883 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.

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

The invention 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 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 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 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 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 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.

The invention relates to the field of security inspection management, and particularly discloses a public place security inspection management system based on Internet of Things, which is characterized in that a plurality of fixed inspection nodes are arranged in a key inspection area according to public place types and layout identification, and the rationality of the arrangement of the fixed inspection nodes is ensured; each to-be-selected inspection route of the inspection equipment is formulated according to a fixed inspection node, a historical emergency location and current pedestrian flow distribution, and an optimal inspection route is screened according to an inspection coverage degree and an inspection equipment energy consumption evaluation index, so that the dynamic planning and adjustment of the inspection route of the inspection equipment are realized, and the inspection efficiency is improved; according to the method, video data and measurement data collected by fixed inspection nodes and inspection equipment are extracted, hidden danger points in a public place are identified and safety risk levels are evaluated and corresponding early warning is performed in combination with a safety event sample map library and an environment parameter range, multi-source data fusion is realized, and the judgment precision of safety risks in a complex scene is improved.

The invention discloses a power distribution network morphological characteristic evaluation index weighting method based on a combined weight, and relates to the technical field of power distribution network morphological characteristic evaluation, and the method comprises the steps: building an evaluation index system, and defining a power distribution network efficiency index and a source network load storage interaction index framework; performing weight distribution on the evaluation indexes through subjective and objective weight calculation; and the subjective and objective combination weight is optimized according to the game theory, and the optimal index system combination weight is obtained. According to the method, through establishing an evaluation index system, defining a power distribution network efficiency index and a source network load storage interaction index framework and refining a multi-level evaluation standard, evaluation indexes can comprehensively reflect the operation state of the power distribution network, through subjective and objective weight calculation, weight distribution is carried out on the evaluation indexes, and the evaluation efficiency of the power distribution network is improved. The scientificity and balance of the evaluation result are ensured, and dynamic balance between different weight sources is realized by optimizing subjective and objective combination weights through the game theory.

The invention relates to the technical field of terrain wind resource assessment, in particular to a complex terrain wind resource assessment method and system based on CFD enhancement, and the method comprises the steps: processing terrain, weather and vegetation data, and generating a complex terrain feature vector; constructing bidirectional interaction between a mesoscale meteorological mode and computational fluid dynamics, and iteratively optimizing a flow field; fluid dynamic parameters are calculated through reinforcement learning automatic optimization; turbulence simulation is enhanced by combining the generative adversarial network with large eddy simulation; dynamically allocating resolution according to the grid priority of the multi-feature calculation; the lightweight model pre-judges errors, and after the errors reach the standard, evaluation indexes are calculated to complete wind resource evaluation; the system comprises a multi-source data fusion module, a dynamic coupling module, a parameter optimization module, a reinforced turbulence simulation module, an adaptive grid module and an error prediction and evaluation module, and all the modules cooperate to achieve full-process evaluation. The problems that in the prior art, one-way coupling precision is low, manual parameter selection is low in efficiency, turbulence simulation precision and efficiency are difficult to balance, and static grids are wasted are solved.

The invention discloses a digital twinning-based high-altitude falling risk state assessment technology for building workers and a mapping method and system. The method comprises the following steps: extracting aqueduct building engineering site space element information and personnel positions by an IFC model, and respectively inputting the information and the positions into an indoor navigation model and a three-dimensional digital model; historical risk data of an aqueduct construction engineering site are collected to analyze a risk assessment index and a preset risk threshold value; constructing a digital twinborn model according to the risk assessment index, the risk threshold value, the indoor navigation model and the three-dimensional digital model; real-time construction site environment data, worker behavior data and equipment state data are collected to update and simulate the digital twin model in real time; evaluating the risk range of the high-altitude falling risk by combining the updated and simulated results with a risk evaluation algorithm; and performing mapping association on the digital twinborn model through the construction site environment data, the worker behavior data and the risk range, performing visual interface presentation on the mapping association part, and designing a risk early warning trigger threshold.

The invention discloses a virtual power plant and power grid collaborative security risk assessment method and system. The method comprises the steps of determining a virtual power plant and power grid collaborative security risk assessment index; carrying out quantitative analysis on the safety risk assessment indexes, and determining the weight of each safety risk assessment index; according to constraint conditions during cooperative operation of the virtual power plant and the power grid, carrying out assessment calculation on the resource vulnerability of the demand side at a node level; determining a plurality of demand side resource combinations according to a demand side resource vulnerability evaluation result; and setting different scenes when the virtual power plant and the power grid cooperatively operate, calculating the security risk assessment index of each scene, and calculating a corresponding security risk value according to the weight of each security risk assessment index. According to the method, the vulnerability of the demand side resources is considered in the node level, the security risk of cooperation of the virtual power plant and the power grid in various scenes is considered in the global level, and the comprehensiveness and accuracy of risk assessment can be improved.

The invention relates to the technical field of data processing, and discloses an intelligent agent evaluation method and system, and the method comprises the steps: analyzing key factors and performance indexes of an intelligent agent in different combat environments, determining evaluation indexes, and constructing an evaluation index system; setting parameters and algorithms of the evaluation model, and configuring an association relationship between the evaluation model and the evaluation index system; gaming deduction data and agent decision data are collected, and evaluation calculation data are generated and stored; in response to the evaluation calculation data, configuring evaluation calculation parameters, calling the evaluation model to calculate the evaluation calculation data, and recording an evaluation calculation result; and in response to the evaluation calculation result, generating an evaluation report framework, integrating the evaluation calculation data and the corresponding evaluation calculation result into the evaluation report framework in a visual form, and outputting an evaluation report. According to the invention, standardization and systematization of the evaluation process are realized, scientificity and accuracy of evaluation are improved, and efficiency and accuracy of data processing are improved.

The invention discloses a multi-dimensional evaluation method for credential environment migration, and particularly relates to the field of evaluation. According to the method, an evaluation index system covering infrastructure, an application system, data resources and security compliance is constructed, and comprehensive scanning is realized through instruction set detection, binary analysis and equal security compliance inspection; dynamically calculating the weight by adopting an AHP-entropy weight hybrid model, and triggering weight rebalance based on the volatility; through combination of static scanning and a dynamic probe, the compatibility, performance and safety differences are quantitatively analyzed; and finally, generating a visual risk assessment report, integrating data of each stage to calculate a compatibility score, a performance loss rate and a security level, providing instruction level difference comparison, a performance flame graph and a vulnerability attack path interaction view, and outputting a migration suggestion comprising a priority reconstruction list, an NEON instruction optimization scheme and a staged reinforcement plan.

The invention discloses a virtual power plant distributed resource aggregation response capability assessment method, which comprises the steps of selecting a resource response capability assessment index, performing response capability assessment on various distributed resources in a virtual power plant, and constructing various distributed resource response capability models; constructing a virtual power plant aggregation response capability evaluation model according to various distributed resource response capability models and aggregation response capability evaluation parameters; and carrying out weight assignment on the resource response capability indexes in the virtual power plant, and obtaining an aggregation response capability score by considering a conditional value-at-risk. According to the method, various distributed resource response capability indexes are defined, and the independent response capability of various distributed resources and the response capability of the aggregated virtual power plant can be evaluated, so that the aggregation response capability of the virtual power plant is evaluated, and participation in a power system regulation and control decision is assisted.

The invention relates to the technical field of tax data security, and discloses a data sharing privacy protection platform responding to tax digital requirements. The platform comprises a tax data privacy perception layer, a sensitive information positioning mapping layer, a cross-source data security fusion layer, a compliance sharing decision-making layer and an execution state feedback layer. The tax data privacy perception layer obtains multi-source tax information based on a dynamic differential privacy mechanism, generates an initial privacy risk heat map and segments a sensitive area; the sensitive information positioning mapping layer generates a data internal privacy distribution stereogram through a security perception array and time sequence analysis; the cross-source data security fusion layer establishes a space-time mapping relation and generates a privacy disclosure risk score; the compliance sharing decision-making layer converts the score into an executable sharing instruction; and the execution state feedback layer monitors data change, generates an evaluation index and dynamically adjusts a privacy protection strategy. According to the platform, the privacy protection of the whole tax data sharing process is realized, and the accuracy and dynamism of risk identification and strategy adaptation are improved.

The invention relates to a noise damping control system of an offshore wind turbine generator, and belongs to the technical field of offshore wind power generation. The coupling state evaluation module is used for determining a coupling state evaluation index through a preset deep neural network model based on the platform motion information, the vibration spectrum data and the sound pressure signal; the cooperative control strategy generation module is used for determining target rigidity and target damping according to the coupling state evaluation index and the vibration spectrum data; according to the invention, the coupling state evaluation module processes platform motion information, vibration spectrum data and sound pressure signals acquired by the data acquisition module through a preset deep neural network model; the preset model is endowed with strong nonlinear fitting capability through offline supervised learning of massive historical working condition data; and an accurate and quantitative risk basis which cannot be reached by the prior art is provided for subsequent control decisions.