106 results about "Expert evaluation" patented technology

The invention discloses an extreme rainstorm disaster urban waterlogging point early warning method and system based on hybrid intelligence. The method comprises the following steps: collecting waterlogging points of which the waterlogging risk needs to be assessed in a current city; performing risk prediction on each waterlogging point to obtain a machine score by adopting a machine learning model and based on the waterlogging-related multi-source heterogeneous data; according to the performance measurement of the machine learning model, generating the confidence coefficient of the machine score of each waterlogging point; performing risk assessment on the waterlogging point set based on expert experience to obtain an expert score of each waterlogging point; based on the evaluation consensus degree of the expert individuals and the expert groups, calculating the confidence coefficient of expert scoring of each waterlogging point; corresponding weights are dynamically optimized and generated according to confidence coefficients of scoring of machines and experts of the waterlogging points; and finally, according to the man-machine score and the respective weight, performing fusion calculation to obtain a waterlogging risk value of each waterlogging point, and further providing risk early warning. According to the method, the comprehensiveness and robustness of waterlogging point early warning under the extreme rainstorm are improved through dynamic weight optimization and man-machine cooperation.

The invention discloses an individual health risk dynamic assessment system based on multi-modal biological feature fusion, and relates to the technical field of medical health information processing. The method comprises the following steps: constructing a high / low-dimension data combination rule; dynamically adjusting the feature fusion weight according to the clinical feature importance weight and the medical efficiency value calculated by the detection time interval; a cost difference value formula is introduced to quantify the economical efficiency of combined evaluation and independent evaluation; calculating a health risk comprehensive coefficient of the monitoring scene by combining the health data credibility score and the index type / amplitude / duration weighted risk value; the risk level is rechecked through expert assessment or a secondary model, so that the assessment accuracy is ensured; updating the individual credibility score according to the evaluation result; and for the result exceeding the confidence interval, classifying and optimizing a feature fusion algorithm, a model parameter or a data combination strategy. The system realizes accurate, economic and real-time individual health risk dynamic assessment, and is suitable for scenes of chronic disease management, health intervention and the like.

The invention discloses an initiating explosive device processing safety assessment and early warning method based on a Noisy-OR Bayesian network. According to the method, a Noisy-OR model and a Bayesian network are combined, and an initiating explosive device processing process safety evaluation model is constructed. According to the method, a Noisy-OR model can be used for carrying out quantitative processing on expert evaluation information endowed with weight, a corresponding CPT (conditional probability table) is generated, and the risk probability of the initiating explosive device processing process is generated through a Bayesian network by combining real-time monitoring data of a dangerous point and simulation data of an initiating explosive device processing finite element model through a multi-source information fusion method. According to the method, subjectivity of traditional initiating explosive device processing safety evaluation and singleness of a safety monitoring method are avoided, the Noisy-OR model and the Bayesian network are fused and applied to the field of initiating explosive device processing safety monitoring, the problems that safety evaluation is not objective and not real-time, and the monitoring method is local are solved, and the safety of initiating explosive device processing is improved. The method has a practical guiding significance for monitoring the actual processing safety of the initiating explosive device.

The invention relates to the technical field of traditional Chinese medicine preparation, in particular to an optimization method and system for a stir-frying process of traditional Chinese medicine decoction pieces. Acquiring formula information and process requirements of the target traditional Chinese medicine decoction pieces; retrieving a stir-frying process scheme of the similar traditional Chinese medicine decoction pieces from the traditional Chinese medicine stir-frying expert knowledge base; fusing the stir-frying process scheme to generate initial parameters; performing a first round of stir-frying; collecting process data through a multi-mode sensor; an expert evaluates and provides an adjustment suggestion; generating a parameter adjustment scheme in combination with expert suggestions and process data; executing multiple rounds of interactive optimization; and recording the optimal frying process parameters and storing the optimal frying process parameters in a knowledge base. A closed-loop optimization system is formed by constructing a knowledge retrieval mechanism driven by formula information, a multi-modal sensing monitoring system, a parameter adjustment strategy combining expert evaluation and data analysis and an interactive iterative optimization framework. The technical problems of difficulty in knowledge reuse, single process monitoring, lack of system support for parameter adjustment, insufficient man-machine collaboration and the like in the traditional Chinese medicine stir-frying process are solved.

The invention discloses a method for establishing a risk management model of a hydrogen-doped natural gas pipeline. Firstly, a Bow-Tie model system is used for analyzing a failure reason and an accident consequence development scene of the hydrogen-doped natural gas pipeline; then, the model is mapped into a Bayesian network by utilizing GeNIe software; and calculating the prior probability of the basic event through an expert evaluation method and a fuzzy set theory. Setting a conditional probability table of the Bayesian network by using a fault tree mapping method and a historical data calculation method; on the basis, simulating an equipment component failure process through a Markov model, introducing hydrogen corrosion and hydrogen-assisted fatigue crack propagation models to carry out hydrogen-related dynamic analysis, and finishing parameter setting of a dynamic Bayesian network; and finally, calculating the failure probability of the hydrogen-doped natural gas pipeline and a potential life loss result when an accident occurs in combination with a model output result. The method adopts the dynamic Bayesian network to predict the pipeline failure probability, is more in line with the actual operation condition, and can be widely applied to the risk management process of the hydrogen-doped natural gas pipeline.

The invention relates to the field of data processing, and provides a software cost method and system based on dynamic configuration and hierarchical analysis. The method comprises the following steps: dynamically loading corresponding configuration parameters and algorithms by evaluating a method identification field; constructing a standardized cost parameter configuration system, including configuring an estimated function point reference value, an estimated function point weight coefficient and a scale adjustment factor; based on the evaluation method, a tree budget configuration table is preset, hierarchical node design is adopted, a budget tree is constructed, and a tree budget configuration and cost declaration / evaluation header is formed; transferring the configuration right of the tree budget configuration and the cost declaration / evaluation header to the back end, so that the back end dynamically loads a preset header template according to the enabled cost standard, and synchronously injects field attribute rules and calculation formula metadata; obtaining an unstructured document uploaded by a user, and analyzing the unstructured document and the header template by adopting a large language model to obtain software cost details; and generating an evaluation report through the expert review software cost details.

The invention discloses a medicine cold-chain logistics enterprise recommendation method based on a Fermat fuzzy set. The method comprises the steps of obtaining a transportation quality data set of a plurality of alternative logistics enterprises under evaluation indexes; constructing an expert evaluation matrix through the Fermat fuzzy linguistic variables, and evaluating the importance of experts through the Fermat fuzzy linguistic variables so as to determine the weight of each expert; giving a language evaluation matrix through a Fermat fuzzy language variable and converting the language evaluation matrix into a Fermat fuzzy number; obtaining a Fermat fuzzy group evaluation matrix based on the expert weight and a Fermat fuzzy Einstein interactive integration operator, and performing standardization; based on a Fermat fuzzy set score function and an Einstein interactive integration operator, objective and subjective weights of evaluation indexes are obtained through a Fermat fuzzy variable coefficient method and a Fermat fuzzy step-by-step weight evaluation ratio analysis method respectively, and then a comprehensive index weight is obtained; and according to a Fermat fuzzy set-based scoring function and an Einstein integration operator, obtaining a sequence through an improved comprehensive compromise solution method so as to determine an optimal enterprise.

The embodiment of the invention provides a medicine evaluation method and related equipment, and belongs to the technical field of information processing. The method comprises the following steps: acquiring medicine input information corresponding to a first medicine; the first medicine is any medicine, and the medicine input information comprises medicine type data; generating a corresponding medicine evaluation template according to medicine type data in the medicine input information; collecting expert evaluation information corresponding to the first drug through the drug evaluation template; the expert evaluation information comprises text data and score data; and performing text analysis on the text data corresponding to the first drug through the drug evaluation model to generate a model evaluation score, and integrating the model evaluation score and the score data to obtain a target evaluation result corresponding to the first drug. According to the embodiment of the invention, the consistency and objectivity of the drug evaluation result can be remarkably improved, so that more comprehensive and intelligent decision support is provided for drug selection of medical institutions.

The invention discloses an expert review componentization system and a review method, belongs to the technical field of software systems, and aims to solve the technical problems of poor universality, insufficient intelligence and rigid process in the process of business declaration and qualification affirmation. Comprises: a component library configuration module for constructing a standardized review component and constructing a component library based on the review component; the expert database configuration module is used for collecting review experts to construct an expert database according to the review requirements of each business scene; the review rule configuration module is used for pulling related review components from the component library according to review rules and constructing a review process based on the pulled review components; the expert extraction module is used for pulling related reviews from an expert database to construct a review expert group and setting expert review rules; the review analysis module executes a review task based on a review process, and summarizes review suggestions of review experts to form a review result; and the data storage module is used for storing the review rule, the review expert group and the review result.

The invention discloses an equipment failure risk analysis method based on a probability hesitant fuzzy evidence theory, and relates to the field of failure risk analysis, and the method comprises the steps: S1, building a fault tree structure, and constructing a reference failure rate set of basic events in a fault tree; s2, acquiring evaluation data of an expert on a reference failure rate set of the basic event, and obtaining an adjustment score and a tendency degree of the basic event; s3, converting the adjustment score into an evaluation probability by using a score conversion rule; s4, establishing a qualification weight according to the qualification of the expert; quantizing and adjusting the uncertainty of the score and the tendency degree to obtain an evaluation uncertainty weight, and carrying out weighted summation on the two weights to synthesize a total weight; s5, based on the evaluation probability and the total weight, synthesizing a final evaluation probability through a Dempster-Shafer evidence theory; and S6, performing fault tree analysis based on the final evaluation probability. According to the method, the probability hesitant fuzzy set and the Dempster-Shafer evidence theory are introduced, the diversity and uncertainty of expert evaluation are reserved, and conflicts and consistency between evidences are effectively processed.

This invention discloses a method and system for application design transformation based on technological experience, relating to the field of digital technology. The method includes: collecting open-source technology product data via the internet to construct a digital technology experience center; providing online technology experiences for students to obtain experience monitoring data; designing online courses based on a technology application creation platform to obtain online teaching courses; teaching students to obtain technology application concept design proposals; conducting simulation verification and evaluation through a mechanism simulation system to obtain simulation evaluation results for the design proposals; presenting the technology application concept design proposals in a roadshow to obtain a design demonstration dataset; conducting online expert evaluation; and transforming the technology application concept design proposals into innovative achievements based on the evaluation results. This addresses the technical problems of limited teaching resources, limited practical scenarios, and restricted technological innovation capabilities in existing science and technology education, achieving the technical effect of enriching technological experiences and enhancing technological innovation capabilities.

The invention discloses an intelligent operation and maintenance method of photo-thermal energy storage key equipment and a related device, and relates to the field of photo-thermal system risk management and control, and the method comprises the steps: obtaining a failure mode, a failure position, a failure reason and a failure result of groove type photo-thermal system key equipment; performing criticality analysis on the failure mode of each device to obtain risk factors of the failure mode of each device; determining the expert evaluation aggregation fuzzy number of each risk factor and the weight of each risk factor by adopting a Z-number improved AHP method; weighting the expert evaluation aggregation fuzzy number of each risk factor and the weight of each risk factor to obtain a risk priority number of the key equipment of the slot type photo-thermal system; sorting the risk priority numbers of the key equipment of the slot type photo-thermal system to obtain a failure mode risk sequence; and determining corresponding management and optimization measures based on the failure mode risk ranking. According to the invention, the safety risk management and control level of the photo-thermal system can be improved, so that the risk is stably controlled.

The invention relates to the technical field of power electronics, and discloses a state evaluation method based on combination of combined empowerment and weighted rank sum ratio, which is used for evaluating and analyzing the running state of a vacuum circuit breaker in a switch cabinet. In the evaluation method, four state indexes of vacuum characteristics, mechanical characteristics, electrical characteristics and temperature characteristics of the vacuum circuit breaker are selected for comprehensive evaluation. In order to improve the accuracy of evaluation, the invention provides a comprehensive evaluation method based on combination of combined weighting and weighted rank sum ratio. According to the method, a single weight method can be prevented from being influenced by subjective or objective factors, the evaluation model is simple and reliable, the possibility is provided for simultaneously detecting the states of a plurality of vacuum circuit breakers, and meanwhile, the results can be basically kept consistent with expert evaluation results. The detection means does not make up for the disadvantage that the measurement data is not utilized perfectly in the previous evaluation method, the measurement data information is utilized to a greater extent, and meanwhile, early warning can be carried out when the normal operation of the circuit breaker is not influenced by the fault.

The present invention discloses an access control authorization method based on fuzzy rough sets, which is used to optimize attributes and fine-grained access control. In this method, the value of the attribute required by the request is uncertain, and the attribute value is converted into a fuzzy membership value to achieve the conversion of traditional attributes to fuzzy attributes. The system introduces attribute simplification points, and through expert evaluation of the user's membership of authorization, the attribute value is simplified, and the membership relationship of the attribute is specified by upper and lower approximate values, and the possible membership relationship between the two is specified, thereby achieving efficient access authorization under massive data. At the same time, the user authorization level and resource sensitivity are used as judgment conditions to measure user access behavior, and the user authorization rights and resources are authorized by the definition of authorization hierarchy, thereby achieving fine-grained access control and meeting the security access requirements of resource access in practical applications.

The present application discloses a method and system for modeling the resources of complex electronic product instruments and equipment, which includes: using a Bayesian optimization method to weight the multi-dimensional indicators of instruments and equipment to construct a process weighted feature matrix; using an improved hybrid hierarchical clustering algorithm to generate a classification of instrument and equipment resources, and combining process expert evaluation to establish an instrument and equipment classification standard; based on the instrument and equipment classification standard, on the demand side, based on the demand for instrument and equipment resources of each process in the process flow, an instrument and equipment resource demand model is established, and each process is bound to a standardized resource classification. On the supply side, a production line resource capacity model library that conforms to the standardized resource classification is established; and resource scheduling is performed using a scheduling strategy that integrates the probability of resource start-up demand and physical distance. The present application improves the scheduling feasibility and accuracy of instruments and equipment, and improves the efficiency of instrument and equipment use.

The present application relates to expert feedback driven government procurement intelligent auxiliary bid evaluation system, including: tender document analysis module; bid document processing module; expert evaluation annotation module; and evaluation model and its optimization module; data intercommunication between tender document analysis module, bid document processing module, expert evaluation annotation module, evaluation model and its optimization module. The feedback mechanism of expert evaluation is emphasized, the close contact between expert experience and model optimization is further strengthened, the expert experience is effectively converted into model optimization power, the optimization process of the model is accelerated, the system can reach a high evaluation level in a relatively short time, and can continuously maintain the industry forefront, better meet the growing demand of government procurement business, help the procurement party to select better and more suitable suppliers, and realize efficient allocation of resources.

This invention provides an improved FMEA device that considers the relationship between uncertainty and failure modes. It features the following components: an SVTraNN scoring conversion module for obtaining the SVTraNN evaluation matrix; an expert weight calculation module for calculating expert weights; an expert evaluation summarization module for calculating the SVTraNN decision matrix; a subjective weight calculation module for calculating the final subjective weight of each risk factor; an objective weight calculation module for calculating the objective weight of each risk factor; a comprehensive weight calculation module for calculating the comprehensive weight of each risk factor; a comprehensive score calculation module for calculating the comprehensive score of each risk mode; a risk ranking module for obtaining the risk ranking result of each risk mode; and a risk level module for obtaining the risk level of each risk mode. In summary, this method can effectively assess the risk level of risk modes.

The embodiment of the invention relates to a processing method and device for performing brain efficacy prediction based on multi-dimensional indexes. The method comprises the following steps: setting a multi-dimensional index data set for brain efficacy prediction to obtain a corresponding multi-dimensional index set; constructing a deep learning model used for performing brain efficacy prediction as a corresponding brain efficacy prediction model; constructing a model data set and recording the model data set as a corresponding first data set in a manner of performing traditional Chinese and western medicine examination and brain efficacy expert evaluation on pre-recruited volunteer crowds; training a brain efficacy prediction model based on the first data set; and after the model training is finished, performing brain efficacy index prediction according to the multi-dimensional index set of any testee based on the brain efficacy prediction model. According to the invention, prediction efficiency and prediction accuracy can be improved.

The invention discloses a leg posture analysis and rating system for a football player in a ball kicking process, and the system comprises a data collection unit which collects four-stage kinematics parameters of kicking, kicking and stretching, backward swinging, forward swinging and ball touching through a three-dimensional motion capture system and a high-speed camera; the data processing unit is used for processing parameters and extracting characteristic parameters of each stage; the expert evaluation interface is used for receiving evaluation data of multiple experts on different ball kicking technology types on the multi-score influence factors; the weight calculation unit is used for calculating an influence factor attribute weight by using a Bayesian method based on expert data; the rating decision matrix construction unit is used for constructing a comprehensive matrix in combination with a weight rough projection method; the multi-angle analysis unit is used for performing multi-angle risk rating by fusing a rough set theory and an approximate ideal solution sorting method; and the result output unit is used for outputting the posture quality grade and the training suggestion. According to the method, the actual requirements of football training on precision, standardization and comprehensiveness of posture analysis can be met.

The present application relates to the technical field of traditional Chinese medicine preparation, and particularly relates to a method and system for optimizing a frying process of traditional Chinese medicine decoction pieces. Formula information and process requirements of target traditional Chinese medicine decoction pieces are obtained; a frying process scheme of similar traditional Chinese medicine decoction pieces is retrieved from a traditional Chinese medicine frying expert knowledge base; initial parameters are generated by fusing the frying process scheme; first-round frying is performed; process data are collected by a multi-modal sensor; adjustment suggestions are proposed by experts; parameter adjustment schemes are generated in combination with the expert suggestions and the process data; interactive optimization is performed for multiple rounds; and optimal frying process parameters are recorded and stored in the knowledge base. By constructing a formula information-driven knowledge retrieval mechanism, a multi-modal sensing monitoring system, a parameter adjustment strategy combining expert evaluation and data analysis, and an interactive iterative optimization framework, a closed-loop optimization system is formed, and technical problems such as difficulty in knowledge reuse, single process monitoring, lack of systematic support for parameter adjustment, and insufficient human-machine cooperation in traditional Chinese medicine frying processes are solved.

The invention discloses an evaluation analysis method and system for review behaviors of scientific research project review experts. The evaluation analysis method and system cover data acquisition and grouping modeling, professional level and professional ability baseline determination, technology and management review ability evaluation, credit and review attitude evaluation, comprehensive score generation and output and the like. According to the method, the evaluation result distribution of the evaluated evaluation expert in different evaluation groups and the evaluation result deviation condition of the evaluated evaluation expert in the same evaluation group are comprehensively investigated, the score distribution degree and the score deviation degree are adopted as two core indexes, and the technical evaluation ability and the management evaluation ability of the evaluation expert are evaluated. On the basis, three first-level evaluation indexes including professional ability, credit evaluation and review attitude are adopted, and objective quantitative evaluation is carried out on the evaluated experts. The invention provides an objective and scientific evaluation expert evaluation method, also has the characteristic of convenient and efficient calculation, and is suitable for large-batch scientific research project evaluation expert evaluation management.

The invention provides a news pushing method and device, a computer program product and a news pushing system. The method comprises the following steps: extracting a plurality of initial news from a webpage; news in which the target object is interested is screened out from the multiple pieces of initial news, multiple pieces of target news are obtained, and the number of the target news is smaller than or equal to the number of the initial news; the target news is sorted according to the importance degree of the target news, the sorted target news is obtained, and the importance degree is the degree of the influence of the target news on the target object; the sorted first N pieces of target news are pushed to the target object, and N is larger than or equal to 1. According to the method, the problems that in the prior art, news screening and sorting depend on manual screening, expert evaluation or simple statistical analysis, obvious limitation and defects exist, and news cannot be accurately recommended to the user are solved.

The invention provides a hydroelectric power transmission project construction accident risk analysis method based on an RF-WA-PHFWFGSM operator, and the method comprises six core steps: data preprocessing, RF preliminary feature screening, improved expert simulation evaluation, WA-PHFWFGSM operator aggregation, comprehensive verification, and result output, and each step is progressive layer by layer to form a complete risk analysis closed loop of data, features, evaluation, verification, and application. Aiming at the problems of expert evaluation hesitation and information uncertainty, a probability hesitation fuzzy set (PHFS) is used for processing qualitative attribute feature evaluation, expert hesitation attitudes and fuzzy information are completely reserved through language terms and probability distribution and quantification of expert opinions, information loss caused by the fact that a traditional method is simplified into a determined numerical value is effectively avoided, and the method has the advantages of being high in accuracy and high in reliability. And the authenticity and integrity of expert evaluation results are improved.

The present application relates to a multi-modal data inconsistency detection and cleaning method and system based on adaptive energy guidance, belonging to the field of artificial intelligence and data mining technology. The present application proposes a multi-modal data inconsistency detection method based on adaptive energy guidance. First, a multi-expert uncertainty estimation framework is constructed to effectively mine multi-dimensional modal semantic correlation information such as prediction uncertainty, cross-modal consistency and teacher-student difference. Second, according to the multi-expert evaluation signal, an adaptive energy scoring mechanism is designed to effectively aggregate and quantify the sample modal consistency by constructing a comprehensive energy function and a weighted function based on Boltzmann distribution. Finally, a dynamic data cleaning and screening strategy based on energy score is constructed to improve the quality of the data set and the robustness of the downstream model, and better achieve the purpose of data service.

The invention discloses an old-age care case matching method based on big data processing, and the method comprises the following steps: collecting and integrating the care case data of the elder of a nursing home, and constructing a multi-dimensional information set; performing standardized field mapping and time serialization processing on the data, and unifying the data into a structured data set; performing text analysis based on the medical subject word bank to generate a searchable semantic tag set; inputting the structured data set and the semantic tag set into a TabTransform network based on an attention mechanism, extracting interaction features between fields, and generating a fusion feature vector; similarity retrieval is achieved through FAISS, and a similarity ranking candidate case set is output; and in combination with the nursing effect and expert evaluation, dynamically marking high-quality cases to form reusable nursing scheme resources. According to the invention, rapid matching of nursing cases and intelligent recommendation of high-quality schemes can be realized.

The application provides a non-intrusive experimental evaluation system and method for pilot human-machine trust behavior, and relates to the technical field of human-computer interaction, which comprises the following steps: collecting a plurality of original data points including eye movement trajectory data points, operation instruction data points and flight instrument parameter data points of a pilot to form an objective behavior data set; receiving a plurality of subjective evaluation data points of the pilot based on the objective behavior data set to form a subjective evaluation data set; receiving a plurality of expert evaluation data points of a flight expert according to the subjective evaluation data set and the objective behavior data set to form an expert evaluation data set; and synchronously matching each data point in the objective behavior data set, the subjective evaluation data set and the expert evaluation data set according to a flight task time node to generate a multi-dimensional behavior mapping data set with time corresponding relationship. The application can realize accurate identification, dynamic evaluation and quantitative analysis of pilot human-machine trust behavior.

The application discloses a medical service process iterative decision method and system based on multi-source feedback information, and the method steps comprise the following steps: in step S01, multi-source feedback information of each link of a medical service process is acquired, and service satisfaction scores and service quality scores are obtained; in step S02, the medical service process is modeled as a dynamic Bayesian network model, an emotion value after state transition is calculated by using an emotion transmission function, and a quantitative transmission effect is obtained by using an emotion transmission coefficient, and a time sequence cascade model is trained; in step S03, according to the deviation between expert evaluation benchmark data and the satisfaction scores, the feedback information weight is adjusted; in step S04, a multi-objective optimization function is iteratively solved, and in the solving process, the time sequence cascade model is used to predict the satisfaction change under different medical service processes, and an optimal medical service process scheme is obtained. The application can optimize the service efficiency and resource utilization rate of medical services, and improve the medical experience of patients.

A supply chain risk identification method, system, device and medium belong to the technical field of supply chain risk management. The risk identification method comprises: acquiring characteristic information of a supply chain risk event, the characteristic information of the supply chain risk event comprising event fuzzy description and corresponding parameter information; the parameter information comprising expert evaluation data and supply chain platform operation data; inputting the event fuzzy description into a supply chain language large model for matching processing, and outputting a plurality of risk index items corresponding to the event fuzzy description, the risk index items comprising events affecting supply chain capacity or transportation; and determining the risk levels of the plurality of risk index items corresponding to the parameter information. The risk identification method can improve the supply chain risk identification capability, make it possible to comprehensively perceive multi-source heterogeneous risk factors in the whole chain range and early warn key node risks, and realize dynamic monitoring and accurate identification of complex equipment supply chain operation risks.

The application discloses a power cable line degradation diagnosis system and method using a database sample, and the method comprises the following steps: 1) extracting the harmonic current characteristics of the sample cable, marking the harmonic current characteristics with expert evaluation data of the sample cable as labels, and dividing the set of the marked harmonic current characteristics into a training set and a test set; 2) constructing a convolutional neural network corresponding to the expert evaluation data, wherein the convolutional neural network corresponding to the expert evaluation data comprises an input layer, a plurality of convolutional layers, a maximum value pooling layer and an output layer; 3) inputting the training set into the convolutional neural network corresponding to the expert evaluation data to obtain a target model; and 4) collecting the harmonic current characteristics of a cable to be diagnosed and inputting the harmonic current characteristics into the target model to obtain the degradation degree of the cable to be diagnosed. According to the embodiment of the application, the cable circuit insulation degradation analysis can be performed without power interruption.

This invention discloses a digital evaluation method for the sensory quality of formulated products, comprising: establishing a training sample matrix X using measurement data of m formulated products as training samples; establishing an expert sensory evaluation vector Y by combining expert evaluation results of the training samples; and setting the test samples x... m+1 Add the training sample matrix X to form set X (m+1) The number of sensory indicators in the expert sensory evaluation vector Y is used as the number of test samples x. m+1 Assign the same number of sensory indicators; based on set X (m+1) The distance between samples is used to express the distributional differences between samples using a similarity metric. Based on the distributional differences between the test sample and the training sample, the sensory index evaluation value of the training sample with the highest distributional similarity is selected to predict the test sample x. m+1 The sensory quality assessment method of this invention is simple and effective, and the assessment results are not affected by subjective human factors, thus having high reliability.