170 results about "Objective evaluation" patented technology

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The invention discloses a quantitative evaluation system and method for context retention capability of multi-round dialogues of a large language model, and relates to the technical field of natural language processing evaluation. In order to solve the problems of standard missing and subjective dependence of LLM in multi-round dialogue ability evaluation, the scheme adopted by the invention comprises a dynamic scene generation module for cooperatively generating multi-round dialogue streams containing marked information points based on a rule template and LLM; the interference injection module is used for inserting controllable interference into the dialogue stream; the three-layer evaluation module takes the dialogue stream with interference as input, quantifies basic memory decline, topic logic correlation degree and anti-interference robustness of LLM through a memory precision layer, a correlation depth layer and an interference resistance layer, and calculates an information attenuation rate, an attenuation index and an anti-interference coefficient; the attenuation rate analysis module is used for correspondingly generating a curve based on a calculation result and intuitively presenting the performance change trend of the LLM; and the visual report module is used for integrating the outputs to generate an interactive evaluation report. The method is used for objective evaluation of the LLM context understanding capability.

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention discloses a building low-carbon reconstruction evaluation method based on multi-objective optimization. The method comprises the following steps: acquiring real-time data; obtaining static data; based on the real-time data and the static data, a multi-target evaluation model covering the total carbon emission amount, the transformation cost, the energy-saving income and the investment payback period is constructed, and indoor comfort and project implementation constraint conditions are set for the multi-target evaluation model; an NSGA-II algorithm is adopted to carry out optimization solution on the multi-target evaluation model; and performing automatic screening on the Pareto optimal solution set generated by the NSGA-II algorithm by using an ideal point method to generate a unique recommended transformation scheme, wherein the recommended transformation scheme comprises an optimal low-carbon transformation strategy and various key performance indexes corresponding to the optimal low-carbon transformation strategy. According to the method, the problems of evaluation benchmark distortion, insufficient multi-target collaboration and strong subjectivity of a decision-making process of a current existing building low-carbon reconstruction evaluation method are solved.

The invention relates to an automatic parameter adjustment method and system for an automatic driving system, and the method comprises the steps: R1, model pre-training and preparation: carrying out the precondition and early-stage preparation of the overall automatic parameter adjustment, carrying out the first training of a parameter adjustment model based on a neural network, and completing the initialization, completing a reward and punishment function to formulate a subsequent objective evaluation score of the performance of the automatic driving system; and R2, online parameter adjustment: the online parameter adjustment is a main part of overall automatic parameter adjustment, objective work performance evaluation scoring based on a reward and punishment function is combined with manual scoring, and parameter setting is autonomously completed in a real vehicle operation test process of an automatic driving system. According to the method, the defects in the prior art can be overcome at a time, the scene adaptability and scene generalization of the parameter adjustment result of the automatic driving system are improved, and the problems of efficiency, parallelism and accuracy in the parameter adjustment process of the automatic driving system are solved.

The invention relates to the technical field of kayak motion evaluation, and discloses a kayak motion posture evaluation method based on vision. The method comprises the following steps: synchronously acquiring dynamic image data of an athlete in different exercise stages through a multi-angle image acquisition device, and constructing an original exercise posture data set; key frame extraction and preprocessing are carried out on the dynamic image data, and a multi-dimensional visual feature space is constructed based on the preprocessed data; constructing and training an attitude quality evaluation model by using the space, and extracting attitude evaluation indexes; establishing a corresponding relation between the kayak motion posture scoring grade and the evaluation index; obtaining real-time training image data, obtaining a dynamic self-adaptive attitude evaluation index by combining with the model, and further obtaining a real-time attitude scoring grade; and if the grade is lower than a preset standard value, generating a posture adjustment suggestion. The method can capture attitude information in multiple dimensions, forms objective evaluation, feeds back and adjusts the direction in real time, and is suitable for kayak exercise training scenes.

The invention provides an NoC low-delay data transmission method based on a dynamic routing algorithm, relates to the technical field of data transmission, and aims to solve the technical problems that an existing NoC routing algorithm cannot adapt to a link dynamic load, is lack of congestion trend prejudgment, is slow in path search convergence and is insufficient in QoS differential scheduling. The method comprises the following steps: constructing a dynamic sensing module containing a double-branch LSTM time sequence prediction model, collecting states such as link bandwidth and queue length in real time, and predicting a congestion trend in 50-100ms in the future; constructing a multi-objective evaluation function taking delay as a core, and dynamically adjusting the weight according to the QoS level; solving an optimal path by adopting an improved ant colony algorithm which introduces a congestion penalty and pruning strategy; during transmission, dynamic reselection is triggered through a pre-calculation + fast matching mechanism; and iteratively optimizing the prediction model based on incremental learning. According to the method, congestion is avoided in advance, the path search efficiency and the transmission reliability are improved, the delay is remarkably reduced compared with a traditional algorithm, and the method is adaptive to delay sensitive scenes such as a high-performance processor and an AI chip.

The invention relates to an intelligent automobile multi-dimensional performance evaluation method and electronic equipment, and the method comprises the steps: defining multi-dimensional evaluation indexes of an intelligent automobile, including an evaluation criterion layer and a sub-criterion layer; determining the weight of each evaluation index by using an analytic hierarchy process; calculating the efficiency of each test model by using a data envelope analysis method; calculating a comprehensive evaluation score based on each evaluation index weight and the efficiency of each test model; and giving optimization suggestions for low-efficiency evaluation indexes of the model. Compared with the prior art, the method has the advantages of fusing subjective and objective evaluation, improving scientificity and credibility of evaluation results and the like.

A system is provided.SOLUTION: A system comprising: means for receiving and pre-processing lesson content; means for operating a generative AI model for automatically generating lesson plans and test questions based on the pre-processed content; means for transmitting the generated lesson plans and test questions to terminals of educational institutions; means for collecting lesson videos through cameras installed in classrooms; means for automatically generating objective evaluations of students; and means for transmitting the generated evaluation results to the terminals of the educational institutions.SELECTED DRAWING: Figure 1

The invention discloses a tactical simulation confrontation training system based on digital twinning and VR. The tactical simulation confrontation training system comprises a digital twinning construction and synchronization module, a virtual reality interaction and presentation module, a tactical confrontation simulation and deduction module, a training process monitoring and data acquisition module and a training effect evaluation and intelligent redisk module. According to the invention, the digital twinborn body synchronously mapped with the real combat unit and the environment is constructed, and is deeply integrated with the immersive VR interaction environment, so that a highly vivid and panoramic immersive tactical training scene is provided for trainees, the system supports red and blue parties or multiple parties to carry out intelligent confrontation in a virtual-real combined dynamic environment, and the training efficiency is improved. The multi-dimensional training data is acquired in real time, and based on the data, the system can perform refined and quantitative objective evaluation and intelligent redisk on the tactical actions, decision-making processes and collaborative effects of individuals and teams, so that the authenticity of training, the intelligence of confrontation and the scientificity of evaluation are remarkably improved.

The embodiment of the invention discloses an engineering drawing auditing ability evaluation method for a multi-modal large model, and the method comprises the steps: obtaining a plurality of engineering drawings from different construction engineering projects, and the plurality of engineering drawings comprise a construction drawing, a structure drawing, an equipment drawing and an electrical drawing; all the engineering drawings are marked, and the marked content comprises component information, space information, standard information and defect information; training and testing of a component recognition task, an information extraction task and a common sense judgment task are carried out on the multiple to-be-evaluated multi-modal large models through the marked engineering drawings; according to a test result of each task, respectively evaluating a drawing analysis understanding capability, an information identification capability and a professional common sense judgment capability of each large model; and carrying out weighted summation on the evaluation score of each capability, and determining the engineering drawing auditing capability of each large model. According to the embodiment of the invention, objective evaluation of the multi-modal large model can be realized.

The invention relates to a tunnel safety state evaluation and prediction method based on lining damage evolution in the technical field of tunnel engineering. The method comprises the following steps: acquiring tunnel lining crack data, inputting the tunnel lining crack data into a TF-Transform deep learning analysis model, outputting the tunnel lining crack data to obtain damage characteristics, summarizing a damage evolution mechanism and a development rule of a cracked tunnel, and establishing a cracked tunnel safety evaluation index system; a PL-VIKOR comprehensive evaluation model is constructed based on a crack tunnel safety evaluation index system and an improved subjective and objective combination weighting method based on normal distribution goodness of fit; establishing a multi-dimensional crack lining monitoring system, and monitoring real-time tunnel section data; and predicting a tunnel section development result by adopting an LSTM model based on real-time tunnel section data, and evaluating a future safety state of the tunnel in combination with a PL-VIKOR comprehensive evaluation model. And real-time and objective evaluation of the safety state of the cracked tunnel structure is realized.

The invention discloses a software quality evaluation method based on a fuzzy theory. The method comprises the following steps: obtaining a quality factor set according to a Meiger software quality model; determining an evaluation grade set, and dividing the evaluation grade set into five evaluation grades; constructing a membership function by adopting a kernel density estimation method; constructing a judgment matrix through the triangular fuzzy number and calculating a defuzzification weight; and converting the score into a fuzzy synthesis value through fuzzy synthesis operation, and generating a fuzzy comprehensive evaluation score through weighted average and normalization processing. According to the method, the fuzzy theory and the Meicole model are combined innovatively, and uncertainty in subjective and objective evaluation is processed by applying fuzzy mathematics. The objectivity, scientificity and repeatability of an evaluation result are effectively improved through a membership function and a weight set constructed based on historical data of similar software. The method is suitable for quality evaluation and optimization decision making of various complex software systems.

The invention belongs to the technical field of emergency recovery of an electric power system coping with extreme disasters, and discloses a new energy electric power system black-start power supply subjective and objective evaluation method and system, and the method comprises the following specific steps: 1, calculating a weight through a CRITIC method, obtaining all indexes in an index system of a conventional black-start power supply and a new energy black-start power supply through a daily power station, and calculating the weight; and the variability of each index is displayed by calculating the standard deviation. The index provided by the invention is suitable for a traditional black-start power supply and a new energy black-start power supply; the CRTIC method is adopted for objective weighting, conflicts among indexes are considered, relevance among the indexes is also considered, and subjective weights are added by using the analytic hierarchy process, so that the finally obtained comprehensive weights give consideration to subjective experience and objective basis, and evaluation is more scientific; the improved TOPSIS method is adopted for sorting, the situation that ranking is difficult to evaluate due to approximate relative closeness of traditional TOPSIS can be processed, and the evaluation result is more obvious.

The invention belongs to the field of power distribution area resource scheduling, and provides a power distribution area adjustable resource complementary potential evaluation method and system fusing subjective and objective, and the method comprises the steps: constructing a power distribution area complementary potential evaluation index system; based on the sorting result of each index, determining the subjective weight of each index by using an order relation analysis method; acquiring actual operation data corresponding to the indexes, and determining objective weights of the indexes based on the standard deviations of the indexes and conflicts among the indexes; performing fusion optimization based on the subjective weight of each index and the objective weight of each index to obtain a subjective and objective evaluation fusion weight; and obtaining actual operation data corresponding to each index, determining cloud model digital features of each index based on subjective and objective evaluation fusion weights, and performing weighted fusion by using a cloud model to obtain an evaluation result. According to the method, the operation characteristics of the multi-type adjustable resources in the power distribution area can be comprehensively depicted from the three aspects of source, load and source-load complementation.

The invention discloses a system and method for checking and optimizing questions and answers of a port yard design scheme, and the system comprises a question and answer analysis unit which is used for obtaining design task information, and analyzing the design task information through a natural language processing model to obtain a structured design task; the multi-objective evaluation unit is used for setting a function loss value based on the structured design task and constructing a multi-objective balance loss function according to the function loss value; the iterative optimization unit is used for performing iterative optimization calculation on the multi-target balance loss function through an improved Bayesian algorithm and outputting a preliminary design scheme; and the feedback adjustment unit is used for acquiring user feedback information, analyzing the user feedback information through a natural language model to obtain a feedback quantification result, and updating the multi-target balance loss function according to the feedback quantification result. According to the system and the method provided by the invention, the design scheme for port engineering can be accurately output by processing and quantifying expert feedback suggestions.

The invention relates to a pipeline defect intelligent detection method based on YOLOv12 improved attention enhancement and multi-scale adaptive fusion. According to the method, an A2C2f enhanced convolution module, an MS-AFFN multi-scale feature fusion module, a double-path attention mechanism module and an intelligent detection decision module are innovatively constructed on the basis of a YOLOv12 architecture, a three-branch parallel feature extraction mechanism is constructed, collaborative learning of attention enhanced convolution and multi-scale self-adaptive fusion is adopted, and the intelligent detection decision module is constructed. And collaborative optimization of local defect feature enhancement and global context modeling is realized. An ACEM adaptive channel enhancement module and an SAM space attention module are innovatively designed, and adaptive fusion of defect features of different scales is realized through a weight dynamic adjustment mechanism guided by global adaptive pooling. The method effectively solves the problems of insufficient small target identification precision, simple multi-scale fusion strategy and difficulty in balancing real-time performance and accuracy in traditional YOLOv12 in pipeline defect detection, mAP50 on a data set containing 22 and 120 labeled samples reaches 99.3%, the mAP50 is remarkably improved compared with a standard YOLOv12 method, the average reasoning time is only 9.4 ms / image, and the method is suitable for large-scale pipeline defect detection. Experimental results show that the model has excellent performance in objective evaluation indexes and subjective evaluation, has a remarkable effect in six types of pipeline defect detection, and has a remarkable technical effect and industrial application value.

A set Kalman filtering turbulence model assimilation method based on full field similarity criterion is disclosed, which comprises the following steps: sensitivity analysis is performed on the coefficients of the candidate turbulence model to obtain sensitive coefficients and their sampling range for Latin hypercube sampling, i.e., sampling interval; model constant sample combinations are extracted from the sampling interval by using a directional Latin hypercube sampling method, and target flow field results are calculated; a data assimilation algorithm based on the set Kalman filtering algorithm is used for the target flow field results and the model constant sample combinations, and after obtaining the optimized model constant combination, the model constant combination is updated and verified in cycles to obtain the optimal model parameter combination; finally, the improved turbulence model is used to simulate the internal flow field of the fuel assembly in the core of the pressurized water reactor with high similarity. The present application can provide an objective evaluation standard for the full physical field similarity of the data assimilation method, significantly improve the convergence speed of the algorithm, strengthen the robustness of the algorithm, and reduce the calculation cost of the algorithm.

The invention relates to a digital hot continuous rolling loop evaluation method. The method comprises the steps that the complete control period of a loop is divided into a steady-state control stage, a loop rising control stage and a loop falling control stage; establishing evaluation parameters according to the working parameters in the process of the steady state control stage, the loop rising control stage and the loop falling control stage; setting a reasonable range and an optimal range for each evaluation parameter, and mapping each evaluation parameter into a single score through a preset scoring function; and obtaining the loop control quality standard according to all the single scores. According to the method, comprehensive digital evaluation is formed by decomposing, standardizing and scoring key parameters of the whole loop control process, objective evaluation, accurate management and control, fault early warning and continuous optimization of the loop control effect are achieved by utilizing the periodic trend of the comprehensive digital evaluation, and finally the out-of-control probability of the loop and production accidents are reduced.

The invention relates to the technical field of parking management systems, in particular to a parking lot service level evaluation method based on the perspective of user experience, and the method comprises the steps: constructing a parking lot service level evaluation index system which comprises a first-level index, a second-level index and a third-level index; acquiring basic data of the parking lot; calculating the score of each third-level index according to the basic data; calculating the weight of each third-level index in the second-level indexes by adopting a standard deviation method, determining the final weight by combining subjective fine tuning, and calculating the score of each second-level index; the weight of each second-level index in the first-level indexes is calculated by adopting a standard deviation method, and after the final weight is determined by combining subjective fine tuning, the score of the parking service index is calculated; and performing qualitative evaluation on the parking lot service level according to the parking service index score. From the perspective of a user, comprehensive and objective evaluation of the service level of the parking lot is realized by scientifically constructing an evaluation index system and calculating scores, reference is provided for the user to select the parking lot, and an operator is guided to optimize the service.

The application discloses a MOFs synthesis route multi-objective optimization method based on machine learning. The method is to collect the synthesis conditions of prepared Ce-UiO-66, and evaluate the defect content and thermal stability thereof through a thermogravimetric analysis curve, as initial data set; the data set is randomly divided into a training set and a test set, eight algorithms are adopted to model each performance of Ce-UiO-66 and select the proxy model for performance prediction; the target achievement probability (PA) value of each performance in the synthesis space is calculated and expanded into a multi-objective evaluation factor; the Ce-UiO-66 material is prepared; the obtained material is subjected to characterization test, if the test data does not meet the requirement, the data set and the proxy model are updated. The application has the advantages of low cost, short cycle and the like in optimizing the catalytic and stable performances of MOFs based on reliable experimental data and machine learning, and can be popularized to the design of synthesis routes of other materials.

The application provides a noise annoyance modeling method for an audio injection system, which comprises the following steps: firstly, according to a target sound, a corresponding control sound is selected to perform time domain superposition, so as to obtain a plurality of superimposed sound samples; then, a subjective evaluation experiment and data processing are performed on the obtained superimposed sound samples; then, psychoacoustic parameters of the target sound, the control sound and the superimposed sound are calculated, and the difference between the corresponding psychoacoustic parameters of the target sound and the control sound is calculated; the difference between the calculated psychoacoustic parameters of the target sound and the control sound is taken as an independent variable, and the corresponding superimposed sound annoyance score value is taken as a dependent variable; the correlation between the independent variable and the dependent variable, the correlation between the independent variables and the collinearity between the independent variables are calculated; according to the calculation results, the modeling independent variable is determined; the determined modeling independent variable and the superimposed sound annoyance score value are taken as the dependent variable, and a multiple linear regression is adopted to realize the audio injection superimposed sound annoyance modeling. The application improves the precision and parameter correlation of the model, and further improves the objective evaluation of the audio injection superimposed sound annoyance.

The application discloses a kind of tire and whole vehicle matching quantitative evaluation method, first carry out objective evaluation test, obtain whole vehicle objective index and tire objective index;Next, subjective evaluation test is carried out, and subjective area score is obtained;Then the objective index is processed with feature dimension reduction, then the objective index after dimension reduction and subjective area score are input into subjective-objective fusion generative adversarial network to carry out data enhancement, and matching enhancement dataset is obtained, finally, the matching enhancement dataset is used to train subjective-objective fusion combination model to obtain the quantitative evaluation result of tire and whole vehicle matching.The application is helpful to provide accurate evaluation and guidance for tire selection, and shorten tire matching option cycle.

The embodiment of the present application relates to the technical field of artificial intelligence and natural language processing, and discloses a petroleum large language model evaluation method and device, an electronic equipment and a storage medium.The present application inputs test data corresponding to the task target into the petroleum large language model according to the task target, and obtains a test result; the task target is divided into the following categories: knowledge understanding category, data processing category, task execution category, anti-interference category and process deduction category; the test result is evaluated using a preset evaluation rule corresponding to the category of the task target, so that the petroleum large language model can be objectively evaluated, and the application effect of the model can be quantified and verified.The evaluation task target includes multiple dimensions and indexes, and can comprehensively evaluate the performance of the large language model in the petroleum field.

The present application relates to the technical field of fastener production, and discloses a fastener production combined process determination method based on a parameter matching algorithm. The method comprises: classifying fastener parameters using a multi-dimensional parameter classification method; collecting sample data based on the classification results, and establishing an association matrix of parameters and processes; generating process labels using a constraint clustering algorithm; dynamically adjusting parameter weights for samples to be processed, and generating candidate schemes using a weighted matching algorithm; selecting the optimal process combination through multi-objective evaluation; and determining detailed production parameters. The system comprises parameter classification, data analysis, constraint clustering, weighted matching, multi-objective evaluation, and parameter determination modules. The present application solves the problems of fixed parameter weights, lack of systematic process induction, and ignoring process constraints in existing methods, realizes the intelligentization and optimization of process determination, and improves production efficiency and scheme accuracy.

The embodiment of the invention relates to the technical field of data processing, in particular to an intelligent remote distributed bid evaluation system and method, and the system comprises a distributed evaluation system, an expert management system and an AI algorithm service system. In the bid evaluation preparation stage, the distributed evaluation system generates a plurality of subjective evaluation items and objective evaluation items about the bid files according to the received bid invitation file and the plurality of bid files; in the bid evaluation stage, the distributed evaluation system calls an expert management system to extract a plurality of bid evaluation experts from an expert database, and distributes bid evaluation tasks corresponding to the subjective evaluation items to account systems of the bid evaluation experts on the distributed evaluation system for the bid evaluation experts to perform evaluation; distributing bid evaluation tasks corresponding to the objective evaluation items to an AI algorithm service system for evaluation; and summarizing all review results to generate a bid evaluation report about whether the bid winner exists or not. According to the scheme, the problems in the current'on-site expert centralized closed 'bid evaluation mode can be effectively solved.

The application relates to the technical field of natural language processing, and provides a conference minutes product evaluation method and device, equipment and a medium. The method comprises the following steps: calling an evaluation interface, inputting a transcription text into a plurality of conference minutes products, collecting conference minutes generated by each conference minutes product for the transcription text, and recording performance time consumption data of each conference minutes product; evaluating each conference minutes in multiple evaluation dimensions to obtain evaluation results of each conference minutes for each evaluation dimension; performing weighted average on each conference minutes for objective evaluation dimensions, subjective evaluation dimensions, general capability evaluation dimensions, characteristic capability evaluation dimensions and safety and value evaluation dimensions according to preset dimension weights to obtain evaluation results of each conference minutes; and generating an evaluation report of each conference minutes product based on the evaluation results and the performance time consumption data. The application can realize comprehensive performance evaluation of cross-platform conference minutes products.

The invention discloses a physical education teaching quality dynamic evaluation system and method based on artificial intelligence, and the system and method are realized through the following steps: employing an inertial motion capture sensor, a non-embedded physiological signal measurement device, a camera and a microphone; collecting motion postures and physiological data of students and teaching behaviors and explanation contents of teachers in real time; after cleaning, denoising and format conversion are carried out on the collected data, feature extraction and mode recognition are carried out by adopting a machine learning and deep learning model, and the exercise performance of students and the teaching effect of teachers are analyzed; a dynamic evaluation model is constructed based on the analysis result, and the model integrates a motion efficiency comprehensive evaluation function and comprehensively considers the motion accuracy, the motion stability, the physiological load index and the motion sequence fractal dimension to achieve objective evaluation; finally, personalized teaching suggestions are provided for teachers according to evaluation results, and customized learning paths and resources are planned for students. According to the invention, real-time, dynamic and intelligent assessment of the physical education teaching process is realized, the assessment efficiency and accuracy are significantly improved, and teaching optimization and personalized development are promoted.

The invention provides a method and a device for evaluating usability of a panoramic looking-around system, a terminal and a medium. The method comprises the following steps: acquiring a plurality of test tasks of the panoramic looking-around system and test evaluation indexes for evaluating the plurality of test tasks; according to the test task, test environment parameters are set for a whole vehicle optical test platform, subjective scoring data and objective test data are obtained, and objective scoring data are obtained according to the objective test data; analyzing correlation intensity between a subjective evaluation index and an objective evaluation index based on the subjective scoring data and the objective scoring data to obtain objective forward close degree data and net correlation intensity data; and obtaining system usability according to the subjective score, the objective score, the objective forward close degree data and the net correlation intensity data. The fusion degree of technical parameters and user perception is improved, the evaluation dimension of the panoramic looking-around system is improved, and the evaluation effect of the panoramic looking-around system is improved.

The invention discloses a subjective and objective combination weighting evaluation method based on variance optimization, and the method comprises the steps: firstly evaluating the relative importance of each index, and obtaining the subjective weight of each index; constructing a sample-index matrix, calculating the information entropy value of each index, obtaining the weight of each index based on the information entropy, removing redundant indexes through the calculation of the index correlation degree and the correlation degree between the indexes, achieving the de-overlapping, calculating the absolute correlation degree of the indexes, and carrying out the normalization to obtain the objective weight of the indexes; setting subjective and objective weight coefficients, carrying out linear addition processing on subjective and objective weights, and on the premise that the sum of constraint condition coefficients is 1, enabling the variance to be as small as possible to obtain the optimal subjective and objective combined weight of each index; and judging subjective and objective weights of the indexes and the consistency of sorting arrays, and determining final weights of the indexes. On the basis of keeping the advantages of the original subjective and objective evaluation method, the advantage complementation of subjective weighting and objective weighting is realized, and the evaluation effect of the evaluation model is improved.