1227 results about "Assessment index" patented technology

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

A method and system for early prediction and warning of mine disasters through on-site monitoring is provided. [Solution] The method involves installing sensors at the mine site to monitor surface water, groundwater data and underground rock data in real time, so that mine safety management personnel can obtain timely and comprehensive data to better understand the state of the mine environment. The predictive analysis module uses deep drilling and computational learning technology to analyze the surface water, groundwater data and underground rock data to predict the risk assessment index Fpzs, which is helpful in predicting potential disaster risks, and comprehensively takes into account various factors such as the number of past disasters Lzs, the water level fluctuation coefficient Sbxs, and the rock stability coefficient Ywxs, which is helpful in quantifying the risk level, providing a more accurate disaster risk assessment, and reducing the casualties caused by mine disasters.

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.

Disclosed in the present invention are a rapid large-range asphalt-pavement quality assessment method and system based on high-resolution remote sensing. The method comprises the following steps: acquiring a high-resolution multispectral image and panchromatic image of a pavement area of a road network area to be subjected to assessment; generating superpixels of the pavement area on the basis of the high-resolution multispectral image and panchromatic image of the pavement area; removing superpixels of a non-asphalt pavement from the superpixels of the pavement area, so as to obtain superpixels of an asphalt pavement in the pavement area; on the basis of the superpixels of the asphalt pavement, performing calculation to obtain a brightness indicator, texture indicator, structure indicator and spectrum indicator of each superpixel of the asphalt pavement; and according to the calculated brightness indicator, texture indicator, structure indicator and spectrum indicator, performing calculation to obtain a remote-sensing quality assessment index corresponding to each superpixel of the asphalt pavement, and then obtaining a quality situation assessment diagram for the asphalt pavement. By means of the present invention, various assessment indexes are comprehensively used, thereby improving the precision and visibility of an assessment result, and reducing the manual intervention.

The invention discloses a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention provides a data asset multi-dimensional assessment method and system based on a block chain and machine learning, an assessment index system covering dimensions such as data quality, value, risk and security is constructed, a block chain technology is adopted to carry out assessment process and result evidence storage, a machine learning model is introduced to dynamically optimize index weights, and the assessment efficiency is improved. And a data asset marketization pricing mechanism is explored and formed. According to the method, the problems that an existing data asset evaluation method is single in dimension, high in subjectivity and lack of credibility are solved, scientific evaluation, marketization pricing and credible publication of the data asset value are achieved, important reference is provided for data element circulation, and the method has great significance in promoting prosperous development of the data element market.

The invention provides an evaluation method, system and device for a large language model and a storage medium, and the method comprises the steps: constructing a sample data set which comprises input data and corresponding reference answers, inputting the input data into an evaluated model, and generating an output result; a multi-dimensional evaluation index system is designed according to task requirements, a dynamic weight is allocated to each evaluation index, each evaluation index is provided with scoring standard description, and the evaluation indexes comprise at least two items of context correlation, term consistency, language fluency and expression accuracy; combining the input data, the reference answer, the output result and the scoring standard description into a standardized input instruction, and calling an evaluation model to perform multi-dimensional scoring on the standardized input instruction to generate an evaluation result; and analyzing the evaluation result according to a preset threshold value, and generating a structured feedback suggestion containing an improvement direction. According to the method, rapid optimization and iteration of the large language model can be effectively supported.

The invention discloses a method for detecting disconnection of a control loop. The method comprises the following steps: acquiring an original signal data set, performing sampling frequency optimization on the original signal data set to obtain an optimized sampling frequency value, performing resampling and noise processing, and generating a quality evaluation index; performing enhancement processing on the preprocessed signal, extracting time-frequency domain features, performing correlation analysis and time sequence decomposition, and generating a reliable feature set and a reliability report; performing feature space mapping and hierarchical feature extraction based on the reliable feature set, executing fault recognition to obtain fault probability distribution, performing credibility evaluation, and generating an anomaly evaluation report and a final diagnosis result; and extracting time sequence characteristics according to the abnormal assessment report and the diagnosis result, performing trend prediction and risk assessment, and generating an optimization decision scheme. According to the invention, through multi-level signal processing and feature analysis and in combination with a credibility evaluation mechanism, accurate detection and predictive maintenance of the control loop broken line fault are realized.

The invention proposes a power supply service risk identification system based on big data analysis, and relates to the technical field of power supply services, and the system comprises a data collection module which is used for collecting multi-source data related to a power supply service, and the multi-source data comprises work order data, feedback data and public opinion data; the data processing module is used for preprocessing the multi-source data to obtain standardized data; the data fusion module is used for fusing the standardized data to obtain fused data; the risk identification module is used for analyzing the fusion data, identifying the risk type in the power supply service by establishing a power supply service risk assessment index set, and determining the risk level; and the early warning output module is used for generating risk early warning information according to the risk identification result, and the risk early warning information comprises a risk type, a risk level, an influence range and a disposal suggestion. According to the invention, comprehensive identification, accurate evaluation and timely early warning of the power supply service risk can be realized, so that the power supply service risk management level is effectively improved.

The invention relates to the technical field of computer control, and discloses an automatic computer control method and system based on artificial intelligence. The method comprises the following steps: firstly, acquiring multi-source heterogeneous sensor data, including environmental parameters, equipment running states and operation instruction logs; then, an environment state evolution model is constructed, an equipment health degree evaluation index is generated, and an operation instruction log is analyzed; and then, inputting the results into a multi-modal decision model to generate a dynamic control instruction set, constructing a control strategy topological graph through an adaptive optimization algorithm, and outputting an equipment regulation and control scheme. The system correspondingly comprises a multi-source data acquisition module, an environment modeling module, a health assessment module and the like. Multi-source data can be effectively processed, the health condition of equipment can be accurately evaluated, a control instruction and a regulation and control scheme can be intelligently generated, the intelligent level of computer control is improved, and the reliability and the operation efficiency of the system are improved.

The invention relates to the technical field of data analysis, and discloses a precise targeted early warning method and device for meteorological disasters of kiwifruit canker and a medium. The method comprises the following steps: performing standardization processing on historical occurrence time, morbidity degree and distribution range data of kiwifruit canker as well as meteorological observation data and cultivation management data of corresponding time periods to obtain a historical disease database; performing multivariate statistical analysis processing on the three-dimensional meteorological monitoring network data and the historical disease database to obtain a disease risk assessment index; performing feature extraction processing on the disease risk assessment indexes through a deep learning algorithm to obtain disease early warning parameters; performing gridding partition processing on the disease early warning parameters to obtain partition early warning data; and performing dynamic optimization processing on the accuracy, the advance and the coverage rate of the partition early warning data to obtain accurate targeted early warning information of the meteorological disaster of the kiwifruit canker. According to the application, the accuracy and timeliness of the existing kiwi fruit canker meteorological disaster early warning method are improved.

A fuzzy cloud-based risk assessment method for reservoir operation schemes, including: collecting basic data of target reservoirs; constructing optimal operation model of conventional reservoirs to obtain model calculation results; acquiring change process of operation characteristics during whole operating period according to model calculation results, and constructing multi-level reservoir multi-dimensional risk assessment index system; dividing different risk level intervals according to multi-level reservoir multi-dimensional risk assessment index system; obtaining large number of sample data xij of reservoir risk index under corresponding working conditions by multiple parallel calculations to conduct risk assessment; calculating cloud digital features of different indexes by inverse cloud generator; and weighting indexes in index layer and criterion layer by Analytic Hierarchy Process to obtain multi-dimensional risk assessment results. In order to promote sustainable development and utilization of water resources and enhance level of reservoir risk management, scientific evaluation of reservoir risk is conducted by comprehensively considering various uncertainties.

The invention provides a reservoir water ecological health diagnosis method and system, and relates to the technical field of reservoir ecological monitoring, and the technical scheme is characterized in that the method comprises the steps: obtaining water quality parameters, biological indexes and environmental data collected by distributed sensors in a reservoir; according to the water quality parameters, the biological indexes and the environmental data, generating reservoir ecological health assessment data through a data fusion algorithm; calculating a reservoir ecological health assessment index according to the reservoir ecological health assessment data; according to the reservoir ecological health assessment index, generating a reservoir ecological health state assessment result; judging whether the ecological state of the reservoir is abnormal or not according to the evaluation result of the ecological health state of the reservoir; and when the ecological state of the reservoir is abnormal, generating an early warning signal. The reservoir water ecological health diagnosis method and system provided by the invention have the advantages that multiple ecological pressure factors are comprehensively considered, and a complex water ecological system is rapidly and accurately evaluated.

The invention relates to the technical field of network security, in particular to a network security evaluation information processing method and system, and the method comprises the following steps: collecting configuration information and operation logs of a target computer system based on network security evaluation information, sorting data according to time information, and executing formatting and standardization processing, and generating an operation data processing result. According to the method, time sorting and formatting processing are carried out on data, data quality is optimized, security events are classified according to time information and severity, event frequency analysis is combined, threat identification accuracy is improved, damage risk levels of multiple security threat types are analyzed, and weights of security assessment indexes are adjusted, so that threat identification accuracy is improved. The response sensitivity and prediction capability of the evaluation model are enhanced, the risk of misjudgment is reduced, the adaptability and foresight of safety measures are improved, and it is ensured that the safety level evaluation accurately reflects the actual safety condition.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

The invention relates to an intelligent power security situation assessment method based on a multi-source sensor network, and the method comprises the steps: obtaining multi-dimensional data from a power system through the multi-source sensor network, and constructing an original data set containing time sequence features; preprocessing and denoising the original data set, and extracting dynamic change characteristics of key parameters to obtain a cleaned parameter change sequence; based on the cleaned parameter change sequence, analyzing an interaction relationship among the key parameters, and obtaining spectrum feature data; judging whether a resonance risk exists in the system or not according to the frequency spectrum characteristic data in combination with a resonance phenomenon theoretical model, and generating a risk assessment report; for the risk assessment report, identifying unstable factors and extracting a high-risk parameter combination, and generating unstable factor distribution feature data; and according to the system stability evaluation index, judging whether the power grid operation state is close to an unstable boundary, and generating a risk early warning signal and a parameter adjustment suggestion.

The invention discloses a textile fabric defect detection system, relates to the technical field of textile fabric defect detection, and is beneficial to comprehensively evaluating the appearance quality of textile fabrics by collecting the number of hair balls, a color difference value and the area of a color difference region in an external defect parameter collection process. The pilling phenomenon on the surface of the textile fabric can be accurately recognized by collecting the number of the hair balls, the overall appearance and the service life of the textile fabric can be affected by the hair balls, non-uniformity of the textile fabric in the dyeing process can be revealed through measurement of the color difference value, and the severity degree of the color difference problem can be determined through statistics of the area of the color difference region. In the external defect evaluation process, whether the textile fabric meets the quality standard or not can be objectively judged by analyzing the external defect evaluation index of the specified textile fabric, and in the structural defect analysis process, the structural performance of the textile fabric can be comprehensively known by collecting the fiber breaking strength, the fiber density and the porosity.

The invention belongs to the technical field of agricultural sensing data management and control, and particularly relates to a vision-based multi-modal data fusion intelligent monitoring culture method and system, and the method comprises the steps: obtaining the growth image data and environmental parameter data of a strain based on a multi-sensor information source; identifying the growth image data through a deep learning algorithm, and outputting an image identification result; performing fusion processing on the image recognition result and the environment parameter data through a multi-data fusion algorithm to generate a comprehensive environment evaluation index; importing the comprehensive environment evaluation index and the abnormal state information into a growth environment control model, generating an environment regulation and control instruction conforming to a preset strain growth index, and executing the environment regulation and control instruction through an environment regulation and control module; the system can be used for realizing comprehensive environment monitoring, accurately evaluating the strain growth environment condition, and avoiding errors and one-sidedness caused by a single data source, so that culture environment parameters are dynamically adjusted.

The invention provides a comprehensive evaluation method and system for the remediation effect of heavy metal contaminated soil, and relates to the technical field of environmental engineering.The method specifically comprises the steps that remote sensing data of a contaminated area is obtained, a soil sample is collected, the microbial respiration rate and the soil physicochemical property of the soil sample are measured, the remote sensing data are processed, and a normalized vegetation index is obtained; generating a soil health assessment index based on the collected physical and chemical properties of the soil; integrating the soil evaluation index, the microbial respiration rate and the normalized vegetation index to construct a soil remediation evaluation index; and comparing the remediation evaluation index with a preset evaluation threshold value, and evaluating the remediation effect of the heavy metal contaminated soil. According to the method, the soil health evaluation index and the remediation evaluation index are constructed, so that the evaluation accuracy of the remediation effect of the heavy metal contaminated soil is effectively improved. Through multivariate correlation analysis, biological, chemical and physical characteristics of the soil can be comprehensively reflected, and a scientific basis is provided for subsequent management and remediation measures.

The embodiment of the invention provides a self-adaptive retail behavior recognition model optimization method for edge calculation, and the method comprises the steps: building a weight evaluation matrix based on an initial retail behavior recognition model which comprises three behavior recognition tasks: commodity interaction, settlement operation and customer moving line; calculating the importance score of the weight of each layer of neurons by using an L2 norm, generating a multi-dimensional evaluation index in combination with the importance score and the interlayer connection density, and setting a self-adaptive pruning threshold according to the evaluation index; collecting real-time retail scene data at an edge device, extracting commodity operation, customer movement track and shopping behavior time sequence features to construct a sample cache pool, calculating feature importance based on the sample cache pool, and performing selective online updating on a quantitative model; according to the method and the device, the operation efficiency and the recognition accuracy of the system on the edge equipment can be improved by constructing an intelligent model optimization and online updating framework.

The invention provides an active transformer area voltage out-of-limit early warning and photovoltaic bearing capacity evaluation method, which comprises the following steps: acquiring multi-source data of a power distribution transformer area, and generating a standardized multi-source time sequence data set through time alignment processing, data quality evaluation and feature fusion; extracting voltage out-of-limit features based on the time sequence similarity matrix and the node influence matrix; the periodicity, volatility and randomness characteristics of photovoltaic output are analyzed, a coupling relation matrix and a sensitivity vector are established, and an influence evaluation model is constructed; performing out-of-limit early warning based on an early warning index system and a deep learning model; and establishing a multi-dimensional evaluation index system for carrying out bearing capacity evaluation in combination with power flow analysis and the dynamic influence factors. Through multi-source data fusion and multi-dimensional feature analysis, the accuracy of voltage out-of-limit early warning is improved, accurate evaluation of the photovoltaic bearing capacity is realized, and effective technical support is provided for safe operation of the power distribution network.

The invention relates to the technical field of shipping management, in particular to a shipping risk management method and system based on big data, and the method comprises the steps: obtaining ships in a target sea area, collecting the driving route and operation data of each target ship, and obtaining the operation abnormality evaluation index of each target ship through processing; obtaining the navigation speed of each target ship, dividing the driving route of each target ship, collecting the environment data of each navigation road section of each target ship, and obtaining the environment abnormal characteristic value of each target ship through processing; and carrying out risk assessment on the operation of each target ship according to the environment abnormity characteristic value and the operation abnormity assessment index of each target ship, obtaining an assessment result and carrying out feedback. According to the big data-based shipping risk management method and system provided by the invention, the environmental factors and the ship operation condition are comprehensively considered, and the composite factors possibly causing the risk can be more accurately identified, so that the accuracy of risk assessment is improved.

The invention relates to the technical field of engineering cost management, in particular to an artificial intelligence-based cost whole-process management method and system, and the method comprises the steps: collecting environment data, market data and project data, and carrying out the feature extraction; calculating the weight of each modal data through a self-attention mechanism, and obtaining a fused feature vector; the knowledge graph, the graph convolutional network and TransE are combined to generate a knowledge graph feature vector, a self-adaptive cost prediction model is constructed, and a prediction cost is output and implemented; fusing a graph attention network, a structure causal model and an improved fuzzy comprehensive evaluation method, constructing a risk assessment model, outputting a risk grade score, and automatically generating and adjusting an optimal risk coping strategy; constructing a low-code intelligent collaboration platform; and based on an evaluation index result, optimizing model parameters and platform configuration by using a genetic algorithm. According to the invention, the risk factors are accurately analyzed through the risk assessment model based on fusion of multiple technologies, and the assessment accuracy is improved.

The invention relates to the technical field of risk management, in particular to a multi-scale urban rainstorm flood risk assessment method. The method comprises the following steps: acquiring a regional basic data set; performing data preprocessing on the regional basic data set to generate a regional basic preprocessed data set; constructing a rainstorm flood risk assessment index system based on the regional basic preprocessing data set to obtain an urban scale flood assessment index, and performing urban scale rainstorm flood risk assessment to obtain an urban scale rainstorm flood risk assessment result; according to the method, the index information is obtained from the open source data, and the defects that in previous researches, part of index data is poor in spatial precision and long in time are overcome; the multi-scale urban rainstorm flood risk assessment method comprehensively considering the urban scale and the block scale is provided, and the accuracy, scientificity and comprehensiveness of urban rainstorm flood risk assessment can be improved.

The invention relates to an urban park physical examination evaluation system construction method based on a machine learning algorithm. The method comprises the following steps: S1) selecting an evaluation index set; s2) taking a park system, an urban comprehensive park, an urban community park and an urban micro-greenbelt collaborative group as evaluation scales, and selecting evaluation indexes from the evaluation index set; s3) acquiring specific data of evaluation indexes to form an index data set; s4) screening repeated indexes with complex collinear features in all index data sets by using a variance expansion coefficient; s5) clustering by using a K-medoids clustering algorithm, and S6) based on an SHAP machine learning algorithm, performing full sample analysis and outputting the importance degree of the index factors, and constructing an urban park physical examination assessment system based on the importance degree of the index factors.According to the method, subjectivity and uncertainty of a traditional assessment method can be avoided, the assessment efficiency is improved, and rapid updating can be performed.

The invention relates to the field of traffic safety and risk assessment, in particular to a multi-element multi-scale highway traffic operation risk assessment method and device, and the method comprises the following steps: S1, determining the assessment index of each risk factor through recognizing the risk factors of highway traffic operation; s2, calculating a subjective weight and an objective weight of the evaluation index, and calculating an optimal combination weight of the evaluation index according to the subjective weight and the objective weight; s3, according to the optimal combination weight, calculating comprehensive determinacy of road traffic operation under different risk levels; and S4, comparing the comprehensive certainty degrees under different risk levels, wherein the risk level with the maximum comprehensive certainty degree is the risk level of road traffic operation. According to the method, through the steps of risk factor identification, weight calibration, cloud model analysis, risk analysis model establishment and the like, multi-dimensional information of various influence factors is fused, comprehensive risk assessment is carried out, and the influence of various risk factors on highway traffic operation can be systematically identified and quantified.

The invention relates to a carbon dioxide salt water layer geological storage site selection method and system, and belongs to the technical field of carbon dioxide energy conservation and emission reduction. The site selection method comprises the following steps: acquiring logging data and a geological data set of a target area, and identifying a potential salt water interval based on a preset rule; grading quantitative evaluation is carried out according to the identified potential salt water interval, and a corresponding evaluation index system is established; wherein the evaluation index system comprises a plurality of first-level evaluation indexes and a plurality of second-level evaluation indexes corresponding to each first-level evaluation index, and the first-level evaluation indexes comprise geological sequestration condition evaluation, sequestration capability condition evaluation and social environment condition evaluation; carrying out weight assignment on the evaluation indexes in the evaluation index system based on an index weight assignment method, and carrying out comprehensive quantitative evaluation to obtain a comprehensive evaluation score of each candidate target region; and taking the candidate target region with the highest comprehensive evaluation score as a storage site for geological storage of the carbon dioxide saline water layer. According to the invention, the scientificity and reliability of storage site selection can be improved.

The invention discloses a coal-fired power generation unit operation optimization method and system based on industrial big data, and relates to the technical field of industries. A starting priority analysis module dynamically adjusts a unit starting sequence and obtains unit starting priorities by analyzing real-time data sets and power grid load requirements; the system can quickly judge the starting sequence of the units according to the numerical values and effectively recognize the units with possible fault risks, and the fault recognition module performs fault assessment on the unit operation state and equipment state data in combination with the real-time data set and the historical data set and calculates the current fault assessment index. Through comprehensive analysis of a historical trend and a real-time state, the system can accurately judge whether maintenance operation needs to be carried out on a unit or not, so that early prediction and timely intervention of potential faults are realized, an effect feedback and optimization module evaluates feedback data of an actual operation effect, constructs a performance feedback index, and improves the maintenance efficiency of the unit. And dynamic adjustment and optimization of unit load distribution are realized.

The invention provides an equipment state data analysis method and system applied to automated electrical equipment, and the method comprises the steps: obtaining a historical operation data set of target equipment, and constructing a multi-level state analysis model based on the historical operation data set, the multi-level state analysis model comprises at least one first analysis level and at least one second analysis level, key operation parameters are extracted from the real-time operation data flow of the target equipment through an operation parameter screening strategy in the first analysis level, and a parameter screening result is generated according to the key operation parameters; inputting a parameter screening result into a state evaluation strategy in a second analysis hierarchy, and generating a state evaluation index of the target equipment in combination with a dynamic threshold range corresponding to the key operation parameter; and outputting a real-time operation state grade of the target equipment according to a comparison result of the state evaluation index and a preset state threshold value. According to the invention, efficient monitoring and accurate early warning of the equipment health state can be realized.