network VTNF, and performing feature analysis respectively; 5) constructing a UNet-based feature fusion module, and generating a denoising result conforming to human visual perception characteristics; and 6) designing a loss function, and achieving an optimal balance state through multiple times of loop training. The method belongs to the technical field of image processing, and solves the problems of incomplete dynamic feature extraction, insufficient time domain information utilization and low overall visual quality of a video in a denoising process in the prior art.

The invention relates to the technical field of water quality monitoring, and discloses a water quality monitoring method and system based on edge calculation. According to the method, a water quality sensor array is deployed at an edge node, multi-dimensional water quality parameters of a target water area are collected in real time, and an original water quality data flow is generated. Performing space-time correlation analysis on the original data stream, identifying the change trend of the water quality parameters in time and space dimensions, and generating a preprocessed data set with a timestamp and a position mark; a dynamic water quality evaluation model is constructed based on the data set, and model parameters are updated in real time by utilizing the computing capability of edge computing nodes. Inputting water quality parameters collected in real time into the model, detecting abnormal water quality fluctuation and generating an abnormal water quality event report. And triggering a traceability analysis process according to the report, and determining a potential source of the abnormal water quality event in combination with historical water quality data and environmental factor data. The method is high in real-time performance, efficient in data processing, capable of accurately tracing, capable of effectively improving the water quality monitoring level and capable of guaranteeing the safety of water resources.

The invention relates to the technical field of traffic management, in particular to a traffic video event detection method and system based on space-time correlation analysis, and the method comprises the steps: obtaining traffic video data, traffic text description data and traffic equipment state data, carrying out the preprocessing of the data, and unifying the format; extracting space and time characteristics of the video frame sequence, calculating a target space-time association weight by means of a space-time attention mechanism, and establishing a space-time association model; identifying a traffic event according to a model rule to obtain a preliminary result; the preliminary detection result is verified and supplemented through text association matching and image feature matching in combination with user query, a final event detection result is obtained, and the space-time association model is updated; and presenting a final event detection result to a user in a visual form. According to the method, multi-modal traffic data can be fused, the traffic incident detection precision in a complex scene is improved, a model can be optimized in combination with user requirements, a result is visually presented, and efficient traffic management is assisted.

The invention relates to the technical field of induced draft fan bearing seats, and discloses an induced draft fan high-temperature-resistant bearing seat structure and thermal state monitoring algorithm integrated system which comprises a bearing seat body. Four corner areas of the bearing seat body are respectively provided with a fixing hole used for being fixed with a base, and the center of the bearing seat body is provided with a circular bearing mounting hole; according to the induced draft fan high-temperature-resistant bearing seat structure and thermal state monitoring algorithm integrated system, the annular liquid storage cavity structure provides an optimal measurement point for the temperature sensor while actively dissipating heat, high unification of the heat dissipation efficiency and accurate monitoring is achieved, and secondly, through time sequence correlation analysis of temperature and vibration signals, a multi-parameter fusion diagnosis algorithm is used for diagnosing the heat dissipation efficiency of the bearing seat structure and the thermal state monitoring algorithm. In addition, localization processing is combined with the self-adaptive learning function, the anti-interference performance and long-term stability of the system are enhanced, and meanwhile the maintenance process is simplified through remote parameter configuration.

The invention provides an intelligent behavior identification and analysis method and system. The method comprises the following steps: segmenting preprocessed video stream data by using a semantic segmentation technology to obtain animal monomers, and respectively extracting spatial-temporal characteristics and overall dynamic characteristics of an animal group; inputting the spatio-temporal characteristics and the breeding environment semantic information into a monomer behavior semantic recognition model to obtain monomer behavior semantic characteristics, performing spatio-temporal correlation analysis on each monomer behavior semantic characteristic to calculate behavior interaction strength among different animal monomers, and constructing a group behavior correlation matrix according to each behavior interaction strength; and inputting the overall dynamic characteristics and the group behavior association matrix into a group behavior semantic recognition model to obtain a group behavior classification result, and generating prompt information. According to the method, key associated information such as group stress and ingestion competition can be effectively captured, and accurate identification and analysis of individual behaviors in a group behavior scene are realized.

The invention discloses a natural gas pipeline failure risk assessment method and system based on data driving, and relates to the technical field of pipeline safety. The method comprises the following steps: firstly, calculating and correcting the failure probability by establishing a pipeline failure probability evaluation model, extracting key features by constructing a corrosion and design five types of failure factor index systems and principal component analysis, and calculating the failure probability by utilizing an improved correction coefficient formula; secondly, establishing a consequence evaluation model based on a risk matrix, and realizing differentiated evaluation of failure consequences in different geographical environments by combining GIS data; setting an online learning mechanism, and automatically updating a risk matrix when sensor data triggers a threshold value; the problems that a traditional method is high in subjectivity, insufficient in data fusion and low in simulation precision under complex conditions are solved, the accuracy and dynamic adaptability of risk assessment are improved through machine learning and space-time correlation analysis, accurate decision support is provided for pipeline operation and maintenance, and the safety accident rate is effectively reduced.

The application discloses a ship lock concrete structure durability analysis method and system under the coupling action of multiple factors, which comprises the following steps: acquiring internal structure images of different erosion areas of a ship lock concrete test piece; identifying and quantitatively analyzing the internal structure images of different erosion areas by a deep learning image segmentation algorithm to generate an erosion structure factor set; calculating environmental characteristics corresponding to multiple environmental parameters by a multi-physical field coupling inversion algorithm; constructing a three-dimensional microstructure model of different erosion areas; dynamically simulating the three-dimensional microstructure model according to the environmental characteristics; performing spatio-temporal correlation analysis on simulation result data by a hypergraph neural network to generate a prediction result; and comparing an expert knowledge base and performing construction maintenance based on text information of the prediction result by an Euclidean distance similarity algorithm. The application introduces high-precision three-dimensional model multi-factor coupling simulation, and improves the ship lock concrete structure analysis precision through high-precision coupling prediction of the hypergraph neural network.

The present application relates to dynamic time warping and graph neural network technology, in particular to a load data space-time correlation analysis method based on space-time graph neural network, which solves the technical problem that the accuracy improvement space of the traditional wind-solar-power prediction method is limited due to single consideration factor, and discloses the content of using space-time graph neural network to analyze the space-time correlation of load data, first using the FastDTW algorithm to analyze the space-time correlation of the obtained load data, constructing a correlation matrix, and finally using the obtained data set to train multiple independent parallel space-time graph neural networks to obtain the space-time characteristics of the load data and provide high-quality basic data for prediction.

The invention discloses a substation near-electricity operation safety control method based on binocular vision detection, and relates to the technical field of vision detection, and the method comprises the steps: carrying out the multi-view geometric reprojection and virtual parallax fusion of an initial depth image, generating an enhanced depth image, carrying out the semantic segmentation of the enhanced depth image, and generating a semantic depth alignment image; re-projection deviation of the semantic depth alignment map is calculated, consistency correction is carried out according to the re-projection deviation, and a high-precision depth map is generated; and extracting a real-time spatial distance parameter of the high-precision depth map, performing risk level division and time sequence correlation analysis on the real-time spatial distance parameter by using a safety threshold, and generating an operation event record packet. According to the method, multi-view geometric reprojection and virtual parallax fusion are carried out on the initial depth map, and semantic reprojection consistency correction is synchronously carried out, so that the whole-process intelligent control closed loop of the substation near-electricity operation is realized.

This invention discloses a method and system for intelligent scheduling and optimization of hospital resources based on multi-source data fusion. The method includes: collecting multi-dimensional data across hospital areas through a multi-source medical data mining and analysis platform and establishing correlation mappings; analyzing resource supply and demand relationships using a dynamic game equilibrium model of medical resources; identifying bottlenecks and temporal coupling relationships at treatment nodes using a treatment process temporal correlation analysis algorithm; establishing a decision matrix by integrating parameters between hospital areas through a cross-hospital resource scheduling collaborative algorithm; constructing a multi-objective optimization model by combining parameters such as resource capacity and treatment timeliness; and outputting cross-hospital resource allocation schemes, treatment process adjustment paths, and dynamic scheduling instructions through a step-by-step refinement process of temporal analysis, cross-hospital scheduling decision-making, and multi-objective solution. This invention achieves precise, collaborative, and intelligent scheduling of hospital resources, improves resource utilization efficiency and the adaptability of treatment services, and is applicable to scenarios involving the overall optimization of cross-hospital medical resources.

This invention discloses a performance testing method and system for Ethernet switches, relating to the field of switch testing technology. The method includes setting up an Ethernet switch test scenario, fixing Ethernet switch parameters, using a tester to collect multi-source data transmitted by the Ethernet switch, classifying data packets into complete packets and missing / abnormal packets, further dividing the data into normal packets and abnormal packets, locating and tracing the source of abnormal data, analyzing the influencing factors of abnormal data, and comprehensively evaluating the Ethernet switch test results. This invention overcomes the limitations of traditional testing methods that rely solely on normal data for evaluation. By incorporating missing and abnormal data into the performance analysis system, it marks missing and abnormal packets through integrity verification and identifies outliers. This not only distinguishes between occasional noise and genuine anomalies but also improves the efficiency of root cause localization of abnormal data through a hierarchical progressive method and temporal correlation analysis, thereby enhancing the realism of Ethernet switch performance testing.

The application discloses an air quality high-value event identification method, device, equipment, medium and product, relates to the technical field of air quality monitoring, and comprises the following steps: acquiring multi-source heterogeneous data of a target area; performing spatio-temporal alignment processing on the multi-source heterogeneous data to obtain a multi-modal feature vector; performing dynamic threshold calculation on the multi-modal feature vector based on a meteorological correction factor to obtain preliminary event information; wherein the meteorological correction factor is determined based on the correlation between historical contemporaneous pollutant concentration data and meteorological reference data of the target area; performing spatio-temporal correlation analysis on the preliminary event information through spatial clustering to obtain event traceability results; comparing the event traceability results with historical event records to generate deduplicated event records; and performing grade division on the deduplicated event records according to preset event grading rules and generating a scheduling result. The method can accurately identify air quality high-value events.

The invention discloses an air quality high-value event identification method, device and equipment, a medium and a product, and relates to the technical field of air quality monitoring, and the method comprises the steps: obtaining multi-source heterogeneous data of a target area; performing space-time alignment processing on the multi-source heterogeneous data to obtain a multi-modal feature vector; based on a meteorological correction factor, performing dynamic threshold calculation on the multi-modal feature vector to obtain initial event information; wherein the meteorological correction factor is determined based on an association relationship between historical corresponding-period pollutant concentration data of the target area and meteorological reference data; performing space-time correlation analysis on the initial event information through spatial clustering to obtain an event traceability result; comparing the event tracing result with a historical event record to generate a duplicate removal event record; and according to a preset event grading rule, grading the de-duplication event record, and generating a scheduling result. According to the method, the air quality high-value event can be accurately identified.

This invention relates to the field of data processing technology, specifically to an artificial intelligence-based geological prospecting data processing and analysis system. The system includes: a geological parameter module, a spatial strain module, a causal analysis module, a resonance response module, and an optimized prospecting module. In this invention, spatiotemporal unified expression is achieved through the acquisition and registration of multidimensional geological parameters, eliminating observational biases and improving data comparability. Noise interference is reduced and key feature expression is enhanced through feature dimensionality reduction and ore-forming parameter extraction. Parameter gradient vectorization quantifies strain distribution and reveals tension concentration areas. Directional offset and temporal correlation analysis reveal causal relationships of disturbances and avoid misjudgments. Phase synchronization and amplitude difference analysis of time-series data reflect the energy resonance response of the geological system, forming a hierarchical logic of data fusion, spatial analysis, causal reasoning, and dynamic response, thereby improving the accuracy of geological information analysis and the reliability of prospecting prediction.

The application relates to the technical field of coal mine safety monitoring, and discloses a multi-source heterogeneous ground disaster monitoring data fusion and space-time correlation analysis method and system for a coal mine power supply line, which comprises the following steps: collecting multi-source ground disaster data of the coal mine power supply line, and performing standardization processing on the multi-source ground disaster data to obtain a standardized multi-source heterogeneous data set; sequentially performing feature-level fusion and decision-level fusion on the standardized multi-source heterogeneous data set to obtain a real value domain global ground disaster feature tensor; analyzing the global ground disaster feature tensor by using a coal mine ground disaster space-time correlation analysis model constructed based on a quaternion algebra structure, outputting ground disaster prediction results of multiple regions of the coal mine power supply line, and performing ground disaster risk grading early warning based on the ground disaster prediction results, wherein the ground disaster prediction results comprise a ground disaster occurrence probability and a ground surface deformation amount prediction result. The accuracy of ground disaster identification and the advance nature of early warning of the coal mine power supply line are significantly improved.

The present application discloses a method for weather radar bird flock identification and target difference analysis, which relates to the field of radar signal processing technology. The method includes preprocessing the weather radar raw data to obtain preprocessed weather radar data; screening the preprocessed weather radar data based on a first dynamic threshold method and a fuzzy logic classification method to obtain an initial bird flock target unit; screening the initial bird flock target unit based on spatiotemporal correlation analysis operation constraints to obtain a bird flock target; generating a bird flock activity distribution map based on the initial bird flock target unit and the bird flock target. Based on the bird flock activity distribution map, multidimensional feature extraction is performed on the preprocessed weather radar data to construct a multidimensional feature matrix of the bird flock target; principal component analysis, Fisher linear discriminant method, and clustering algorithm are used to calculate the multidimensional feature matrix of the bird flock and the weather target to obtain a target separability verification result; the present application realizes the accurate identification of bird flock targets.

The application discloses a forestry disease and pest control method based on big data mining, which comprises the following steps: collecting and preprocessing multi-source heterogeneous data of a target forest area; extracting multi-source features and performing fusion processing according to the correlation of ecological factors; performing spatio-temporal correlation analysis and constraint fusion processing based on the multi-source fusion feature set; performing spatio-temporal prediction modeling and result analysis on the comprehensive representation sequence of diseases and pests based on an improved AGCRN model, and generating disease and pest prediction results; performing risk zoning and control decision determination processing on different monitoring areas in the target forest area; constructing a control decision mapping relationship and generating a control strategy set; constructing a strategy evaluation index and performing comprehensive evaluation and sorting optimization to determine the target control strategy. The application adopts a multi-source data fusion and spatio-temporal correlation modeling method, realizes accurate prediction of diseases and pests and optimization of control strategies, and has the advantages of high prediction accuracy, strong decision scientificity and high resource utilization efficiency.

The present application relates to the field of image processing technology, and discloses a smart campus security monitoring method and system based on visual understanding. The method includes: performing adaptive semantic segmentation on campus scene images through prior knowledge of campus functional areas to obtain a campus functional area segmentation map; extracting multi-dimensional identity features of campus personnel based on the segmentation map to obtain personnel identity detection results; inputting the identity detection results into the campus behavior semantic perception network to perform abnormal behavior recognition to obtain abnormal behavior recognition results; performing a comprehensive evaluation based on the segmentation map, identity detection results and abnormal behavior recognition results through the campus scene context perception fusion algorithm to obtain campus security monitoring decision results. The present application solves the technical problem that abnormal behavior detection in existing campus security monitoring technologies lacks spatial semantic correction and temporal correlation analysis.

This invention provides a method and system for querying the operation logs of substation lock control terminals. It collects raw operation log streams generated by substation lock control terminals within a preset time period, serializes them to obtain a log data sequence, and then uses context association analysis to identify entity interaction relationships and behavioral attribute descriptions in the log text. After standardization and integration, a structured log metadata set is obtained. A spatial topology index is constructed based on the physical layout and electrical connection relationships of substation equipment, mapping the equipment association information to the corresponding node positions in the spatial topology index to generate a spatially enhanced log data set. Natural language query requests are received, a structured query expression is generated, and multi-dimensional retrieval and matching are performed on the spatially enhanced log data set to output a preliminary query result set. Temporal correlation analysis and operational impact assessment are performed on the log records to generate a query report. This invention enables efficient and accurate querying of substation lock control terminal operation logs.

The invention belongs to the technical field of carbon emission monitoring, and particularly discloses a campus carbon emission monitoring method and system based on multi-source data fusion, and the method comprises the steps: carrying out the division of a monitoring region and the arrangement of a monitoring network based on a campus emission source list; the carbon dioxide concentration is monitored in real time through a monitoring network, and carbon emission abnormity analysis is carried out in combination with a dynamic reference range established by historical data; based on the sensor network in the abnormal area, the real-time wind speed and the wind direction, identifying an abnormal main emission source through space-time correlation analysis and determining the abnormal degree of the abnormal main emission source; and generating a monitoring report and feeding back. According to the invention, through differentiated monitoring network arrangement, dynamic reference evaluation and multi-dimensional abnormal traceability, accurate monitoring of campus carbon emission and rapid positioning of an abnormal emission source are realized, and the problems of monitoring blind areas, difficult traceability and insufficient management pertinence in the prior art are effectively solved. And the representativeness and the accuracy of a monitoring result are improved.