79 results about "Key factors" patented technology

The invention discloses an enhanced GAN extreme scene generation method and system considering weather factors, and relates to the technical field of scene generation in new energy power system planning and operation, and the method comprises the steps: extracting key factors from multi-time-delay meteorological data, forming an extreme label, converting the extreme label into a generation condition, and achieving the output through two-stage confrontation generation, wherein a scarce sample is stably amplified by WGAN-GP, a joint curve is generated according to conditions, and related structures, climbing and power boundary constraints are synchronously embedded to ensure statistical and physical consistency. According to the method, the authenticity of the distribution tail, the cross-variable correlation and the time structure can be considered when the sample is scarce. Extreme situation deduction of a novel power system can be supported while extreme situation coverage and credibility are improved, and an existing new energy scene analysis and planning process is integrated.

The invention discloses a method and a system for presuming a correction coefficient of the strength of a standard test piece by adopting a small core sample test piece, and belongs to the technical field of concrete strength detection. The method solves the problems of large error and high discreteness of a presumption result caused by factors such as size effect, aggregate constraint and process difference when the standard strength is directly presumed by adopting the strength of a small core sample in the prior art, constructs a historical sample library containing multi-dimensional characteristics, calculates an initial correction coefficient and a size ratio, and obtains the presumption result of the standard strength. Key factors such as the small core sample size, the coarse aggregate particle size, the water-binder ratio, the curing age and the height-diameter ratio are comprehensively considered, an intelligent correction model is established by utilizing algorithms such as a gradient boosting decision tree and a random forest, and a comprehensive correction coefficient can be scientifically and accurately output, so that the actually measured strength of the small core sample is reliably converted into the strength of a standard test piece; and the presumption precision and the result stability are greatly improved, so that reliable technical support is provided for safety and durability evaluation of an engineering structure.

The invention discloses a tree safety risk assessment method, system and device based on a key index priority mechanism, and a storage medium. The method comprises the following steps: constructing an inference model according to a fault tree model of a street tree and a fuzzy Bayesian network; based on the inference model, through reverse inference, sensitivity analysis and most approximate cause chain analysis, screening out a key index set and a conventional index set from a preset risk candidate index set; and based on a hierarchical risk assessment principle, respectively assessing the key index set and the conventional index set to obtain an assessment result. According to the inference model, key factors having significant contributions to the overall risk of the border tree are identified from a large number of risk indexes; a conjoint analysis method of reverse reasoning, sensitivity analysis and most approximate cause chain analysis is adopted, so that false alarm and missing alarm are effectively reduced; according to the hierarchical risk assessment principle, key factors are assessed preferentially, it is ensured that the street tree with structural fatal defects can be recognized and controlled in time, and the reliability and timeliness of assessment are improved.

The invention discloses a power distribution network fault self-healing success rate prediction and strategy evaluation method, system and device and a medium, and relates to the technical field of power distribution network self-healing, and the method comprises the steps: collecting power distribution network operation data and historical self-healing case data, analyzing key factors influencing the self-healing strategy success rate after preprocessing, and constructing an influence factor network; a probability graph model is established based on the network to perform success rate prediction, a classification evaluation model is constructed to perform preliminary evaluation, a prediction result is output through multi-model integration optimization fusion, a strategy evaluation index system is constructed to perform comprehensive evaluation on a self-healing strategy, and an adaptive evaluation mechanism is introduced. Dynamically adjusting model parameters according to the network state similarity, and finally outputting an evaluation result. The power distribution network fault self-healing success rate prediction and strategy evaluation method realizes the technical spanning of power distribution network fault self-healing success rate prediction and strategy evaluation from single dimension to multiple dimensions, from static analysis to dynamic optimization, and from simple prediction to comprehensive evaluation, and significantly improves the intelligent level and decision reliability of power distribution network self-healing operation.

The invention discloses a mine underground space recycling project disaster-causing key factor identification method, and relates to the technical field of underground space energy storage. The method comprises the following steps: firstly, collecting multi-source monitoring data such as displacement, microseism, stress and the like, and carrying out timestamp alignment, denoising and interpolation completion preprocessing through improved bidirectional dynamic time warping, Hampel filtering and GRU-D algorithms; converting the data to generate an initial index set containing the underground space stability state rate and the inter-field coupling strength, and screening out an optimal index subset through 20-50 generations of Bayesian optimization search and 5-fold cross validation; constructing a multi-field coupling dynamic graph based on the subset; and finally, inputting the dynamic graph into a graph neural network, outputting a node importance degree sequence through a GNN Explainer algorithm, and determining disaster-causing key factors. According to the method, multi-source data and a multi-field coupling mechanism are fused, the recognition accuracy and the model interpretability are improved, and technical support is provided for safe operation and disaster prevention and control of mine underground space recycling engineering.

The invention provides an enterprise environment influence assessment method, device and equipment and a computer storage medium, and relates to the technical field of environment assessment, and the method comprises the steps: collecting environment index original data with a timestamp, and constructing a multi-source environment data set in combination with enterprise resource consumption data and emission factor data; carrying out credibility evaluation and weighted fusion on the data set, inputting the fused data into a pre-training evaluation model, and outputting a comprehensive environmental impact evaluation value; the contribution degree of each input feature is analyzed and calculated through model attribution, key factors are identified based on metadata, and a customized improvement measure list of priority ranking is generated in combination with a measure knowledge base and a multi-objective optimization algorithm; the invention further provides a matched portable detection device, electronic equipment and a storage medium, dynamic quantification, accurate attribution and closed-loop optimization of enterprise environment influences are achieved, and the method is suitable for intelligent environment management of high-emission industrial enterprises.

The invention relates to the technical field of test design, in particular to a key factor mining-based accommodating and arranging condition optimization method, medium and equipment, which are characterized in that accommodating and arranging conditions are generated by analyzing a target text, an initial simulation queue is constructed in combination with database retrieval, and baseline feature distribution is analyzed, so that representative research crowds can be obtained without real object recruitment; the test cost is reduced; a panoramic object feature matrix is constructed by integrating multi-dimensional data, the limitation of a single feature is broken through, and comprehensive data support is provided for key factor mining; the contribution degree of each characteristic variable to the preset key outcome is analyzed based on the matrix, and the key characteristic variables are positioned, so that the experience driving is changed into the data driving, and the scientificity and the pertinence of the accommodating and arranging condition design are improved; the accommodating and arranging conditions are dynamically adjusted through the closed-loop optimization process, it is ensured that the target accommodating and arranging condition set gives consideration to queue representativeness, feature pertinence and distribution rationality, and the design scientificity and success rate of clinical tests are improved.

This invention relates to the field of machine learning technology, specifically disclosing a machine learning method based on engineering project contract data, comprising the following steps: Step S1: Collect historical engineering project contract data, including factor values ​​and project progress at each time point; calculate the impact sensitivity and sensitive impact value of each factor, distinguishing between high-sensitivity factors and low-sensitivity factors; Step S2: Set a monitoring interval, collect factor values ​​and project progress data in real time; dynamically correct the sensitive impact value of high-sensitivity factors based on real-time data, generate correction parameters, and ensure that the model adapts to the latest project status; Step S3: Combine the correction parameters of high-sensitivity factors and the fixed sensitivity values ​​of low-sensitivity factors to predict the project completion time interval; calculate the probability value of a reasonable long payment period for the project based on the comparison between the predicted time and the actual planned time; This method improves the accuracy of engineering project progress prediction by dynamically adjusting key factor parameters.

A kind of power transmission and transformation engineering progress identification and analysis method and system based on deep learning, method first acquires and pre-processes power transmission field multimedia data, constructs engineering progress data set, then utilizes plan and progress data set to train deep learning model, then input model with new progress data to identify current engineering state, and compare planned progress, analyze deviation, finally based on model and key influencing factors are comprehensively trend forecasted;The design in application, through the recursive transformer fusion network model fused with multiple modules and the acquisition of key factors of various difference data, and combining the difference of different voltages realizes comprehensive trend analysis, so that it can effectively extract deep features from multiple data sources, realize high-precision identification of engineering progress, compared with traditional manual inspection and recording method, greatly improve the identification efficiency and accuracy, reduce the interference of human factors, ensure the real-time and reliability of progress identification.

The invention discloses an e-commerce big data analysis method and system based on artificial intelligence, and belongs to the field of data analysis, and the analysis method comprises the following specific steps: I, obtaining various e-commerce data from various channels, preprocessing the e-commerce data of different sources and different formats, and constructing an integrated data warehouse; iI, based on the collected e-commerce data, mining behavior characteristics of the user, extracting a fine-grained behavior pattern, and constructing a personalized label of the user; iII, analyzing each piece of text data uploaded by the user, and extracting the psychological demand, risk perception and consumption intention of the user; according to the method, the workload of manual cleaning, labeling and merging is greatly reduced, the user portrait does not stay on a static label any more, an interpretable dynamic behavior mode is formed, the causal inference and real-time key factor recognition capability is improved, and platform treatment, compliance requirements and manual checking are facilitated.

The invention discloses a scene feature joint modeling method, system, equipment and medium, and relates to the technical field of power grid engineering, and the method comprises the steps of completing spatial-temporal scale calibration, extracting independent feature vectors, generating a fusion feature map, screening key influence factors, generating comprehensive feature representation, and measuring and calculating cost and engineering quantity. The system comprises an acquisition and alignment module, a feature extraction module, a feature fusion module, a correlation analysis module, a depth modeling module and a prediction module. By constructing a space-time unified reference framework, multivariate cross-modal data is mapped to a unified space-time scale, and the problem of data islands is solved; a multi-resolution feature adaptation strategy is utilized to extract fusion features considering shallow details and deep semantics, and feature extraction comprehensiveness is ensured; and the influence of the key factors is quantified by establishing a regression mapping model, so that the accuracy and reliability of power grid project cost measurement and calculation are greatly improved.

The invention discloses a product quality defect analysis method and device and a medium, and relates to the technical field of data processing. The method comprises the following steps: firstly, establishing a prediction model from a raw material performance index to a product quality index based on historical data by using a random forest algorithm; when product defects are detected, initial raw material data of the batch of products are obtained and subjected to multi-round random disturbance, a large number of virtual samples are generated, and the quality of the virtual samples is predicted through a model. Then, qualified samples are screened out from the virtual samples, and a sample subset with the highest model prediction reliability is further selected from the samples. And finally, performing statistics on the consistency of the disturbance direction of each raw material performance index in the subset, and judging the raw material index with the highest consistency as a key factor which is most likely to cause the current quality defect. Noise in industrial data can be effectively processed, product quality problems can be quickly and accurately attributed to specific raw material characteristics, and accurate guidance is provided for production control and process optimization.

The purpose of the present invention is to provide a yield prediction simulation system and method that can simulate yield prediction by predicting a yield during a second cycle and reflecting a label variation amount on the basis of process operation data during a first cycle. Moreover, the invention also provides a yield prediction simulation system and a yield prediction simulation method, which can divide the first period into a plurality of sections based on the service life of the catalyst and the corresponding yield change, and perform key factor analysis, yield prediction and label variation analysis according to the different sections, thereby improving the yield prediction simulation efficiency. Yield prediction simulation is carried out in the second period, and the prediction accuracy is further improved in the whole period.

The application discloses a kind of dangerous event chain extraction method and system based on complex traffic scene, including obtaining test scene data;Based on the key factors of dynamic attribute and interaction between vehicle and environment determined by preset natural driving data and sensor performance information, and the uncertainty of key factors is quantified;Based on the risk estimation multidimensional feature set and uncertainty information preset constructs driving risk estimation model for test scene;The driving risk estimation model is optimized, and the optimized risk estimation model for test is obtained;Based on the time evolution characteristics of dangerous event, a dangerous time chain model is constructed according to the optimized risk estimation model;Dangerous time chain model is solved, and dangerous event chain is obtained.The application uses traffic accident data and traffic conflict data to reveal the space-time evolution law of complex traffic scene, and improves the effect of later test scene reproduction and reconstruction by solving the dangerous time chain model.

The application discloses a DEMATEL-ISM model dynamic threshold calculation method based on centrality characteristics. The method aims at the subjectivity problem of threshold determination in the construction of the reachable matrix of the traditional DEMATEL-ISM model, introduces the sorting distribution characteristics of factor centrality, establishes a centrality-influence strength correlation model to realize the dynamic optimization of the threshold, determines the initial threshold through an empirical formula, expands the candidate threshold range, calculates the reachable matrix corresponding to each candidate threshold and the factor node degree sorting, introduces the Spearman correlation coefficient to quantify the similarity of the node degree sorting and the centrality sorting, and finally selects the optimal threshold. Compared with the prior art, the application gets rid of the dependence on a single empirical formula, improves the scientificity and objectivity of threshold determination, enhances the robustness of the threshold and the noise resistance of the model, effectively retains the interaction relationship between key factors, makes the reachable matrix more consistent with the actual causal relationship network, and is suitable for complex system factor analysis, risk assessment, hierarchical structure division and other scenes.

The invention relates to the technical field of machine learning, in particular to a city update district intelligent decision-making method and system based on machine learning and a genetic algorithm, and the method comprises the steps: collecting historical city update data of a to-be-decided city; key factors in historical city update data are screened based on features; based on the key factors and the city updating types, respectively taking the key factors and the city updating types as training data of a first machine learning model and a second machine learning model, predicting areas needing to be updated through the trained first machine learning model, and predicting updating types of the areas needing to be updated through the trained second machine learning model, and generating an implementation time sequence scheme of the city update districts through a genetic algorithm. According to the method, full-process intelligent decision making is realized, and the problems of high subjectivity, low efficiency and insufficient systematicness in a traditional planning method are effectively solved.

The invention discloses an Internet of Things data traceability method and system for smart agriculture, and belongs to the field of data traceability, and the method comprises the following steps: real-time collection and encryption trace leaving of purchase transaction data, construction of an effective price sample set, generation of a supervision reference price, and cross analysis of key factors and formation of an auditing evidence chain. Canteen purchase transaction full-link data is collected in real time through an Internet of Things terminal, an original price traceability sample is formed through encryption, and dynamic association traceability is performed; preprocessing the sample according to a preset period, and performing weighted average to generate a commodity supervision reference price; according to the method, key influence factors are extracted for cross analysis, a traceability report is generated and stored in an associated manner, and an auditable price decision evidence chain is constructed, so that the data traceability reliability of the Internet of Things is improved, and the problem of low data traceability reliability of the Internet of Things in the prior art is solved.

Embodiments of the present application provide a power consumption prediction method, device, equipment, medium and product. The method first determines preset power consumption key factor data of a to-be-predicted area, wherein the preset power consumption key factor data contains a value of a power consumption key factor of the to-be-predicted area in an actual power consumption scenario, and then performs normalization processing on the preset power consumption key factor data to obtain first power consumption key factor data. Subsequently, first predicted power consumption data of the to-be-predicted area is determined according to the first power consumption key factor data and a prediction model, wherein the prediction model is determined based on normalized historical power consumption key factor data and historical power consumption data. The method of the present application realizes accurate prediction of power consumption.

The invention provides an oil and gas storage and transportation station safety risk dynamic evaluation method and system, and belongs to the technical field of oil and gas storage and transportation safety, and the method comprises the steps: carrying out the recognition and analysis of hazard source events of an oil and gas storage and transportation station, and preliminarily constructing a hazard source event tree; key factor events of the oil and gas storage and transportation station are identified and analyzed, and a key factor event tree is preliminarily constructed; performing safety level division, dividing the safety problem of the oil and gas storage and transportation station into three levels of equipment, operation and management, and analyzing the hazard source event tree and the key factor event tree according to the divided safety levels; identifying and analyzing security elements of the key equipment; and formulating a solution. The system comprises a data acquisition module, a database and a multi-terminal communication module. According to the method, the event tree is constructed, possible trigger factors and development paths of each dangerous source and key factor are analyzed in detail, the safety management work of the oil and gas storage and transportation station is perfected, and the long-term and stable development and progress of the oil and gas storage and transportation station are finally realized.

The invention relates to the technical field of wind power prediction, and discloses a short-term wind power prediction method, system, equipment and medium, and the method comprises the steps: obtaining original wind power plant data, carrying out the correlation analysis, and obtaining key factor data influencing wind power generation; decomposing the key factor data to generate a plurality of IMFs components; the IMFs components are input into a Transform encoder and a GA-BiGRU network, and global features and local features are extracted respectively; inputting the two features into a cross attention mechanism module for feature fusion, and generating each prediction component; and constructing a prediction confidence interval of the wind power to obtain probability distribution information of the wind power. According to the method, non-stationarity and high volatility of wind power plant data are effectively reduced through CEEMDAN decomposition, prediction precision and robustness are improved in combination with a dual-network model, uncertainty is quantified through ABKDE, reliable decision support is provided for power grid dispatching, and prediction accuracy is improved.

The present disclosure provides a feature disturbance-based pavement performance key factor identification method and system, relating to the technical field of performance factor identification, comprising obtaining the characteristic performance and geometric parameters of the road pavement, and constructing a scaled model of the road pavement; obtaining a plurality of performance influencing factors that affect the road pavement, inputting the performance influencing factors into a neural network model for extreme feature disturbance, adjusting the values within the range of [0, 1] by setting the maximum and minimum values, observing the changes in the output results of the neural network model, and preliminarily determining the key factors that have a great influence on the output results of the neural network model; repeatedly inputting the obtained key factors into the neural network model and verifying the accuracy of the selected key factors in a probabilistic manner. The present disclosure can accurately obtain the performance key factors generated by the change of the pavement.

The invention discloses a method and system for evaluating the life extension of an oil and gas facility, and the method comprises the steps: disassembling the oil and gas facility to be evaluated, obtaining a plurality of subsystems to be evaluated, carrying out the three-element analysis of the subsystems to be evaluated, and selecting key factors affecting each subsystem; the influence degree of each subsystem on the whole oil and gas facility is analyzed, and on the basis, key factors of the corresponding subsystems are combined to determine key factor characteristic values; according to the structural feature detection data and the key factor feature value of each subsystem, determining the residual life of the subsystem; and according to the residual life of each subsystem and the life extension target of the whole oil and gas facility, determining a life extension measure of the subsystem which does not reach the life extension target. According to the method, the subsystems with different influence degrees can be evaluated respectively, the subsystems in bad states can be scientifically discriminated, and the aim of prolonging the service life of all the subsystems can be synchronously achieved by taking appropriate measures, so that the final recovery efficiency of oil and gas field development can be improved, and the maximization of oil and gas yield and economic benefits can be realized.

This invention discloses a method for formulating a winning strategy based on a two-on-one pursuit-escape game in orbit, belonging to the field of aerospace technology. The method includes acquiring parameter information of the participants in the two-on-one pursuit-escape game; analyzing the impact of the participants' parameter information on the game outcome to generate an analytical game result; and formulating a winning strategy based on the analytical game result. This invention, through a comprehensive consideration of multiple key factors, can provide targeted winning strategies for different pursuit-escape scenarios.

The invention discloses a design method of scrap steel group materials for converter steelmaking, which comprises the following steps of: determining factors influencing smelting cost as independent variables and the cost as dependent variables, and designing a test scheme by adopting a quadratic regression orthogonal combination design method. And performing cost accounting on the test scheme, and based on cost data, fitting by adopting a least square method to obtain a quadratic regression equation reflecting the relationship between each variable and the cost. And performing variance analysis and significance test on the quadratic regression equation, and determining key factors and non-key factors which have significant influence on the cost. And the values of non-key factors are fixed, the influence of the key factors on the cost is analyzed, and a scrap steel proportioning scheme with the optimal cost is determined. According to the method, through systematic experimental design, a mathematical model, statistical analysis and a hierarchical optimization strategy, fundamental transformation of converter burdening from experience dependence to model driving and from local optimization to global optimization is achieved, and therefore the minimization of the steelmaking cost is achieved.

A quantitative analysis method for the spatiotemporal distribution characteristics of system frequency based on LQR optimal feedback control is proposed. This invention belongs to the field of power system frequency analysis and control technology. To address the problem that existing indicators are insufficient to characterize the entire dynamic process and are not effectively correlated with control methods, this invention defines the integral of the weighted sum of squared frequency differences between nodes as the objective function, transforms it into an LQR standard quadratic form, and solves for the optimal feedback control matrix K. The Frobenius norm of matrix K is used as a quantitative evaluation index, and its rationality in measuring control potential and frequency distribution significance is proven through matrix inequalities. A functional relationship is established between the index and the state coefficient matrix A and the input state coefficient matrix B, and the Fréchet derivative is obtained to analyze the key factors affecting the spatiotemporal distribution of frequency. This invention starts from the control level, considers the entire dynamic frequency process, and can quantitatively analyze the impact of system structure and disturbances on frequency differences.

The invention relates to an intelligent identification method for main control factors of surface mine slope deformation instability, and the method comprises the following steps: S1, data acquisition and preprocessing: obtaining surface mine slope geological data, deformation data and environmental data, and removing and filling abnormal values and missing values; s2, determining candidate control factors: extracting the candidate control factors from the maximum vibration rate in the environment data and the rainfall data; s3, key control factor identification based on a neighborhood rough set: calculating the importance degree of each control factor to the mine slope deformation data based on a neighborhood rough set method, and identifying the key control factors; and S4, key control factor verification based on support vector regression: establishing an SVR model of the screened key control factors and deformation data, and evaluating the accuracy of the key factors based on an SVR prediction effect. According to the method, the SVR model is established based on the neighborhood rough set and the support vector machine regression method, and the accuracy of the key factors can be evaluated based on the SVR prediction effect.

The invention relates to a network public opinion key influence factor identification method based on influence degree optimization, and the method comprises the steps: carrying out the value adjustment according to a single dimension, so as to find the single dimension which can remarkably improve the influence degree; and combining the dimensions with small weights in pairs so as to find a dimension combination capable of remarkably improving the influence degree. After the single dimension capable of remarkably improving the influence degree is recognized, the value of the influence factor is adjusted in the dimension so as to find the key factor capable of remarkably improving the influence degree. And after the combination dimension capable of remarkably improving the influence degree is identified, selecting the influence factors with small weights in the combination dimension, and carrying out inter-dimension pairwise combination so as to find key factors capable of remarkably improving the influence degree. After key influence factors are identified, important reference data can be provided as health guidance public opinions.

The invention relates to a mutual information and Shapley value fused power system operation reliability diagnosis method and equipment. On the basis of the power system operation reliability agent model, the mutual information and Shapley value fused power system operation reliability diagnosis method is provided, and the influence degree of uncertain factors on operation reliability indexes can be effectively quantified. According to the scheme, firstly, a normalized mutual information index is applied, the interpretation ability of each uncertainty factor for a reliability index is quantitatively evaluated, and key factors are screened out; and calculating the contribution of each key factor to the reliability index through a Shapley value to realize interpretable decomposition and responsibility attribution of the operation reliability of the power system. According to the scheme, interpretable decomposition and quantitative attribution of the influence of the uncertainty factors on the reliability indexes are achieved, an innovative analysis framework is provided for power grid operation risk identification and scheduling decision making, and the method can be used for assisting in further diagnostic analysis of the operation reliability risk of a power system.

The present application belongs to the technical field of poultry house temperature prediction, and specifically discloses a poultry house temperature prediction method and system. The present application first constructs a multi-factor coupled high-density feature system, breaking through the limitation of traditional single feature input. Secondly, a time series prediction model integrating attention mechanism and environmental self-adaptive mechanism is designed. Through the feature attention layer, the key factors are dynamically weighted, solving the problem of the contribution degree of multiple source factors changing over time. By using TCN inflation convolution to efficiently extract local fluctuations, and by using the LSTM gating mechanism to capture long-time dependence, the deep mining of the evolution law of complex thermal environment is realized. In addition, the present application proposes an outdoor temperature driven environmental self-adaptive mechanism, taking the outdoor temperature change rate as the trigger signal of environmental sudden change, and dynamically fusing multi-scale receptive fields through the gating network, solving the prediction lag problem of traditional fixed window model at key moments such as sunset and cold wave.