463 results about "Entropy weight method" patented technology

The invention provides a pressure-bearing equipment damage mode identification and risk assessment method and system, and relates to the technical field of safety engineering, and the method comprises the steps: collecting multi-source sensor data and image data, inputting the data into a deep neural network after preprocessing and feature extraction, extracting spatial features through a convolutional layer, and extracting time sequence features through a recurrent neural network. And using the attention mechanism to fuse the features to identify an injury pattern. And then, constructing a multi-level evaluation index system, performing combined weighting by adopting an analytic hierarchy process and an entropy weight method, inputting weights into an improved Bayesian network model based on a D-S evidence theory, dynamically updating a conditional probability table by the model by utilizing a deep neural network and a fuzzy inference rule, and finally obtaining a risk evaluation result. According to the invention, the damage mode of the pressure-bearing equipment can be effectively identified, risk assessment is carried out, and assessment precision and reliability are improved.

The invention discloses an ecological environment health intelligent assessment and management system based on PSR and entropy weight dynamic self-adaption, and belongs to the technical field of ecological environment monitoring and intelligent management. The system comprises a data acquisition module, a preprocessing module, a classification and dynamic empowerment module, an intelligent prediction module, a feedback optimization module and a data security module. The system uses a sensor and edge calculation to acquire and process data, and the EQR is calculated through preprocessing. The core lies in that based on PSR model classification indexes, index weights are determined by applying an entropy weight method in combination with a dynamic adjustment mechanism, and pressure, state and response factors are generated. The intelligent prediction module adopts a deep learning model, outputs a health comprehensive index (Jzzs) based on factors, and performs adaptive optimization. And the feedback optimization module compares the Jzzs with a dynamic threshold value, and generates a graded, quantized and optimized treatment scheme in combination with machine learning to realize closed-loop management. And the data security module ensures that the data cannot be tampered and can be traced by using a block chain. According to the invention, dynamic accurate evaluation and intelligent adaptive management of ecological environment health are realized.

The invention provides a multi-heat-source multi-material additive manufacturing optimization method and system based on machine learning, and relates to the field of additive manufacturing, and the method comprises the following steps: collecting process parameters, material physical properties and target response data in real time, and constructing a multi-source data set; on the basis, a multi-task learning model is trained, common features are extracted, target responses are synchronously predicted, a proxy target function is constructed through predicted values, if errors exceed a threshold value, experimental verification is conducted, and new data is fed back to update the model. And applying the weight coefficient to a multi-objective evolutionary algorithm, iteratively screening a Pareto candidate solution set, verifying a high-uncertainty solution, and updating the model to error convergence. And selecting and deploying an optimal parameter combination by using an entropy weight-TOPSIS method, and embedding an online learning mechanism to form closed-loop optimization. According to the method, multi-task learning and a self-adaptive evolutionary algorithm are combined, and efficient optimization of multi-heat-source multi-material additive manufacturing process parameters can be achieved.

The invention relates to the technical field of data security, in particular to a data asset classification and grading and authority dynamic management system, which comprises an asset parameter acquisition module, a classification feature judgment module, a grading weight calculation module and an authority dynamic adaptation module. According to the method, metadata and path parameters are automatically collected through a distributed crawler in combination with a regular expression, naming parameters and content feature parameters are separated to generate a structured parameter set, field naming similarity is quantized through a Levenshtein distance, content character distribution features are verified through chi-square verification, and a naming similarity and distribution feature dual verification mechanism is constructed. According to the method, sensitivity, access frequency and data volume weight are dynamically distributed through an entropy weight method, grading parameters are generated through linear superposition, an RBAC model is combined with a Dijkstra algorithm to verify access path topology legality, unauthorized access is blocked, and the heterogeneous data classification grading and authority control dynamic adaptive capacity is improved.

The invention belongs to the technical field of finite element simulation optimization, and provides a hot upsetting process multi-target collaborative optimization method and system based on finite element simulation, and the method comprises the steps: firstly constructing a hot upsetting process multi-field coupling finite element model, and then deploying a sensor network in hot upsetting equipment to collect actual process data. Actual data and simulation data are matched through a space-time registration algorithm, then a parameter optimization model is constructed based on a dual-channel depth deterministic strategy gradient algorithm, and a technological parameter solution set is obtained through calculation; and finally, a multi-objective optimization function is defined according to a preset optimization index, and optimal hot upsetting process parameters are screened out in combination with an entropy weight method and a reference point-based non-dominated genetic algorithm. According to the method, high-precision simulation, real-time monitoring and multi-target collaborative optimization of the hot upsetting process are achieved, and the production efficiency and the product quality of the hot upsetting process are remarkably improved.

The invention discloses a scientific paper academic value measurement method based on semantic fusion and reference behavior analysis. The method comprises the following steps: determining a literature database according to a target research field, constructing a field keyword list, and retrieving and collecting an initial literature data set; the internal innovation value is quantified; quantizing an external approved value; the step of constructing the academic value comprehensive measurement model comprises the sub-steps of applying an entropy weight method to distribute index weights; calculating an academic value comprehensive score through linear weighting; checking and evaluating through a Spearman correlation coefficient; according to the method, a comprehensive evaluation system based on multi-dimensional semantics and behavior characteristics is constructed, internal knowledge contribution and external recognition value of the papers are systematically fused, objectivity and precision of scientific research paper academic value evaluation can be effectively improved, and therefore the problems of evaluation time lag, reference behavior deviation and subjective weighting existing in a traditional method are solved, and the scientific research paper academic value evaluation efficiency is improved. And the method has good universality and popularization value.

The invention relates to the technical field of safety evaluation of highway bridge girder erection machines, and discloses a safety evaluation method for the initial installation state of a highway bridge girder erection machine. Firstly, a multi-modal safety evaluation model including a structure layer, a dynamic parameter layer and a risk factor layer is established; dividing a dynamic parameter layer feature interval by using a fuzzy clustering algorithm, calculating a risk factor correlation coefficient by using a grey correlation analysis method, determining a dynamic parameter layer parameter objective weight by using an entropy weight method, and performing parameter fusion by combining the two to obtain a comprehensive weight; then multi-source sensing data installed by the bridge erecting machine are collected in real time and discretized; and finally calculating risk indexes layer by layer, and judging the safety level of the initial installation state of the bridge girder erection machine. The method comprehensively considers the structure, the dynamic parameters and the risk factors of the bridge girder erection machine, improves the evaluation accuracy by applying various algorithms, realizes the automatic judgment of the safety level, can effectively reduce the installation risk of the bridge girder erection machine, and guarantees the construction safety and the smooth engineering construction.

The invention relates to a gulongshale oil reservoir dessert evaluation method based on an improved fusion algorithm, and the method comprises the steps: constructing a dessert parameter prediction and lithofacies recognition model based on time-frequency information and an improved fusion neural network BWO-CNN-LSTM, and achieving the parameter prediction and lithofacies classification of a gulongshale oil reservoir; main control parameter selection is realized based on gulonia shale oil reservoir dessert parameters; according to different lithofacies types, calculating based on an analytic hierarchy process and an entropy weight method to obtain a comprehensive master control parameter weight; weighting the screened master control dessert parameters and the corresponding weights to obtain a comprehensive dessert value, visualizing to form a spatial distribution diagram, introducing an adaptive adjustment mechanism, constructing a gulonium shale oil reservoir dessert evaluation model based on BWO-CNN-LSTM, and evaluating the gulonium shale oil reservoir dessert. According to the method, the time-frequency information and the improved fused neural network are combined, so that the accuracy of gulongshale oil reservoir sweet spot parameter prediction and lithofacies classification is improved.

The invention discloses a hydraulic rammer tamping construction parameter adjusting method based on sensing parameter real-time feedback, and relates to the technical field of hydraulic rammer tamping construction.The hydraulic rammer tamping construction parameter adjusting method comprises the steps that a multi-mode sensing monitoring network is constructed to collect full-amount construction data, a wavelet packet decomposition algorithm is adopted for noise layered suppression, and a multi-mode sensing monitoring network is established; constructing a working condition associated data set in combination with the construction stage labels; based on the working condition associated data set, establishing a dynamic tamping effect evaluation model, and outputting a deviation index moment of time-space distribution; training a parameter adjustment intelligent model based on the deviation index matrix and a transfer learning mechanism, generating a multi-parameter collaborative adjustment strategy, and carrying out working condition adaptation degree scoring and adjustment risk early warning on strategy output; and adjusting the intelligent model based on incremental learning and model distillation technology optimization parameters. According to the method, the multi-modal sensing network, the geological dynamic quantitative model, the improved entropy weight method, the migration and reinforcement learning and the lightweight deployment technology are fused, so that full-chain intelligent dynamic optimization and safe controllable execution of hydraulic rammer construction parameters are realized.

The invention discloses a dynamic identification system and method for fault nodes of a heat pump measurement and control network, and aims to solve the problems of one-sided weight distribution, lack of directional modeling and insufficient dynamic adaptability in the prior art. The system comprises an energy transfer topology network model building module which is used for respectively quantifying the physical connection strength and the directional energy transfer path of the heat pump system by building an undirected graph and directed graph bimodal model; the multi-dimensional weight distribution module adopts a subjective and objective fusion strategy and combines a complex network theory, an entropy weight method and an analytic hierarchy process to distribute comprehensive weights for nodes and explicit modeling directivity dependence; the dynamic robustness analysis module is used for simulating and positioning structural hub nodes through static attacks, simulating and tracking cascade failure and topology reconstruction processes through dynamic attacks, and comprehensively evaluating the robustness of the system; and the fault simulation verification module verifies weight rationality and method effectiveness from four dimensions of objectivity, center matching degree, interpretability and robustness based on a fuzzy comprehensive evaluation framework. According to the method, through fusion topology modeling, dynamic weight distribution and dynamic and static combination robustness analysis, key fault nodes are accurately recognized, the system vulnerability is revealed, a data driving basis is provided for redundancy design, intelligent operation and maintenance and reliability improvement of the heat pump system, and the operation and maintenance cost is remarkably reduced.

The invention discloses a multi-source data fusion method of a power system and a real-time monitoring device, and the method employs a self-adaptive DTW to align the multi-source data of the power system, and solves the problems of time scale difference and time delay between different data sources. And then a Transform model is adopted to perform feature extraction on the aligned data, and a complex time sequence dependency relationship in the data is captured, so that the validity of the data is further enhanced. And finally, quantifying the contribution degrees of different data sources through an entropy weight method, and optimizing the weight distribution of each data source, thereby improving the overall quality of the fused data. Through the method, information from multiple data sources such as wind power can be effectively fused, so that the accuracy of state estimation of the power system and the stability of real-time monitoring are improved. Moreover, the method can improve the application effect of the multi-source data in the power system, and avoids the estimation deviation caused by the heterogeneity of the data sources.

The invention discloses a data and knowledge dual-drive wheel set multi-parameter comprehensive state evaluation method and system, and belongs to the technical field of railway vehicle maintenance. The method comprises the following steps: firstly, constructing a mapping relation between geometric parameters and dynamic performance indexes through sampling and dynamic simulation, and carrying out global sensitivity analysis to screen key dynamic performance indexes; secondly, determining subjective and objective weights of the indexes in combination with an analytic hierarchy process and an entropy weight method, and introducing a dynamic optimization model based on a Bellman equation to generate a final combined weight; then, constructing a multi-dimensional state space division model by applying adaptive kernel density estimation and fuzzy C-means clustering, and determining the probability density and membership function of each index under different health levels; and finally, performing simulation prediction on the target wheel set, inputting a predicted value into the state space model, fusing a dynamic combination weight and a D-S evidence theory, calculating a comprehensive health index, and outputting a grading result, so that the health state of the wheel set can be accurately and efficiently evaluated.

The invention discloses a detection system for natural land resources, and relates to the technical field of resource detection, the detection system comprises a data preprocessing module, a fusion space-time diagram construction module, a multi-target enhancement module and a feedback module, an entropy law is combined with a semantic space-time diagram index, quantitative modeling of an ecological law is realized, and the real-time performance of the system is improved. Natural-human collaborative intervention simulation under multiple time scales, time-space semantic query response time improvement, prediction precision improvement and interdisciplinary fusion are supported, the entropy law of ecological economics is combined with time-space diagram index, the limitation that a traditional model only pays attention to physical indexes is broken through, ecological-economic balance is achieved through an entropy weight method and Monte Carlo simulation, and the prediction precision is improved. According to the method, self-adaptive decision making is supported, a perception-decision-execution integrated platform is constructed based on the real-time updating capability of space-time diagram indexes, a full-chain solution from data collection to dynamic optimization can be provided for natural resource intelligent management, a prediction model is combined with entropy increase constraint, and the ecological economic cost is quantified.

The invention relates to the technical field of disaster prevention and reduction of a power system, and discloses an icing risk early warning method based on multi-model fusion and residual time sequence characteristic analysis, which comprises the following steps: collecting meteorological data of a line area in real time, removing abnormal values through secondary judgment of a Pauta criterion and a trend, and standardizing; adopting a TEROL algorithm to screen high-weight key features; running SWD-BP, MUL-GRNN and ELM models in parallel, constructing a dynamic weight by combining DSI, an independence weight method and an entropy weight method, and calculating a final meteorological predicted value; generating a prediction residual signal, extracting time domain features such as a mean value and a peak value, and constructing a residual feature matrix through a sliding window; and inputting an LSTM model to process a time sequence dependency relationship, and judging an icing risk level. According to the method, meteorological prediction is optimized through multi-model dynamic fusion, and deviation is analyzed and corrected in combination with residual time sequence characteristics, so that the problem of weak generalization ability of a single model is effectively solved, and the accuracy of icing risk early warning is obviously improved.

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

The invention belongs to the technical field of virtual power plants, and particularly relates to a virtual power plant resource aggregation method for a dynamic peak regulation demand of a power grid, which comprises the following steps of: acquiring multi-source data, preprocessing the multi-source data, and then verifying the data quality; aiming at different resource types including temperature control load, energy storage and charging piles, respectively constructing refined models, setting constraint conditions of the refined models, and solving a resource operation feasible region by applying multi-dimensional space mapping and linear programming; establishing a target function and a constraint condition by taking the lowest cost and the minimum energy abandoning as targets; solving a target function by using a dung beetle optimization algorithm, and screening an optimal resource aggregation scheme by using an entropy weight method; and based on the optimal resource aggregation scheme, dividing peak, valley and normal periods, constructing a four-dimensional peak regulation index, determining a weight by using an analytic hierarchy process, and screening an optimal resource combination in each period to execute scheduling. The method can guarantee the accuracy and high efficiency of the peak regulation demand response of the power grid, and assists in improving the stability of the power system and the renewable energy consumption level.

The invention discloses a regulation and control quota distribution method and system fusing subjective and objective multi-dimensional features. The method comprises the following steps: acquiring electrical load data, historical response behavior data and subjective response intention information of a user; firstly, a Ward system is used for clustering, users are clustered according to active power, then a primary clustering center is used as an initial clustering center of secondary clustering, FCM clustering is carried out, and a typical load curve of the users is described based on a secondary clustering method; load prediction is carried out based on an NARX neural network, a predicted load curve is compared with a typical load curve, and adjustable potential is calculated; constructing a DR feature data set; and the DR feature data set is fused with an entropy weight method and an analytic hierarchy process to obtain a combined weight, a fuzzy relation matrix is constructed, a user comprehensive score is quantified, and a comprehensive response potential score of the user is formed.

The invention discloses a water resource bearing capacity early warning and response decision-making method, which relates to the technical field of water resource early warning, and comprises the following steps: analyzing water resource bearing capacity, and establishing a water resource bearing capacity system; based on the DPSIRM conceptual model, establishing a dynamic model of the water resource bearing capacity early warning system, performing comprehensive weight calculation on index data of the dynamic model of the water resource bearing capacity early warning system through an analytic hierarchy process and an entropy weight method, and obtaining a comprehensive evaluation result of the water resource bearing capacity in combination with a TOPSIS evaluation model; dividing a comprehensive evaluation result of the water resource bearing capacity, and discriminating key indexes influencing the water resource bearing capacity in combination with a geographic detector model; and taking the key index as the input of a Bayesian network, obtaining an output result, and meanwhile, carrying out variable scene simulation to obtain the influence of different variables on the bearing capacity. According to the method, overload condition risks occurring in different scenes are simulated, and the probability and possibility of occurrence of water resource overload events are calculated.

The invention discloses a laser equipment fault prediction and maintenance method based on digital twinning, and the method comprises the following steps: S1, collecting structure parameters, control logic and historical data, and constructing a unified neural network digital twinning model; s2, collecting multi-source operation data in real time in an operation process, and constructing a standardized state data set; s3, mapping the data to a unified neural network digital twin model to realize state synchronization; s4, predicting a future key parameter trend by using an LSTM model; s5, a health score is calculated through an entropy weight method, and the equipment state is quantitatively evaluated; s6, triggering early warning based on the health threshold value, identifying abnormity and generating a maintenance strategy; s7, pushing the strategy to an operation and maintenance system for closed-loop execution; and S8, maintaining feedback for retraining and self-optimizing the unified neural network digital twin model. According to the method, high-fidelity digital twin modeling is realized, so that a prediction-maintenance-feedback-updating intelligent closed-loop system is formed, and the fault prediction accuracy and the maintenance response efficiency of the laser equipment are remarkably improved.

The invention provides a hot rolled strip convexity prediction method based on weight distribution strategy hybrid modeling, and relates to the technical field of metallurgical machinery and automation. The method comprises the following steps: acquiring historical production data in the rolling process of a hot-rolled strip, and establishing a convexity mechanism model of the hot-rolled strip; an improved ant colony algorithm is adopted to optimize the extreme learning machine, and a hot rolled strip convexity data driving model is constructed; calculating the information entropy of the mechanical model and the information entropy of the data driving model by adopting an entropy weight method; according to the model information entropy, calculating an initial weight coefficient of a mechanical model and an initial weight coefficient of a data-driven model, and establishing a hybrid model through a weighted summation method; and in hot-rolled strip production of different rolling units, the weight is dynamically adjusted according to the deviation value between the predicted value and the actual value of the mechanism model and the data driving model, and a final hot-rolled strip convexity prediction result is output. By adopting the method, the accuracy of convexity prediction of the hot-rolled strip can be improved.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

The invention relates to the technical field of underground space development, and provides a coastal bedrock type city underground space development geological suitability evaluation method comprising the following steps: analyzing key geological problems of land area and sea area underground space development, and extracting related geological factors; basic geological data are collected and classified, a geological suitability evaluation index system is constructed, and geological evaluation units are divided; subjective and objective weights are determined based on an analytic hierarchy process and an entropy weight method, and an optimal comprehensive weight is obtained through game theory combination weighting; modeling based on a grey correlation analysis method, and calculating and grading suitability scores of the evaluation units; and integrating evaluation results, and generating an underground space development geological suitability partition map of land-sea overall planning. According to the scheme, the underground space development pattern is optimized, comprehensive utilization of land and ocean resources is promoted, and safe, efficient and sustainable development and utilization of the coastal city underground space are powerfully supported.

The invention relates to the technical field of education evaluation, and discloses a course teaching effect evaluation method, which comprises the steps of constructing a two-dimensional evaluation index system containing student ability and teacher teaching as a basic framework of evaluation; based on the two-dimensional evaluation index system, determining an initial weight by adopting an interval intuitionistic fuzzy analytic hierarchy process, and dynamically correcting the initial weight in combination with an entropy weight method to form a combined weight; collecting multi-source data corresponding to the two-dimensional evaluation index system through a triangular verification method, and preprocessing the multi-source data; combining the combined weight with the preprocessed multi-source data, and performing layered fuzzy comprehensive evaluation, thereby realizing quantitative evaluation and weak link diagnosis of the course teaching effect; according to the method, expert knowledge and objective data are fused, so that the evaluation weight is dynamically changed along with actual teaching data, and the problem that evaluation is separated from reality due to weight solidification is solved.

The invention relates to a dynamic multi-dimensional quality evaluation system and method for a thermal power plant intelligent inventory supervision model. Real-time data flow of the DCS is collected and compared with the model prediction value, and a prediction error is generated; based on the load rate, the fuel characteristic and other characteristics, the current operation state is matched to a preset working condition library, and historical data is called for index calculation; the method comprises the following steps of: according to stability, reusability and economic indexes such as a variable coefficient of a prediction error, a model calling frequency, economic benefit influence and the like, normalizing the indexes by adopting a range method, and calculating the weight of each index by utilizing an entropy weight method; constructing a weighted standardization matrix, defining a dynamic ideal solution, calculating a close degree index, and outputting a comprehensive evaluation result; according to the scoring result, an optimization instruction is automatically pushed, and model parameter adjustment or data calibration is carried out, a set of dynamic and multi-dimensional thermal power plant supervision model evaluation system is constructed, the model quality can be objectively and comprehensively evaluated, and a closed-loop optimization mechanism is provided.

The invention provides an enterprise investment efficiency measuring and calculating system based on LASSO-game theory combined empowerment-super efficiency SBM, and the system comprises the steps: firstly, carrying out the screening of an existing input-output index system based on an LASSO method, and further providing subjective intervention in an evaluation system construction process; and then, combining output index weights obtained by the entropy weight method and the AHP method by adopting a game theory combined weighting method, and obtaining an output comprehensive index by combining a linear weighting method, thereby realizing scientific and reasonable weighting and dimension reduction of the output index. And finally, measuring and calculating the investment efficiency of the enterprise by adopting a super-efficiency SBM model considering unexpected output. According to the system provided by the invention, redundant information in an evaluation system is effectively simplified, estimation deviation caused by index dimension and importance difference is avoided, and unexpected output is introduced into a calculation process of an enterprise investment efficiency level, so that a result has more practical significance.

The invention provides a distributed energy aggregation cost risk control method based on second-order random dominance constraint, and belongs to the field of virtual power plant aggregation cost risk decision. The method comprises the following steps: firstly, establishing a risk neutral type-based VPP aggregation decision model; secondly, processing the VPP aggregation decision model by adopting a CVaR method and a risk neutral stochastic programming model to obtain a feasible region boundary of an SSD reference function; thirdly, establishing a VPP aggregation decision risk management model based on the SSD; and finally, proposing a Sharp ratio calculation method suitable for cost management by using the original definition of the Sharp ratio. And calculating the weights of the Sharp ratio and the CVaR by using an entropy weight method, and comprehensively evaluating the advantages and disadvantages of each reference scene. According to the method, the fluctuation risk of the VPP polymerization cost is effectively managed by introducing the second-order random dominance constraint. Meanwhile, by means of a CVaR risk management model, a benchmark feasible region of the second-order random dominance constraint is defined, and a more targeted reference is provided for risk decision-making of multi-element distributed resource aggregation adjustment cost.

The invention belongs to the technical field of electric power information physical system security, and discloses an urban power grid information physical system security situation early warning method based on cross-space risk propagation. A power grid physical layer and information layer coupling model and a cellular space false data injection attack model are constructed, and a fault cross-space propagation mechanism is simulated through an event-driven cellular automaton theory; an integrated kernel extreme learning machine model is adopted to predict the operation state of the power grid, multi-dimensional data fusion is realized through a radial basis kernel function, and an integrated structure is adopted to fuse a plurality of model prediction results so as to improve the model prediction precision; and establishing an early warning system including voltage out-of-limit, line overload and load loss indexes, and dynamically distributing weights and dividing early warning grades in combination with an entropy weight method. According to the method, the problem of low precision of urban power grid security situation early warning under multivariate disturbance is solved, and the accuracy and robustness of security situation early warning can be effectively improved.

The invention discloses a contact network state evaluation method and system based on adaptive empowerment optimization. The method comprises the following steps: collecting dynamic and static parameters of a contact network, calculating subjective and objective weights of each parameter by adopting an analytic hierarchy process and an entropy weight method, and calculating a static composite weight by adopting a combined weight method; historical degradation data of dynamic and static parameters of the overhead line system are collected, and the expected degradation rate of each parameter is obtained by adopting a time sequence neural network; calculating a parameter real-time degradation rate, and dynamically optimizing the static composite weight by adopting an adaptive method based on the real-time degradation rate and an expected degradation rate to obtain a dynamic composite weight optimization result; optimizing and updating the gain in the adaptive method; based on a dynamic composite weight optimization result, performing state evaluation on the contact network; the method and the system provided by the invention are not only suitable for contact network state evaluation, but also can be popularized and applied to other state evaluation fields, and have wide application prospects.

The invention discloses an online anomaly monitoring method and system for a linear movement cutting ore pulp sampler, and relates to the technical field of industrial automation, and the method comprises the steps: executing frequency band energy separation and sliding window statistical analysis on a working condition data set of the ore pulp sampler, and obtaining a multi-dimensional feature matrix; performing weight distribution and dynamic weighted aggregation on the multi-dimensional feature matrix to form a space-time analysis data packet, performing collaborative analysis on the space-time analysis data packet, and outputting a trend collaborative interaction matrix; and performing risk quantification and contribution degree distribution on the trend collaborative interaction matrix by using an entropy weight method to generate an abnormal quantification parameter, and performing confidence coefficient weighted calculation on the abnormal quantification parameter to form an abnormal probability value. According to the method, the working condition data set of the ore pulp sampler is fully fused through the sliding window statistical analysis and the entropy weight method, and meanwhile, deep feature mining and spatial relation fusion are performed through the dynamic causal atlas and the space-time convolutional neural network model, so that the reliability of anomaly monitoring is improved.

The invention discloses an overhead line system state evaluation method and system based on response type multivariable weighting optimization. The method comprises the steps of collecting dynamic and static parameters of a contact network, calculating subjective and objective weights of the parameters by adopting an analytic hierarchy process and an entropy weight method, and calculating a static composite weight by adopting a combined weight method; designing a dynamic response mechanism, if a response condition is met, dynamically optimizing the static composite weight to obtain a dynamic composite weight optimization result, and optimizing and updating the adaptive gain matrix; if the response condition is not met, keeping the dynamic composite weight optimization result and the adaptive gain matrix at the previous response moment; performing state evaluation based on a dynamic composite weight optimization result; the method and the system are not only suitable for contact network state evaluation, but also can be popularized and applied to other state evaluation fields with parameter coupling relations.