134 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 a multi-scheduling scheme decision-making method and device based on hesitant fuzzy information and a multi-attribute group decision-making theory in the technical field of power grid power generation optimization scheduling. The multi-scheduling scheme decision-making method comprises the following steps: determining a plurality of attribute indexes influencing a power grid power generation scheduling scheme according to the power grid power generation scheduling scheme; performing quantitative processing on each expert evaluation information matrix based on a preset computable hesitant fuzzy language term set to obtain a quantitative data decision matrix; calculating the positive separation degree and the negative separation degree of quantized data corresponding to each power grid power generation scheduling scheme based on the positive ideal point and the negative ideal point of each quantized data decision matrix; and calculating the progress of each power grid power generation scheduling scheme, and selecting the power grid power generation scheduling scheme corresponding to the maximum progress as the optimal scheduling scheme. The method has the advantages that the positive ideal solution and the negative ideal solution can be determined according to the group decision evaluation information, the relative schedule of each scheduling scheme is calculated, the evaluation scheduling schemes are sorted and preferentially selected, and the problem that the decision schemes are difficult to achieve consistency is effectively solved.

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 an equipment health degree assessment method for staged model deployment, and belongs to the technical field of specific calculation models, and the method comprises the steps: sequentially dividing a model deployment process into an initial stage, a middle stage and a later stage; for the initial stage, obtaining an equipment health degree score according to the collected sample data through an expert evaluation method, and setting an evaluation rule; in the middle stage, according to the collected sample data, an equipment health degree score is obtained through an evaluation rule, and the sample data is divided for artificial intelligence algorithm training; and for the later stage, obtaining an equipment health degree scoring model according to the artificial intelligence algorithm obtained by training. The staged model deployment strategy covers all stages of the equipment from no data to sufficient data, so that the continuity and availability of health degree assessment are ensured. High-quality sample data is provided for training of an artificial intelligence algorithm through an expert evaluation method, the model training quality and efficiency are improved, and the problem of high data dependence in the prior art is solved.

The invention relates to the technical field of sports event content processing, in particular to a sports event content comprehensive processing method based on artificial intelligence, which comprises the following steps: generating a plurality of popularity events from sports event content data propagated by a network through a popularity word mining model based on improved BERTopic; constructing a preheating degree event weight and a popularity index weight for the plurality of popularity indexes corresponding to the plurality of popularity events through an expert evaluation method; calculating and adjusting the weight of the preheating degree event according to the time sequence data of the multiple heat indexes through a correlation analysis model based on grey correlation analysis; and the evaluation matrix, the time sequence popularity event weight and the popularity index weight are used to determine a comprehensive sports event attention event through a popularity event judgment model based on a fuzzy evaluation method. According to the method and the device, the hot events of the sports events can be timely and accurately determined, and the content of the sports events can be spread and popularized more in accordance with audience interests.

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 relates to the field of unmanned aerial vehicles, and discloses a VIKOR-based unmanned / manned vehicle earthquake rescue scheme decision-making system and method, and the method comprises the steps: calculating the optimal evaluation value and the missing evaluation value of a selection scheme in an expert evaluation decision-making matrix, forming an aggregated evaluation decision-making matrix, carrying out the normalization processing of the aggregated evaluation decision-making matrix, and carrying out the calculation of the optimal evaluation value and the missing evaluation value of a selection scheme. Calculating a comprehensive regret perception utility value of each scheme, and forming a perception value matrix; a fusion weighting method is adopted, a subjective weighting method and an objective weighting method are combined, the weight of each attribute is determined, the subjective weight and the objective weight are fused through a multiplication normalization method, and the fusion weight of each attribute is obtained; using an improved VIKOR method to obtain an optimal scheme to a worst scheme; and outputting a final decision result. According to the method, the improved VIKOR method is used for sorting the evaluation decision results, and the optimal rescue scheme can be screened out more accurately.

The present application provides a posture correction training method and device for human-centered intelligent manufacturing. The method provided by the present application includes: obtaining target joint point coordinates, inputting the target joint point coordinates into a human digital model to obtain the correspondence between the target joint point coordinates and human joints; the human digital model is used to match the target joint point coordinates and the real posture data of the human body; calculating according to the vector relationship of the target joint point coordinates to obtain the inter-joint angle and joint torsion angle of the target joint point coordinates; determining a target human risk score based on the correspondence, the inter-joint angle and the joint torsion angle; the target human risk score includes an expert evaluation score; when the target human risk score exceeds a preset risk threshold, a posture correction notification is sent using a composite message; wherein, the composite message sends reminders simultaneously based on multiple senses. The posture correction training method and device for human-centered intelligent manufacturing provided by the present application are used to accurately and reliably correct the posture of the human body.

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.

The invention discloses a physical examination score prediction method and platform. The method comprises the following steps: a) analyzing historical data; b) analyzing a data trend; c) performing simulation test; step d) expert assessment; and e) personal evaluation: step a) historical data analysis: firstly analyzing physical examination score data in past several years, knowing an average score, a highest score, a lowest score and the like, comparing data in different years, observing trends and changes, predicting a possible score range of students according to training conditions, historical scores and examination difficulty of the students, and b) analyzing a data trend: analyzing the collected historical data. According to the method, the phenomena that traditional prediction is too troublesome and the prediction period is long are changed, multiple prediction modules and scientific methods are adopted for prediction, multiple algorithms are not needed for prediction, the prediction period is not very long, and the phenomenon that the prediction accuracy is not high does not occur; and the physical examination quality of the trainee is not influenced.

The present invention discloses a method for characterizing the comprehensive performance of a hob, including the selection of hob performance characteristic parameters, the determination of performance characteristic levels, the calculation of parameter influence coefficients, the determination of performance characteristic functions, the comprehensive performance characterization results, and the selection of hob types; the comprehensive performance of the hob is characterized by performance characteristic parameters such as hardness, load-bearing capacity, cutter ring wear resistance, impact resistance / cutter ring toughness; the performance characteristic levels of the hob are characterized by five levels: level I, level II, level III, level IV, and level V; the influence coefficients of different characteristic parameters of the hob in hard rock formations, soft rock formations, and formations with uneven hardness are established by using analytic hierarchy process and expert evaluation; the comprehensive performance characteristic function of the hob is constructed by using trapezoidal membership function or triangular membership function. The present invention can qualitatively and quantitatively characterize the comprehensive performance of the hob, sort and evaluate the comprehensive performance of the hob according to the characterization results, and optimize the type of hob most suitable for the actual engineering project.

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 present application provides a method for quantifying the value of scientific questions by embedding a multi-dimensional mixture-of-experts mechanism, which relates to the technical field of data processing and includes: reading predetermined quantification dimensions; introducing a predetermined triple strategy to construct a target instruction fine-tuning dataset; performing parameter quantification based on the fine-tuning principle of the QLoRA large language model; obtaining a scientific question text, performing semantic feature analysis through a gating network, and obtaining the target weight coefficient of the scientific question text according to the text semantics; analyzing the scientific question text through a target recognition large model and combining the target weight coefficient to obtain a target quantification result. Through the present application, the technical problem in the prior art that due to relying on expert evaluation and subjective judgment, there are inconsistent evaluation criteria, resulting in low efficiency of scientific question value evaluation can be solved. By integrating multiple dimensions and combining large language models for fine-tuning, the internal evaluation of the value of scientific questions is realized, and the efficiency of scientific question value evaluation is improved.

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 invention discloses a curriculum teaching resource personalized recommendation method based on AI big data, and relates to the technical field of curriculum recommendation, and the method comprises the following steps: S1, defining a plurality of features for each curriculum teaching resource, and carrying out the assignment of each feature through expert evaluation, automatic evaluation or learning path analysis, enabling each course teaching resource to obtain a feature set with a plurality of features and corresponding feature values; s2, on the basis of the mutual relation between the curriculum teaching resource features, setting correlation values between each feature in the feature set and other features; and S3, non-privacy learning behavior data of the student is collected through the corresponding learning platform, and the learning behavior data is authorized non-privacy data. By combining the learning behavior data of the students and the dynamic correction of the course features, the accuracy and practicability of the personalized recommendation system are improved, and the recommendation process can more flexibly adapt to the change requirements of the students.

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.

According to the group decision-making method based on the leader-follower dynamic game, a leader-follower hierarchical adjustment rule is designed to carry out iterative adjustment on an expert evaluation matrix, control and stability of a leader on a decision-making direction are guaranteed, bidirectional optimization is formed through feedback of the follower, and the decision-making efficiency is improved. The authority and the democratic are effectively coordinated, the group consensus degree is remarkably improved, the number of iterations is reduced, and the consensus achievement efficiency is improved. The method comprises the following steps: S1, collecting an evaluation matrix expressed by experts to hesitate a fuzzy language term set; s2, clustering experts through distance measurement and determining intra-class and clustering weights; s3, calculating multi-layer consensus degree to measure and evaluate consistency; s4, if the group consensus degree does not reach the preset threshold value, iteratively adjusting the expert evaluation matrix according to a hierarchical adjustment rule until the expert evaluation matrix reaches the standard; and S5, sorting the schemes by using an approximate positive and negative ideal solution.

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.