157 results about "Key factors" patented technology

The invention relates to the technical field of mechanical detection, in particular to an intelligent tool setting control method and system, and the method comprises the steps: collecting burr morphology data and tool state parameters of a slitting edge; establishing a mapping relationship between burr generation and processing parameters based on a process DOE model, wherein the process DOE model is used for extracting a key factor set influencing the slitting quality; when the real-time monitoring value of the key factor set deviates from the optimization interval of the process DOE model, generating a position and posture compensation instruction of the slitting tool rest; iteratively updating the key factor weight distribution of the process DOE model; and when the variance of the key factor weight distribution is converged to a preset threshold value, determining that the tool setting control closed-loop optimization is completed. According to the method, the defects of dynamic response hysteresis and insufficient compensation precision in traditional tool setting control are effectively overcome, and closed-loop self-optimization control of the position and posture of the slitting tool rest-process parameters-burr suppression is constructed through a three-dimensional dynamic compensation mechanism of real-time fusion of multi-source sensing data.

The invention provides a coal bulk loading and unloading efficiency optimization method and system based on machine learning, and relates to the technical field of coal bulk loading and unloading, and the method comprises the steps: recognizing key factors and quantitative association rules between the key factors and the coal bulk loading and unloading efficiency through a large language model; the storage yard decision topological graph is input into a reinforcement learning model, a coal bulk cargo loading and unloading scheme is determined by executing the process of state space coding, action selection and reward function calculation, and a reward function is obtained by taking a predicted value of the coal bulk cargo loading and unloading efficiency in the current period as a target and being constructed according to key factors and quantitative association rules; after the coal bulk cargo loading and unloading scheme is completed, the actual value of the coal bulk cargo loading and unloading efficiency in the current period is collected, the efficiency difference value is calculated, and the coal bulk cargo loading and unloading efficiency in the next period is optimized based on the efficiency difference value. According to the method, the efficiency can be effectively improved while various necessary conditions of loading and unloading operation are accurately met.

The invention discloses a transformer substation hidden danger point risk index model construction method, system and device and a medium, and belongs to the technical field of transformer substation risk assessment, and the method comprises the steps: collecting multi-dimensional data related to the operation of a target transformer substation; screening key factors influencing the risk of the hidden danger point, and determining the weight of each key factor; analyzing the multi-dimensional data by using a machine learning model, and identifying hidden danger points; constructing a risk assessment model and calculating risk indexes according to the weights of the key factors and the identified hidden danger points; and outputting the risk level of the hidden danger point and corresponding early warning information based on the risk index. By combining multi-dimensional data and utilizing a machine learning algorithm, the equipment operation state can be comprehensively analyzed, potential hidden dangers can be deeply mined, a large amount of historical data can be efficiently processed and analyzed, potential hidden danger points can be automatically recognized, the hidden danger point recognition accuracy is remarkably improved, early warning is ensured, equipment faults are effectively prevented, and the method is suitable for popularization and application. The operation risk of the substation is reduced.

The invention discloses an engineering quantity list comprehensive unit price rationality detection method and system, and belongs to the technical field of engineering cost. The invention discloses a bill of quantity comprehensive unit price rationality detection method and system. The method comprises the steps of extracting key factor features; obtaining a comprehensive unit price corresponding to the project name; constructing a project recognition library; s4, correcting and perfecting the prices of the abnormal items; calculating the comprehensive unit price of the new engineering project; the system comprises a unit price calculation module, a database construction module, a comprehensive unit price identification module and a new engineering project determination module. The problem that the comprehensive unit price of the new engineering project cannot be determined in the prior art is solved, the comprehensive unit price of the new engineering project can be calculated, pricing dispute caused by fuzzy feature description of the new engineering project can be reduced, the abnormal quotation can be effectively identified, and by correcting the abnormal quotation deviating from the rationality interval, the pricing efficiency of the new engineering project is improved. The construction period delay or claim risk caused by pricing errors can be reduced.

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 tea processing big data analysis and quality tracing system and method, and relates to the technical field of agricultural informatization management. The tea processing big data analysis and quality tracing system is constructed, full-process data collection, real-time monitoring and accurate tracing from fresh leaves to finished products are achieved, key parameter data in the processing process are collected in real time through a sensor, key factors influencing the tea quality are extracted through an advanced data analysis algorithm, and the quality of the tea is accurately traced. According to the method, the quality of tea leaves can be accurately identified, quality abnormal points are accurately positioned, the problems of incomplete data acquisition, lagged analysis and inaccurate tracing in a traditional method are solved, meanwhile, a unique tracing code is generated for each processing batch, a full-process data mapping table is constructed, dynamic updating and real-time query of quality information are realized, the quality management level of tea leaf processing is remarkably improved, and the method is suitable for large-scale popularization and application. The requirements of consumers on transparency and high quality are met.

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 relates to the technical field of building anti-seismic risk assessment, and discloses a building anti-seismic potential safety hazard assessment method and system based on multi-factor coupling, and the method comprises the following steps: collecting key factors of potential safety hazards of a to-be-assessed building, and endowing each piece of key factor information with a discrete value; constructing a nonlinear coupling model through key factor construction and an interaction effect between key factors; building state information is monitored in real time through a sensor, a damage or collapse possibility index is corrected according to monitoring data to obtain a correction value, and a second comprehensive risk value is obtained through recalculation; and according to the second comprehensive risk value obtained through calculation, building anti-seismic safety risk grades are divided. According to the method, the damage possibility index and the accident consequence severity index are constructed, the comprehensive risk value is calculated according to the damage possibility index and the accident consequence severity index, quantitative modeling evaluation from static attributes to the risk value is achieved, and the method is more scientific and objective compared with a traditional single-factor evaluation method with high empirical and subjective performance.

The invention relates to the technical field of asset assessment. The asset evaluation intelligent system based on the AI technology is provided, and the system comprises the steps that web crawler capture processing is conducted on a data source of a public network channel, and a first type of heterogeneous data is obtained; aPI interface docking processing is carried out on the data source of the enterprise internal system, and second-class heterogeneous data is obtained; performing logic splicing processing on the first type of heterogeneous data and the second type of heterogeneous data to generate multi-source heterogeneous asset evaluation data; performing model matching processing on the asset value characteristic matrix based on the asset type, and selecting an estimation model architecture; performing interpretability analysis processing on the dynamic evaluation parameters to generate a key factor contribution degree and an interpretable evaluation conclusion; and performing document processing on the contribution degree of the key factor and the interpretable evaluation conclusion to generate an interactive evaluation document so as to improve the multi-source data integration capability, enhance the risk dynamic evaluation adaptability and strengthen the real-time relevance and interpretability of the evaluation document.

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 relates to the technical field of engineering slope instability risk analysis, in particular to a weak intercalated layer-containing bedding slope instability risk analysis method, equipment and a medium. According to the technical scheme of the invention, a complex and uncertain slope engineering problem is converted into a computable and reasonable probability graph model. Full life cycle disaster control factors from natural conditions, geological conditions, engineering design, construction and operation maintenance are systematically combed, omission of key factors is avoided, and comprehensiveness of risk analysis is guaranteed. The traditional qualitative causal analysis is upgraded to a quantitative probability dependency relationship, and a mathematical model basis is provided for subsequent risk diagnosis and prediction. And the crossing of risk analysis from'static evaluation 'to'dynamic reasoning' is realized. The model not only can give a static risk value, but also can dynamically update the probability of all related variables after new evidence (such as monitoring data) is obtained, so that intelligent diagnosis and prediction are realized.

The invention relates to the technical field of environmental risk management and control, in particular to a tire wear particle environment fate prediction model construction method based on multi-dimensional data. Comprising the following steps: data acquisition and preprocessing; a feature importance analysis model based on a random forest is constructed, key factor mining is performed on the preprocessed collected data, core factors influencing tire wear particle environment fate are screened by calculating Gini importance values of the key factors, and feature vectors and confidence coefficients of the core factors are output; classifying and integrating factors; carrying out model association construction; model training storage; and performing model verification feedback. According to the method, the continuity, timeliness and consistency of tire wear particle sample data and environment associated data are improved through the built tire wear particle environment fate prediction model and through cooperative collection, space-time registration and noise filtering preprocessing of the multi-source monitoring equipment, and a high-quality data basis is provided for subsequent model building.

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 discloses a postoperative sleep disorder key factor identification method, and belongs to the technical field of medical informatics, and the method comprises the steps: collecting data, and preprocessing the data into a candidate feature set; carrying out feature importance calculation by adopting evaluation methods of at least five different principles; the importance and stability of the features are evaluated through score and ranking analysis path fusion; the features are divided into four quadrants according to importance and stability indexes by using a two-dimensional scatter diagram, and feature classification is performed, so that key factors of the postoperative sleep disorder are accurately positioned. The method improves the robustness and consistency of feature evaluation through multi-model fusion, reduces the deviation of a single method, achieves the precise recognition of key factors of postoperative sleep disorders, supports the construction and intervention optimization of a clinical prediction model, and improves the diagnosis accuracy and patient management efficiency.

The invention discloses a multi-algorithm fusion cyclone similarity identification method and a storm surge forecasting method based on weight optimization, relates to the field of cyclone similarity identification, and establishes a similarity calculation function by constructing a multi-dimensional time sequence containing a plurality of key factors and combining a weighted dynamic time warping algorithm. In this way, multi-dimensional comprehensive characterization of the cyclonic space-time evolution process is achieved, and the identification deviation caused by the fact that a traditional method only depends on a single feature is overcome. On this basis, a genetic algorithm, a particle swarm optimization algorithm and a differential evolution algorithm are respectively adopted to carry out iterative optimization on the weight vectors, minimization of a deviation coefficient of a candidate similar cyclone set is taken as a target, and a global optimal weight vector corresponding to each algorithm is searched; and based on a preset fusion strategy, determining a target optimal weight vector from the plurality of globally optimal solutions. According to the closed-loop recognition process, multi-dimensional feature modeling and scientific weight optimization are effectively fused, and objectivity and accuracy of cyclone similarity recognition are remarkably improved.

The invention provides a key test factor determination method and device based on a Bayesian causal network, and relates to the technical field of reason tracing. The method comprises the following steps: acquiring a multi-dimensional observation parameter of a preset intelligent system, and generating an initial data set based on the multi-dimensional observation parameter; based on an improved Bayesian causal structure learning algorithm and the initial data set, determining a causal structure diagram between an input factor and a target output in the initial data set; based on the causal structure diagram, performing intervention operation on an input factor pointing to the target output to determine an average causal influence degree of the input factor on the target output; and calculating importance degree scores of the input factors according to a preset weight fusion mode and the average causal influence degree, and determining the input factors corresponding to the importance degree scores meeting a preset key factor selection condition as key test factors. In the intelligent system, the real causal-driven effect between the variables is effectively determined, and the accuracy of the key test factor is improved.

The invention provides a manufacturing process quality improvement method based on multi-source data perception in order to solve the problem that an existing method can only achieve quality prediction and cannot achieve quality improvement. The method comprises the following steps: sensing multi-source heterogeneous data during manufacturing, and extracting knowledge from the multi-source heterogeneous data to construct a manufacturing process technology knowledge graph; a Resnet-LSTM quality prediction model is constructed and trained to predict defect types, key factors influencing defects are determined in combination with a manufacturing process technology knowledge graph, a multi-source sensor is deployed based on the key factors, multi-source sensor data influencing the defects can be sensed more accurately, an attention mechanism is introduced to perform weighted summation on the multi-source sensor data, and the defect detection accuracy is improved. The method comprises the following steps of: inputting the data into a trained CNN-ABILSTM quality improvement model to obtain a more accurate and credible quality prediction result, generating a process adjustment scheme capable of improving the manufacturing quality according to the quality prediction result in combination with a manufacturing process process knowledge graph, and improving the quality control and problem traceability during the manufacturing period.

The invention discloses a super capacitor and thermal power generating unit coupling frequency modulation control capability evaluation method, relates to the technical field of electric-thermal coupling frequency modulation, and aims to adaptively capture key factors influencing distribution accuracy and coordination control performance by introducing dynamic weight updating and factor correlation analysis, so that an evaluation result is more suitable for an actual operation condition, and the evaluation efficiency is improved. The accuracy and timeliness of evaluation are obviously improved; unit layer capability evaluation, coordination control evaluation and comprehensive benefit evaluation are organically fused, so that full-chain and multi-dimensional comprehensive analysis from equipment performance to strategy and then to economic benefit is realized; an evaluation model can be continuously corrected by constructing a closed-loop optimization mechanism for feeding back a final conclusion to an initial weight parameter, so that the high efficiency and advancement of an evaluation system are kept for a long time; the dynamic response speed, the control stability and the overall economic benefit of the combined system are effectively improved, and the method has extremely high practical application value.

The invention discloses a vehicle front end cooling module design method and system based on air inlet volume prediction, and belongs to the technical field of vehicle cooling system air volume matching and application, and the method comprises the steps that key factor data influencing the radiator air inlet volume under the current vehicle front end cooling module design scheme is obtained; aiming at the key factor data, extracting non-spatial features and spatial features of a currently designed cooling module by using a convolutional neural network and a graph convolutional neural network respectively; inputting the extracted features into a supply air volume prediction model, and outputting the supply air volume predicted under the current design scheme; and continuously adjusting the key factor data to generate different design schemes, predicting the air inlet rates under the different design schemes, and adjusting and screening according to the predicted air inlet rates to obtain an optimal cooling module design scheme. According to the method, a neural network-based radiator air inlet quantity prediction mode is adopted, a feedback mechanism is matched, the model selection process of the cooling module is improved, design iteration of the front grid is accelerated, and the research and development efficiency is improved.

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 present disclosure provides a processing method and device for generating search data, equipment and storage medium. The present disclosure relates to the technical field of computers, especially to the technical field of search technology and large model technology, and can be used in application scenarios such as generative search, intelligent question answering, intelligent recommendation, etc. The method comprises: collecting feedback data of a target object on a search page first displayed search answer; analyzing the feedback data to identify key factors affecting the satisfaction of the search answer; combining the key factors to mine a first sample set and a second sample set from the feedback data; based on the first sample set and / or the second sample set, optimizing the first large language model to obtain a second large language model; deploying the second large language model to the search system to replace the first large language model. According to the scheme of the present disclosure, high-confidence samples can be automatically mined, a self-optimization cycle of the generative large language model is realized, and the quality of the search answer is improved.

The invention discloses a while-drilling pressure measurement fluidity correction method under a multi-factor interaction effect, and the method comprises the steps: constructing a while-drilling pressure measurement numerical simulation model of an anisotropic stratum inclined shaft, and deducing the model as a while-drilling pressure measurement finite element model of the stratum inclined shaft; performing stratum fluidity inversion analysis on the stratum inclined shaft pressure measurement while drilling finite element model, and identifying key factors influencing the logging pressure measurement while drilling fluidity based on an analysis result; carrying out numerical simulation on the basis of the identified key factors to obtain a pressure response result of the pressure measurement while drilling, analyzing formation fluidity characteristics under the interaction of the multiple factors, and further determining a fluidity response rule expression of the pressure measurement while drilling under the interaction of the multiple factors; and substituting key factor values in the actual measurement while drilling pressure measurement process into the corresponding while drilling pressure measurement fluidity response rule expression, and further calculating to obtain the while drilling pressure measurement fluidity of the vertical well, thereby realizing the correction of the while drilling pressure measurement fluidity. The method provided by the invention provides a theoretical basis and a technical support for the use of the pressure measuring instrument while drilling.

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 application discloses an edge device intelligent sensing method and system based on information freshness, constructs a Markov decision process model by analyzing key factors in the edge device intelligent sensing system. Secondly, the system state is updated in real time through dynamic adjustment of the sensing state, the parameters of the device are adjusted according to the new environmental changes, so that the system can quickly respond in a complex environment. In the aspect of joint optimization, the Lagrange multiplier method is used to jointly optimize the information age and energy consumption, so as to maximize the freshness of information while meeting the energy constraint. Finally, through the deep Q network, the system can continuously optimize the decision strategy through the experience replay mechanism, so that the edge device can minimize the information age under the condition of energy limitation in the changing environment. The application improves the timeliness of the sensing information.

The invention provides a wellhead device fatigue life prediction analysis method, and relates to the technical field of underground engineering, and the method comprises the following steps: collecting load spectrum, material degradation, environmental corrosion and historical maintenance data of a wellhead device, and establishing a four-dimensional damage factor matrix; performing preprocessing and feature fusion on the multi-source data; a self-attention LSTM network is constructed; according to the method, four key factors including a load spectrum, material degradation, environmental corrosion and historical maintenance are brought into a unified analysis framework through the four-dimensional damage factor matrix, the one-sidedness problem of single factor analysis in a traditional method is solved, and the attention weight among multi-source data can be automatically learned through the self-attention LSTM network; according to the method, the capability of extracting multi-physical field parameter correlation characteristics in a complex service environment can be remarkably improved, a fatigue life probability distribution curve is generated in combination with Monte Carlo simulation, a prediction result containing a confidence interval is provided, and a more scientific quantitative basis is provided for equipment maintenance decisions.

The invention discloses a key factor identification method for influencing process industrial load participation in demand response and computer equipment thereof, and relates to the field of power system scheduling and control. Firstly, a state task network is utilized to describe a production link coupling relation, and a maximum total system profit is taken as an optimization target; and constructing an energy optimization scheduling model for the general industrial process. Secondly, common influence factors in the production process are extracted for a power grid and process industrial production interaction scene; the method comprises the following steps of: performing preliminary rapid screening on a large number of influence factors on the basis of a Morris screening method, and finally quantifying global sensitivity coefficients of the screened influence factors on three types of core indexes, namely economical efficiency, productivity and energy efficiency on the basis of a Sobol 'global sensitivity analysis method, so as to identify key factors which influence the participation of the process industrial load in demand response.

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.