234 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 relates to the technical field of fluid machinery design, and discloses a fluid machinery through-flow component intelligent design simulation system, which comprises a multi-physics field modeling module, which is used for constructing fluid mechanics, structural mechanics and heat conduction models based on preset boundary conditions and physical attributes; the intelligent optimization decision module is used for constructing and defining a through-flow performance optimization objective function; the cooperative calculation module is used for constructing a comprehensive performance evaluation model based on the candidate design parameter set; the virtual experiment feedback module is used for reconstructing a corresponding through-flow component structure model according to the multi-target evaluation result; and the scheduling control module is used for controlling algorithm path selection and computing resource allocation in the optimization process. According to the method, a method of combining multi-physics field coupling modeling and a dynamic feedback mechanism is adopted, and design parameters are continuously adjusted through simulation data fed back in real time, so that the optimization process can be dynamically updated according to actual operation conditions, and the optimization convergence speed and the overall performance are improved.

The invention discloses a low-carbon city subjective and objective mixed planning interval entropy weighting evaluation method, and relates to the technical field of city management and low-carbon evaluation, and the method comprises the steps: collecting and preprocessing index data in real time, building a user behavior prediction model according to the index data, and predicting the index data of a user; dynamically adjusting weight distribution of subjective and objective evaluation indexes based on the predicted index data, and constructing a comprehensive evaluation model to calculate a comprehensive score of each region in the city; and formulating optimization measures of each region according to the comprehensive score. A prediction model is established by using a support vector machine, an evaluation result is highly matched with real-time data through dynamic weight adjustment, the hysteresis problem of traditional static evaluation is avoided, and more accurate and flexible low-carbon city evaluation is realized through dynamic adjustment of weight distribution and construction of a comprehensive evaluation model. And the comprehensive score of the urban area is calculated by adopting a nonlinear transformation function, so that the marginal effect change of the evaluation index under different load conditions can be better reflected.

The invention provides a sound standardization evaluation system for multi-dimensional acoustic parameter and emotional scene dynamic analysis, and aims to realize structured and objective evaluation of voice performance. The system comprises an audio acquisition module, an audio processing module, a standard evaluation module and an integration processing module. The audio processing module performs noise reduction, pre-emphasis, sampling rate adjustment, framing and slicing operation on the original voice data; the standard evaluation module executes character and pronunciation accuracy analysis, acoustic basic skill feature evaluation or emotion and scene adaptation degree analysis based on multi-modal fusion according to an evaluation type set by a user; parameters including fundamental frequency, formant, sound intensity, fundamental frequency perturbation, amplitude perturbation and the like are extracted in evaluation, and classification prediction is completed in combination with a machine learning model; and finally, the integration processing module generates an evaluation report including a scoring result, question prompts and personalized training suggestions, and provides closed-loop feedback to support sound training and expression optimization.

The invention discloses a solid state disk performance fluctuation test method, device and equipment and a storage medium, and relates to the technical field of performance tests.A corresponding test action sequence is generated according to a performance test instruction, a plurality of test actions are organized into a standard test process capable of being repeatedly executed, and the performance fluctuation test efficiency is improved. The influence of the difference of manual test operation execution on the test result is eliminated, the performance fluctuation test result of the to-be-tested solid state disk is calculated based on the storage performance index of the to-be-tested solid state disk in the test action sequence execution process, and objective evaluation of the performance fluctuation of the solid state disk is realized on the basis of sufficient data support. The automation and standardization of the test are realized from the two aspects of the test process and the result evaluation, and the efficiency and accuracy of the test are improved.

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 embodiment of the invention provides a heterogeneous network scheduling method and device and a computer readable medium, and the scheme integrates objective evaluation based on index attributes and subjective evaluation based on artificial empowerment through a mode of combining objective evaluation and subjective evaluation, so that a comprehensive score serving as a network scheduling basis is improved, and the network scheduling efficiency is improved. According to the method, the effects of different types of heterogeneous networks for a certain specific target audio-visual service can be reflected more accurately, so that more accurate network scheduling is realized, the user experience is improved, meanwhile, dynamic switching of the networks can be realized, the terminal equipment can be ensured to be always connected with the optimal network in the whole process of executing the audio-visual service, and the user experience is improved. Therefore, the best transmission effect is obtained.

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 provides an analytic hierarchy process-based multi-target evaluation method for a module power supply radiator. The method comprises the following steps of: determining structure influence factors of the module power supply radiator according to an analytic hierarchy process; performing expert scoring on the structure influence factors, and determining a weight vector based on expert scoring; establishing a finite element simulation model of the module power supply comprising the radiator; based on the finite element simulation model and a thermosetting coupling analysis method, determining result data of the structure influence factors; determining the entropy weight of each structure influence factor according to the result data of the structure influence factors; based on the weight vector and the entropy weight of each structure influence factor, determining a comprehensive weight corresponding to each structure influence factor; and according to the comprehensive weight of each structure influence factor, comprehensive scores of the radiators of different specifications are calculated, and the radiator corresponding to the highest comprehensive score is the optimal radiator required by the module power supply. According to the scheme, different radiator structures are scored, and the reliability of the radiator structures is improved.

The invention relates to the field of intelligent transportation, in particular to a driving data acquisition and analysis system and method, and the system comprises a global multi-dimensional data acquisition module, an intelligent fusion perception processing module, a data collection and distribution module, and a backtracking analysis platform. The global multi-dimensional data acquisition module acquires data of an external environment of a vehicle, a state of the vehicle and an operation state of a driver in a running process of the vehicle; the intelligent fusion perception processing module carries out real-time fusion, structured processing and labeling on the data to form truth value perception and road label data; the data gathering and distributing module gathers the processed data, stores and manages the data, distributes the data to the subjective and objective evaluation module, and generates subjective and objective evaluation results through manual label dotting and algorithm rule matching; and the backtracking analysis platform is used for backtracking and evaluating vehicle violation or risk behaviors in combination with subjective and objective evaluation results, and generating an analysis report. Therefore, the problems of low sensing range, insufficient sensing precision, single evaluation function and the like in the prior art are solved.

According to the pathological section image quality evaluation method and the computer program product provided by the invention, automatic and objective evaluation of pathological section quality is realized through the deep learning model, and the problems of low efficiency and low recognition rate of an existing manual evaluation system are effectively solved. The dynamic classification strategy can flexibly adapt to different clinical requirements, technical guarantee is provided for accuracy of pathological diagnosis, and the method has remarkable clinical application value.

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 relates to a sewage treatment water quality prediction method based on an optimized minimum count summary algorithm and a deep learning model, and aims to improve the accuracy of water quality monitoring and reduce the monitoring cost. Along with the acceleration of industrialization and urbanization processes, the problems of water pollution and water resource shortage are more and more serious. In order to improve the utilization efficiency of water resources, sewage recovery and treatment are particularly critical. In the sewage treatment process, water quality monitoring data provides an objective evaluation standard for the treatment process, but due to monitoring period and cost differences of different water quality indexes, a traditional direct measurement method faces great challenges. According to the method, the minimum count summary (Count Min Sketch, CMS) algorithm and the deep learning model are combined, the optimized minimum count summary algorithm is used for extracting frequency domain information, and the frequency domain information serves as features to be input into the long short-term memory network (LSTM)-Attention model, so that the precision of water quality prediction is remarkably improved. The method not only improves the prediction precision of the model, but also is efficient and low in cost.

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 provides a long-term runoff multi-period integrated prediction method based on factorization optimization, and the method comprises the steps: selecting a runoff prediction month and a corresponding prediction period step length, spreading climate factors according to a prediction object period, selecting a maximum related monthly factor of each factor, setting a dimensionality reduction compensation contribution rate, and carrying out the optimization of the dimensionality reduction compensation contribution rate. Dimensionality reduction is carried out on the maximum correlation monthly factor matrix, multiple machine learning regression models are constructed, priority evaluation indexes are set, the optimal contribution rate of each model is determined through machine optimization, model numbers and a multi-target evaluation priority mechanism are set, and runoff asynchronous time period integrated prediction is achieved; factor spreading correlation and dimension reduction are utilized to filter redundant silver influence, and a multi-index priority evaluation mechanism is utilized to perform integrated prediction on multiple models, so that the instability of single model prediction can be effectively overcome, and the model prediction precision is effectively improved.

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 the technical field of artificial intelligence models, in particular to an evaluation and calculation method for artificial intelligence understanding and generating ability, which specifically divides the achievable intelligent ability of artificial intelligence from large to small, independently evaluates each divided content, verifies accuracy, precision, recall rate and the like, and improves the evaluation and generation ability of the artificial intelligence. The comprehensive, objective, specific, flexible and stable universal capability evaluation method is formed by combining objective evaluation of the capability with subjective evaluation and manual intervention, and a scientific and effective standard is provided for development, application and evaluation of artificial intelligence large models of all walks of life.

The invention discloses a method for establishing an in-vehicle noise evaluation and prediction model. The method comprises the following steps of: 1, acquiring in-vehicle noise of a plurality of new energy electric vehicles under a steady-state constant-speed working condition and an unsteady-state acceleration working condition; 2, preprocessing an in-vehicle noise audio file to obtain a stable and effective in-vehicle noise audio database; 3, calculating noise objective evaluation parameters of the effective in-vehicle audio files; 4, performing a subjective annoyance degree evaluation test on the noise quality of the effective in-vehicle audio file; 5, processing a noise quality subjective annoyance degree evaluation result to obtain an effective subjective evaluation result and outputting the effective subjective evaluation result to a sample library; 6, performing feature extraction on subjective and objective evaluation results, screening objective evaluation features closely associated with subjective evaluation, and constructing an effective in-vehicle noise quality objective evaluation input sample library; 7, establishing an in-vehicle noise quality evaluation and prediction model; and 8, introducing a Harris eagle optimization algorithm improved based on differential evolution, and carrying out parameter optimization improvement on the model. According to the method, subjective and objective unification is carried out on the in-vehicle noise quality evaluation process of the electric vehicle, the time-consuming and labor-consuming subjective evaluation test can be avoided, the objective evaluation process and subjective feeling are unified, the established evaluation prediction model can reflect the in-vehicle noise quality condition more truly and effectively, and the method has high accuracy and reliability.