353 results about "Weight analysis" patented technology

The invention discloses an industrial odor online monitoring system based on big data. The system comprises a data acquisition module, a preprocessing module, a feature fusion module, a multi-source sensing weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module. Data interaction among the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a timestamp is established in a distributed database. Through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and a credible data governance architecture, a new industrial bad smell monitoring normal form with environment intelligent adaptability is constructed. The four technical effects form complementary support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

The invention discloses a power equipment intelligent early warning and maintenance management system based on an AI Agent, and relates to the technical field of power equipment maintenance management, and the system comprises a data preprocessing and weight correction module, a feature extraction and denoising module, a dynamic prediction and fault identification module, a time sequence analysis and intelligent early warning module, and a fault simulation and maintenance optimization module. And the data preprocessing and weight correction module is used for preprocessing the operation data of the power equipment and carrying out weighted analysis on the data of the multi-source sensor by adopting a self-adaptive weight distribution algorithm. According to the method, the hidden deviation problem of the sensor is solved through the multi-source data correction and dynamic weight distribution technology, and key features are accurately extracted and noise is eliminated in combination with a deep learning feature enhancement and denoising method. And a dynamic prediction model and an intelligent early warning generation method are utilized to realize accurate fault prediction and graded early warning, a maintenance strategy is optimized, and the reliability and safety of a power system are improved.

The invention discloses an agricultural whole industrial chain traceability method and system based on big data, particularly relates to the technical field of agricultural Internet of Things and intelligent decision making, and is used for solving the problems of cause and effect chain breakage, damage-causing factor positioning deviation and insufficient traceability reliability caused by neglecting irreversibility of biological growth in the existing agricultural traceability system. The method comprises the following steps: dividing time sequence data fragments based on an agricultural growth stage, restoring a time sequence rule of crop growth, and dynamically marking a time window of a key abnormal event so as to lock a damage-causing action interval; quality inspection and transportation parameters are mapped into a system trajectory by combining a phase-space reconstruction technology, and a target growth stage associated with a biological dynamic rule is screened; through contribution weight analysis and stress resistance data fusion, identifying a main cause damage factor and a synergistic effect relationship thereof; and finally, the tracing path is dynamically corrected based on a historical verification result, and the threshold value and the weight of the agricultural rule base are optimized, so that the accuracy and the credibility of agricultural whole industry chain tracing are remarkably improved.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention discloses a risk assessment method, system and terminal based on a gas pipeline, and the method comprises the steps: obtaining the sample monitoring data of a plurality of gas monitoring devices attached to the gas pipeline, and building a risk basic model of the gas pipeline according to the sample monitoring data; performing index weight distribution on the risk basic model by using a weight analysis method to obtain a target risk basic model, and training the target risk basic model according to the sample monitoring data to obtain a risk assessment model; and acquiring current sample monitoring data, inputting the current sample monitoring data into the risk assessment model, and outputting a gas risk assessment result of the gas pipeline. According to the method, the risk model is established through the monitoring data, the weight analysis and training are performed, the gas pipeline risk is finally evaluated, and the potential risk of the gas pipeline can be effectively identified and pre-warned by establishing the risk evaluation model, so that the safety management level is improved, and the accident occurrence probability is reduced.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention relates to a low-voltage power distribution network fragile area identification method. The method comprises the steps of obtaining related power data of a low-voltage power distribution network; in combination with a complex network theory, constructing a node importance evaluation index model reflecting the structural vulnerability of the power distribution network, and performing calculation; constructing a state vulnerability evaluation index model reflecting the operation state of the power distribution network and calculating; performing weighting by using a weight analysis method, and performing weighting on the node importance evaluation index and the state vulnerability evaluation index to obtain subjective and objective weights of each index; coordinating subjective and objective weights according to Nash equilibrium of the game theory, and calculating a comprehensive weight; comprehensive vulnerability is calculated by using a TOPSIS algorithm improved by grey relational degree, and vulnerability grades are divided according to Euclidean distance; and constructing a routine and fault determination process, distinguishing routine vulnerability assessment and emergency vulnerability assessment, and outputting node, line and region vulnerability levels. The method can improve the accuracy of low-voltage power distribution network fragile area discrimination, thereby improving the reliability and safety of the power distribution network.

The present invention belongs to the field of environmental meteorological monitoring and relates to data analysis technology, which is used to solve the problem that the prior art cannot combine multi-source data for comprehensive collection and analysis, resulting in the inability to accurately evaluate the overall state of environmental meteorology. Specifically, it is an environmental meteorological intelligent monitoring system based on multi-source data collection and analysis, including a data collection module, a data analysis module, a monitoring and analysis module, a necessary analysis module, an optimization analysis module and a database; the data collection module is used to collect environmental meteorological data; the data analysis module is used to perform weight analysis on environmental meteorological data; the present invention can monitor and analyze environmental meteorological data, combine the monitored values ​​of environmental meteorological parameters and weight coefficients for comprehensive analysis and calculation to obtain an evaluation coefficient, evaluate the overall environmental meteorological state within the monitoring period through the evaluation coefficient, and provide timely feedback and early warning when the overall environmental meteorological state is abnormal.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment effluent quality real-time detection method and system.The method is applied to a sewage treatment system.The method comprises the steps that a discharge path of the sewage treatment system is divided into at least two effluent monitoring nodes, presetting a weight rule base of water quality characteristic parameters for each effluent monitoring node; collecting a water quality characteristic parameter set of each effluent monitoring node, an operation state data set of the sewage treatment system and an environment parameter data set in real time; determining the current operation condition of the sewage treatment system according to the operation state data, and matching corresponding parameter weight configuration in a weight rule base; performing weight analysis on the water quality characteristic parameter data and the parameter weight configuration to generate a water quality comprehensive abnormal index corresponding to the effluent monitoring node; the abnormal process position of sewage treatment can be accurately traced, and the accuracy of abnormal positioning is improved.

The invention discloses a financial anomaly detection and risk early warning system based on Bayesian deep learning, and relates to the technical field of deep learning, and the system comprises an information contribution weight analysis unit which is used for selecting financial indexes used for measuring financial robustness from historical data of a rolling window formed by a plurality of recent continuous report periods, and outputting the selected financial indexes to a database; for each financial index, normalizing the value of the mutual information into a weight; the pressure score construction unit is used for estimating the probability of occurrence of abnormity at the current period by using a logistic regression model which introduces nonlinear logic Student-t likelihood in the same rolling window; the risk early warning unit is used for calculating potential loss of monetization; and if the monetization potential loss exceeds a set loss threshold value, the system sends out early warning information. According to the method, accurate modeling and uncertainty quantification of the abnormal probability are realized, default opening and loss rate parameters are introduced, and the risk prediction result is monetized into potential loss for dynamic early warning triggering.

The invention relates to the field of computer industry, and discloses an integrated computer software and hardware combined sales optimization system and method, and the method comprises the steps: extracting software and hardware sales records, customer transaction behaviors and version updating tracks, and constructing a fusion data set of heterogeneous sales data; performing association weight analysis on the fused data set, and extracting strategy sensitive points in software and hardware linkage transaction in combination with a customer behavior intention model and product life cycle characteristics; constructing a product combination feasible region in a specific sales window by adopting a combination optimization graph search algorithm based on the tag nested structure; mapping the candidate combined product list to a historical sales behavior map, and extracting dynamic feedback modes of a combined strategy under different sales paths by introducing a price elastic response model and market disturbance fluctuation characteristics; and carrying out iterative adjustment and node intervention on the strategy path based on the high-sensitivity area. The method has the advantage of improving the scientificity of sales strategy formulation.

The invention discloses a cable insulation aging state evaluation method, which realizes aging state evaluation of a running cable system by fusing data acquired by multiple sensors and combining an intelligent algorithm. The method comprises the following steps: 1, collecting cable operation data of different years, and constructing a preliminary data set; 2, generating simulation data to supplement the insufficiency of actually acquired data, and constructing a complete data set; 3, performing feature extraction on the signals in the complete data set; 4, carrying out weight analysis on the extracted features; 5, arranging a sensor, and collecting multi-source data in real time; 6, an alarm threshold value is set, early warning is carried out on the cable section with the aging score exceeding the threshold value, and the remaining life is evaluated; 7, synchronizing the early warning information to a maintenance management platform, and generating a detailed early warning report; and 8, checking an alarmed section, evaluating the alarm accuracy, and if the alarm is unreasonable, correcting the weight coefficient and the scoring criterion in time. By fusing data acquired by multiple sensors and combining an intelligent algorithm, the aging state of the operating system is evaluated.

The invention relates to the technical field of multi-dimensional auxiliary evaluation, in particular to a depression patient pleasant sensation loss auxiliary evaluation system using multimode physiological parameters. The system comprises a data acquisition module for acquiring and quantifying pleasant sensation missing evaluation values under different dimensions; the basic weight analysis module analyzes data distribution conditions of multiple patients under dimensions to obtain basic weights; the low-characterization analysis module is used for obtaining a low-characterization weight through continuous changes of multiple pleasant sensation missing evaluation values and correlation between a low-characterization dimension and a high-characterization dimension; the high-characterization analysis module is used for clustering the patients through the low-characterization weights and analyzing the deviation of the pleasant sensation missing evaluation values of the high-characterization dimensions among the clustered patients to obtain high-characterization weights; and the evaluation module performs evaluation in combination with the basic weight, the low characterization weight and the high characterization weight. According to the method, different dimensions are distinguished through a data acquisition mode to carry out data credibility analysis, a more appropriate weight of each dimension is comprehensively obtained, and the accuracy of a multi-dimensional evaluation result is improved.

The present invention relates to the technical field of ecological dynamic intelligent monitoring, and specifically to a black soil ecological environment dynamic monitoring method and system based on intelligent communication. The method comprises the following steps: setting an analysis and processing module for black soil ecological dynamic monitoring data in an intelligent communication device, and using the module to obtain a dynamic monitoring data set of the black soil ecological environment; setting monitoring demand parameters of the black soil ecological environment according to the data set, and building a multi-level evaluation framework of the black soil ecological environment in combination with the data set and the monitoring demand parameters; introducing a fuzzy comprehensive evaluation method to screen and weight analyze the evaluation framework, and obtaining the screening results and weight distribution results of the evaluation framework; establishing a numerical transformation evaluation and feedback mechanism, obtaining the score evaluation results and grade division results of the evaluation framework according to the mechanism and the data set, and combining the score evaluation results, the grade division results and the dynamic monitoring data set to realize the dynamic analysis and real-time monitoring of the black soil ecological environment.

The invention discloses a building pollution reduction and carbon reduction collaborative evaluation method based on LCA and dynamic weight analysis, and relates to the field of building pollution reduction and carbon reduction collaborative evaluation. According to the method, the influence parameters of the design, construction and operation stages of the whole life cycle of the building are systematically identified and quantified, and the cooperative and unified evaluation of pollution reduction and carbon reduction is realized through dynamic weight reconstruction analysis and a function coupling LCA model, so that the environmental performance of the building in the whole life cycle can be integrally and accurately reflected, and meanwhile, the environmental performance of the building in the whole life cycle can be accurately evaluated. According to the method, the interaction between indexes and the time sensitivity of inter-stage energy consumption and pollutant transfer and emission can be considered at the same time, the complex multi-index and multi-stage environmental influence is converted into a comprehensive synergistic effect value, and an operable environmental performance grade is formed through grading judgment.

The invention provides a wind and light equipment predictive maintenance system and method based on big data and hierarchical weight. The system comprises a data acquisition and edge processing layer, a data management and storage layer, a hierarchical weight analysis and model construction layer and a fault prediction and intelligent decision-making layer. Multi-source data are collected by deploying various sensors, and the multi-source data are transmitted to a cloud for deep cleaning and storage after being preprocessed by edge computing. The index weight of each level is determined by using an improved algorithm, and a fault prediction model is constructed by fusing a plurality of intelligent algorithms. The model combines real-time data and hierarchical weights, predicts the fault probability, type and remaining service life of equipment components, and gives an alarm when a threshold value is exceeded. Meanwhile, a multi-objective optimization model is constructed according to a fault prediction result to formulate a maintenance strategy, and the maintenance strategy is executed after digital twinborn rehearsal, so that the operation and maintenance efficiency is improved. According to the method, multi-source data fusion, accurate fault prediction and intelligent maintenance decision making are realized, the operation and maintenance cost can be effectively reduced, and the equipment reliability and the power generation efficiency are improved.

The invention provides a scheduling optimization method based on automatic detection of a low-voltage metering box, and belongs to the technical field of intelligent power grids, and the method comprises the following steps: S1, obtaining detection data, carrying out the preprocessing of the detection data, and carrying out the feature extraction based on the preprocessed detection data, and obtaining key feature data; s2, enhancing the key feature data based on a dynamic weight analysis enhancement algorithm, and optimizing the representation of data features by dynamically adjusting the weight; s3, identifying the current state of the low-voltage metering box based on an intelligent fusion scheduling algorithm, constructing an optimal distribution model, and intelligently distributing detection tasks; and S4, based on a multi-dimensional situation response scheduling algorithm, performing adaptive optimization on the distribution of the detection task to obtain the distribution of the optimal detection task. The method can ensure that skills of technicians are matched with task requirements, effectively improves the efficiency of automatic detection of the low-voltage metering box, ensures that tasks are distributed reasonably and efficiently, and reduces the cost and time waste at the same time.

The invention discloses an information management and analysis system and method based on image recognition and machine learning. The system analyzes image data into a structured text through the image recognition module, performs text semantic analysis by using the natural language processing module, calculates quantitative indexes such as sensitive word scores and abnormal indexes in combination with sensitive word weight analysis, anomaly detection, a risk conduction map and a co-occurrence relation prediction model of the machine learning module, and performs semantic analysis on the structured text. And a risk index RI and an opportunity index OI are generated through a comprehensive decision module, and decision suggestions such as careful cooperation and preferential investment are output according to a preset rule. The model can dynamically optimize weight parameters based on historical data, and multi-modal data automatic processing and risk opportunity quantitative evaluation are achieved. According to the scheme, the decision making efficiency and scientificity are improved, the method is suitable for financial risk control, enterprise compliance and other scenes, and a traceable scientific basis is provided for commercial decision making.

The invention discloses a reservoir dam safety monitoring method and system based on video monitoring, and the method comprises the steps: S100, constructing a dynamic reference monitoring grid, dividing a dam into a plurality of monitoring points, and adjusting the density of the monitoring grid according to the stress distribution of the dam; s200, collecting video streams of the dam, recording environment data at the same time, and collecting monitoring data; s300, performing physical constraint feature decoupling on the data obtained in the step S200 to obtain pure deformation features and environmental noise features; s400, performing differential consistency data fusion on the pure deformation features and the environmental noise features to obtain a fusion coefficient and a fusion deformation field; s500, performing seepage type anomaly propagation prediction in the reference grid monitoring grid to obtain a risk area set and a propagation path probability graph of the dam; s600, establishing a topology perception model, performing weight analysis on the risk areas in the risk area set, and performing key monitoring on the key areas; and S700, carrying out multi-dimensional risk assessment on the key area, and effectively capturing the deformation condition of the dam.

The invention discloses a lightweight processing method and system for PDMS three-dimensional design data, and relates to the related field of data processing, and the method comprises the steps: receiving PDMS three-dimensional design data to be processed, and carrying out the preprocessing of attribute data; extracting a three-dimensional geometric model, performing geometric fidelity weight analysis, and establishing geometric fidelity weight distribution; determining a lightweight display scene; carrying out degradation processing through a polygon degradation module, and loading a visual distortion analyzer to carry out global visual distortion constraint; after the attribute information is rendered, target lightweight PDMS three-dimensional design data is generated; and transmitting the data to a three-dimensional display terminal for calling and displaying after hierarchical storage. The technical problem that it is difficult to guarantee the model precision, details and visual quality while improving the storage and transmission efficiency in existing lightweight processing is solved, and the technical effect of effectively guaranteeing the model precision, details and visual quality while improving the storage and transmission efficiency is achieved.

The invention discloses an intelligent auxiliary method, a medium and equipment for constructional engineering title review. According to the method, key feature information of unstructured text data in a declaration material is extracted by using a natural language processing technology, image data features are analyzed by using a ResNet-50 deep learning model, and a comprehensive quantitative scoring mechanism of a construction engineering performance material is established. And integrating education background, working experience, scientific research achievements and multi-modal performance data, constructing a constructional engineering title review rule base, setting a standard score threshold based on historical declaration data, and generating a title simulation review score and a review prediction result. And finally, aiming at the model, designing a feature weight analysis layer, and automatically outputting improvement suggestions of the declaration material defects according to the simulation review score after being associated with the associated review rule base. According to the method, automatic feature extraction and quantitative evaluation of the unstructured material are realized, the problems of low manual auditing efficiency and fuzzy standard are solved, and the objectivity of title evaluation is improved through the rule base and the evaluation prediction model.

The invention relates to the technical field of artificial intelligence, in particular to a gynecological traditional Chinese medicine data processing method and system based on four diagnosis data weight analysis. The method comprises the following steps: acquiring four diagnosis data; map mapping is carried out according to the four-diagnosis data to obtain four-diagnosis map mapping data; obtaining expert experience data and historical case data according to the four-diagnosis map mapping data; performing decision tree construction according to the expert experience data to obtain an expert experience judgment tree model; performing hierarchical weighted regression according to the historical case data to obtain historical case hierarchical weighted data; performing four-diagnosis feature weighting processing according to the expert experience judgment tree model and the historical case level weighting data to obtain four-diagnosis data weight analysis data. According to the method, accurate butt joint of four diagnosis features and a traditional Chinese medicine knowledge system is realized, and a complete link from four diagnosis original feature collection, semantic mapping and rule data collaborative modeling to context adaptive reasoning is constructed.

The invention relates to a large silo coal blending analysis system, and belongs to the technical field of coking. The system comprises a silo coal resource access statistics module, a switching analysis module, a coal blending ratio difference analysis module, a coal blending and coke quality prediction module and an information display module. Firstly, the silo coal seam distribution condition is established, then the coal seam resource switching condition is analyzed and judged according to the discharging weight, the accumulated coal blending execution deviation rate is calculated, and the coal blending amount and the switching condition of the next period are estimated; the blended coal and coke quality prediction module predicts the quality and analyzes the change condition according to the actual coal blending ratio and the like; and the information display module stores and visually displays the result. The system has strong analysis and prediction capabilities, and can comprehensively and deeply analyze yesterday coal blending conditions, scientifically predict coke quality, intelligently plan today coal blending by means of accumulated coal blending execution deviation rates, simply, conveniently and quickly grasp dynamics and trends, guide coal blending and quality control, improve product quality, reduce cost and enhance competitiveness.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention relates to the technical field of anti-seismic performance evaluation of ECC jacketed combination columns, and particularly provides a quantitative evaluation method for anti-seismic performance indexes of an ECC jacketed combination column, which comprises the following steps of: calculating key anti-seismic performance indexes such as bearing capacity, accumulated energy dissipation, ductility coefficient and limit value of repairable interval, and combining weight analysis and importance coefficient adjustment to evaluate the anti-seismic performance indexes of the ECC jacketed combination column. A scientific and comprehensive anti-seismic performance evaluation system is established; a method for determining the damage point of the ECC jacket combination column is provided, and the damage state is accurately determined; reparability interval evaluation is particularly introduced, repair grades are divided based on residual deformation and Park-Ang damage indexes, and a basis is provided for post-earthquake maintenance decision making; the provided anti-seismic performance index evaluation index comprehensively considers different loading working conditions, and a clear engineering applicable threshold value is set, so that engineering personnel can quickly evaluate and optimally design; the method is of great significance to promotion of engineering application of the ECC jacket combination column.

The invention discloses a shale oil carbon dioxide huff and puff graded pressure drop mining experiment method. The method comprises the following steps that S1, the crude oil recovery degrees under different huff and puff experiment requirements are obtained through experiments; the method is suitable for the technical field of oil and gas field development, the CO2 utilization rate and the recovery percentage of each stage in the shale oil carbon dioxide huff and puff graded pressure drop exploitation process can be accurately measured, a database is established in combination with influence factors of different injection conditions, and the reliability of the shale oil carbon dioxide huff and puff graded pressure drop exploitation process is improved. And on the basis of experimental results, weight analysis of shale oil carbon dioxide huff and puff graded pressure drop mining is carried out on each factor, so that main influence factors of shale oil carbon dioxide huff and puff graded pressure drop mining can be screened out, which is of great significance to subsequent shale oil carbon dioxide huff and puff mining adjustment.

The invention discloses a data real-time analysis and fault early warning method and system based on a digital electric port box transformer substation, and belongs to the technical field of power equipment monitoring and control systems. The method comprises the following steps: acquiring environmental parameters of each component of the box transformer substation in real time through a monitoring network, and performing data smooth denoising to obtain a parameter sequence; constructing a dynamic storage framework based on the sequence in combination with space-time attributes; extracting historical records of the target component to perform multi-dimensional sorting and change rate calculation, and identifying potential anomalies; when the change rate exceeds the limit and the distribution boundary breaks through, generating an alarm sequence and determining a preliminary abnormal mark; comparing the mark with the historical record, fusing the influence weight analysis verification accuracy, and obtaining a final early warning grading result; and parameter summary information is pushed to a control system according to a grading result, response feedback is obtained, and closed-loop control of the operation state is formed. According to the invention, the problem of control lag caused by data noise and delay is solved, and the timeliness, reliability and automation level of box transformer substation control are improved.

The invention provides a data grading method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data, the technical field of artificial intelligence and the field of financial science and technology. The method comprises the following steps: acquiring to-be-graded data, and processing the to-be-graded data to generate a standard data stream; extracting multi-modal features based on the standard data stream, and fusing the multi-modal features to obtain semantic representation information; acquiring real-time risk information, and performing dynamic risk weight analysis on the real-time risk information to generate risk weight information; and performing classification and grading decision based on the semantic representation information and the risk weight information to obtain a data grading result, and performing data grading on the to-be-graded data based on the data grading result.

The invention relates to the technical field of vegetation remote sensing observation, in particular to a chlorophyll fluorescence observation system based on laser radar, which comprises a target evaluation module used for responding to an observation evaluation condition to determine an observation analysis strategy of each observation target; the observation analysis module is used for determining the dominant direction of the mirror surface of the observation target and determining an inversion execution strategy of the observation target in response to the inversion execution condition; the dominant inversion module is used for determining a pre-estimation execution wave band set and determining observation pre-estimation data based on the polarization reflection parameters of the pre-estimation execution wave bands; the multi-parameter inversion module is used for determining an inversion weight coefficient of each observation wave band and determining whether to adjust the inversion weight index of each observation wave band in response to a weight analysis condition; the auxiliary analysis module is used for responding to the auxiliary analysis condition to determine the auxiliary analysis mode of the observation target, and the reliability of the fluorescence observation result of the observation target is improved.