106 results about "Design evaluation" patented technology

The invention relates to the technical field of power systems and automation thereof, in particular to a distribution network auxiliary decision-making method and system considering source load fluctuation relevance and a medium. The method comprises the following steps: firstly, collecting related information of a distribution network area, quantifying a synchronization and hysteresis association rule of multi-source heterogeneous data fluctuation, and constructing a composite feature vector and a standardized risk perception data set; defining a state space and an action space of a reinforcement learning algorithm based on the composite feature vector, and realizing auxiliary decision-making optimization of the distribution network; constructing a scene feature library, calculating the fluctuation relevance similarity between a new scene and a historical scene, and multiplexing a deep reinforcement learning model architecture and carrying out transfer learning; building a power grid digital twinborn simulation platform, designing evaluation indexes, generating candidate schemes, deducing the candidate schemes, selecting recommendation strategies and storing the recommendation strategies in a strategy knowledge base.

The invention provides an online learning energy concentration degree evaluation method combined with formability evaluation, and belongs to the technical field of online education, and the method comprises the steps: capturing eye movement data of a learner in an online learning process, carrying out feature extraction and time sequence analysis, and combining formability evaluation data to construct a deep neural network energy concentration degree evaluation model. And performing real-time dynamic evaluation on the energy concentration state of the learner by using multi-dimensional indexes such as attention continuity, visual concentration degree and cognitive input degree. Through a sliding time window technology and a specially-designed evaluation equation, the score of the energy concentration degree of each learning stage can be accurately calculated, and finally the overall energy concentration level of the learner is comprehensively output, so that data support is provided for personalized learning. According to the method, learning energy concentration degree monitoring and analysis support is provided, and the problem that it is difficult to continuously, objectively and comprehensively monitor the attention state of a learner on the premise that learning activities are not affected in the prior art is solved.

The invention discloses a bridge support column structure reliability design evaluation method, and relates to the technical field of bridge design evaluation, and the method comprises the steps: obtaining a first modeling data set based on a bridge support column structure design drawing, and building a to-be-evaluated three-dimensional model and a finite element model; generating a first simulated traffic flow, performing simulation by applying a load to obtain standard state stress data, and performing screening to obtain a key stress surface; the statically-determinate structure creep force of the key stress surface is calculated, and the evaluated damage life is obtained through calculation based on the statically-determinate structure creep force; generating a second simulation traffic flow, performing simulation to obtain offset state stress data of the key stress surface, and performing calculation based on an offset nonlinear regression equation to obtain anti-offset reliability; establishing an anti-impact reliability performance function, generating a simulation impact combination through a Monte Carlo algorithm, and determining an anti-impact reliability simulation value; and based on the anti-offset reliability and the anti-impact reliability, a fusion robustness value is obtained through calculation, and threshold judgment is carried out to obtain an evaluation result.

The invention discloses an architectural design preference evaluation method and system based on multi-modal data, and the method comprises the steps: constructing a virtual reality architectural scene based on architectural design data, reinforcing the emotional response of a user through the design of an emotion amplifier, obtaining the neurophysiological data, emotion data and cognitive data of the user, and forming a multi-modal data set; and performing deep analysis on the building design preference according to the multi-modal data to obtain a building design evaluation result. According to the method provided by the embodiment of the invention, the virtual scene simulation technology and the neurophysiological data are combined, and a traditional method for obtaining the multi-modal data of the emotional response of the user in the architectural design is optimized in an immersive virtual reality man-machine interaction scene; the multi-modal data is utilized to perform cross validation on various types of emotion experience data to provide a more accurate preference evaluation result for building design, so that the building design can be optimized according to the preference evaluation result of the user, and a scientific basis is provided for creating high-quality building design.

The invention relates to the technical field of mechanical spindle design, in particular to a spindle precision design evaluation method and system based on big data, and the method comprises the steps: constructing an original design scheme according to a spindle precision evaluation standard of a to-be-set spindle; establishing a scheme design database, retrieving a historical design scheme similar to the main shaft precision evaluation standard in the scheme design database, and generating an iterative calculation group; establishing a scheme iteration comparison model by taking the iteration calculation group as a reference and combining with the original design scheme, and generating an optimal design scheme for the to-be-set main shaft; and performing evaluation verification on the optimization design scheme according to the main shaft precision evaluation standard, and generating a main shaft design scheme based on an evaluation verification result. By means of the method, the problems that in a traditional spindle design method, data utilization is insufficient, the optimization capacity is limited, and evaluation lacks dynamics are effectively solved, the precision and efficiency of spindle design are remarkably improved, and the trial and error cost in the design process is reduced.

The invention discloses a cross-modal data processing method for cultural enabling airport design evaluation, and relates to the technical field of cross-modal data analysis and processing, and the method comprises the steps: obtaining time alignment multi-source evaluation data through an adaptive time alignment algorithm based on preprocessed multi-source evaluation data; based on a convolutional neural network model, constructing a multi-source quality enhancement model, and inputting the time alignment multi-source evaluation data into the multi-source quality enhancement model to obtain high-quality multi-source evaluation data; based on the high-quality multi-source evaluation data, constructing a multi-dimensional semantic space, and obtaining position information and association rules in the multi-dimensional semantic space; obtaining consistency multi-source evaluation data by analyzing the position information and the association rule; and based on the consistent multi-source evaluation data, through a comprehensive scoring mechanism, evaluating culture elements of the culture enabling airport. According to the invention, through the adaptive time alignment algorithm and the convolutional neural network model, the accuracy and reliability of data processing are significantly improved.

The invention relates to the technical field of computer aided design and visual computing, and provides a visual communication design evaluation system and method based on data analysis. The method comprises the following steps: acquiring original design data, and extracting a spatial layout feature matrix, a color distribution feature vector and a semantic content feature set; calling a design specification knowledge base based on the design purpose information; performing simulated attention distribution analysis and design compliance analysis to generate corresponding features; constructing a transmission efficiency prediction model to generate a comprehensive transmission efficiency score and an initial design evaluation report; performing interference effect joint analysis to obtain a comprehensive interference effect quantitative evaluation result; and performing efficiency optimization correction on the initial design evaluation report based on the result, and outputting a final design evaluation report integrating spatial layout and visual attribute optimization suggestions. According to the method, the interference effect is accurately quantified through conjoint analysis of spatial layout and semantic attributes, and the accuracy and reliability of design conveying efficiency evaluation are improved.

The invention discloses a sheet metal part manufacturability reasoning method based on space-semantic map alignment, and relates to the field of manufacturing-oriented design evaluation and industrial knowledge reasoning, and the method comprises the following steps: carrying out geometric analysis on a CAD geometric model of a sheet metal part to be evaluated; abstracting the geometric features and the topological / metric spatial relationship thereof into a computable spatial semantic graph; performing semantic analysis on the process specification described by a natural language, converting the process rule into formalized logic check expression by using a large language model through context learning, and generating an executable domain-specific language check script; executing the script on a spatial semantic graph, realizing deterministic reasoning through graph matching and attribute verification, and completing accurate mapping and violation detection of text rules and geometric features; and outputting an interpretable diagnosis result containing violation feature positioning, triggering rules and numerical evidence. In order to solve the problems that a process rule'natural language-geometric model 'has a semantic gap, a traditional rule system is poor in adaptability, and an end-to-end learning method is high in data dependence and cannot be explained, a new rule can be quickly adapted under the condition that a large amount of data does not need to be labeled and a model does not need to be retrained; the method can accurately identify the violation of the micro-size and spatial relationship, and has reasoning preciseness, interpretability and engineering availability.

The invention discloses an aircraft wing beam section multi-objective optimization design method based on reinforcement learning, and the method comprises the steps: space definition, virtual environment building of a beam structure, and intelligent optimization design. The method specifically comprises the steps of S1, determining a state space, S2, determining an action space, S3, setting a design evaluation value function, S4, building a virtual environment of a beam structure, S5, establishing an intelligent design body of the beam structure, S6, training the intelligent design body of the beam structure, S7, carrying out optimization design by using the intelligent design body of the beam structure, and S8, checking whether a design scheme is reasonable and feasible or not. According to the aircraft wing beam structure multi-objective optimization design method adopting reinforcement learning, multi-objective optimization can be achieved, the design experience can be stored, the design space can be further expanded on the basis in the future, and the design efficiency is improved. In addition, the method can be used for design of other types of beam structures through simple improvement, and has wide application value.

The invention discloses a cooperative training data generation method for an adjustment and optimization problem and electronic equipment, and the method comprises the following steps: carrying out the filtering processing of an input code description text, and obtaining input data; injecting the problem data into the input data to generate first adversarial sample data containing the problem data; gradient information and disturbance amplitude are calculated, and second adversarial sample data are obtained through adjustment and optimization; designing a shared encoder based on the second adversarial sample data to obtain problem data and normal code data features; carrying out separation processing on the two in a feature space; according to the separated features, the shared encoder is promoted to learn through feature comparison, and finally a model generated by code training data is obtained; and designing an evaluation index for judging whether a generation result of the model in the problem data injection scene is correct and stable, thereby completing model training of code training data generation. According to the method, generalization and robustness of the large language model in the process of processing actual problems are improved.

The application discloses a product design intelligent evaluation system based on big data, and particularly relates to the technical field of product design evaluation, and the system comprises an anchor point account establishing module, a product evaluation module, a scheme rating module and a result output module; the anchor point account establishing module forms a design anchor point table according to a demand preliminary evaluation sheet and initial scheme information; the product evaluation module acquires a scheme change sheet and calls scheme verification data, and performs product undertaking verification; the scheme rating module combines the undertaking verification result and an auxiliary verification result to output a scheme evaluation state; and the result output module outputs product design intelligent evaluation results in a summarized manner. The application can continuously verify whether customer demand is undertaken by design parameters, versions, materials, processes, samples, costs and market feedback after scheme change, and can locate abnormalities to specific change fields, change components, anchoring relationships and data sources, thereby improving the traceability and review efficiency of evaluation results.

The invention provides an optimization design evaluation method for a labyrinth welding drum based on a parameterized model, and belongs to the technical field of aero-engines, and the method comprises the steps: building a drum parameterized mathematical model, and determining a drum stress expression; determining a dangerous part of the drum at the maximum working rotating speed, and determining the section average stress of the dangerous part based on the stress expression; aiming at the welding part, determining the residual strength reserve coefficient of the drum based on the initial defect, and determining the stress reserve coefficient of the welding part; for the maximum stress part, the ratio of the minimum self-sustaining radius of the two wheel discs connected with the drum to the pitch diameter of the drum is obtained; when the ratio does not meet the preset condition, the stress reserve coefficient of the maximum stress part is determined; and carrying out optimization design on the structural parameters of the drum by taking the lightest weight of the drum as an objective function and taking the condition that the residual strength reserve coefficient and the stress reserve coefficient do not exceed the material performance as a constraint condition. A complete evaluation system is established, the design period is shortened, and the evaluation accuracy is improved.

A big data-driven automatic evaluation method for automotive exterior styling design is proposed. First, a large-scale multi-view automotive image dataset is created for vehicle angle recognition and grade recognition, and deep neural network methods are used to train the corresponding vehicle angle recognition and grade recognition machines. Next, an automotive styling evaluation dataset is created for exterior styling scoring. This dataset includes multi-view automotive images and corresponding brand, model, and other labeled information, with styling score annotations and semantic evaluation annotations for end users and styling experts respectively. The automotive styling evaluation dataset is then split into corresponding subsets according to vehicle grade and evaluator attributes, and trained using deep learning regression and classification methods to obtain automatic scoring machines and automatic semantic evaluation machines at different levels for automotive exterior styling users and experts. The significant features affecting styling scores and semantic evaluations are visualized, providing stylists with real-time and objective styling design evaluations during the development process.

The invention provides a free surface range prediction method and device for a hole site design evaluation system, and relates to the technical field of tunnel intelligent blasting. The method comprises the steps that a coordinate system is established, blasting design is conducted, and coordinates of the two ends of all blast holes are obtained; determining a detonating time sequence, grouping the blast holes and coordinates thereof according to the detonating time sequence, and determining a grouping number; the slotting holes serve as a first group of blast holes, the range of a slotting area is defined by a triangle, and a slotting area enveloping surface is formed; triangles belonging to an orifice working surface and a hole bottom working surface are screened out and removed, and the remaining triangles form a first group of new free surfaces; and determining the range of the blasting cavities formed after blasting of the subsequent groups, updating the enveloping surfaces of the blasting cavities of the subsequent groups, and updating the new free surfaces corresponding to the groups until the last group. According to the method, the free surface range formed after blasting of each group of blast holes can be obtained, and a foundation is laid for calculating the blast hole resistance line.

The invention relates to a stochastic simulation method considering dimensional deviation and concrete material variability, and belongs to the field of concrete structure analysis. According to the method, accurate simulation of the mechanical properties of the concrete structure under the influence of construction errors and material randomness is realized by determining random factors, establishing a random field matrix, carrying out sample transformation, establishing random models in batches, solving in batches and carrying out post-processing. A random simulation result is used as an output parameter, a random sample is used as an input parameter, and a PRRM-EPR algorithm is adopted to establish a machine learning prediction model. Compared with a traditional method, the method has the advantages that the generation quality of the random sample is improved, the calculation cost is reduced, the randomness in actual engineering can be better reflected, and the established prediction model can directly predict the mechanical response (displacement, load and the like) of the component through actually measured random parameters. The method is suitable for design, evaluation and optimization of a concrete structure, and particularly has a remarkable effect on improvement of structural safety.

The invention provides a dynamic multi-level evaluation system, which comprises an evaluation specification management module used for storing an evaluation plug-in integration specification, an evaluation data description specification, an evaluation plug-in development specification and an evaluation model description specification; the evaluation data management module is used for creating an evaluation data model required by the task based on the evaluation data description specification; the evaluation model management module is used for completing the design of a comprehensive calculation model on the basis of an evaluation model which is developed by the evaluation model description specification; the evaluation plug-in management module is used for managing an evaluation plug-in, and the evaluation plug-in is packaged according to an evaluation plug-in integration specification and is called by the evaluation model management module; the evaluation system management module is used for designing and managing a multi-level index system required by an evaluation task; and the evaluation scheme management module selects a multi-level index system based on the evaluation task to create an evaluation scheme, and executes the evaluation scheme to generate an evaluation result. The method is clear in logic and simple to operate, and the evaluation work efficiency is greatly improved.

This invention discloses a method for constructing an AI-assisted assessment system for physical education teaching scenarios, belonging to the field of education. The method includes the following steps: First, a standard movement sample database is constructed based on physical education teaching standards, containing movement samples and feature annotations from multiple angles and difficulty levels; second, an AI assessment model is designed, using computer vision technology to extract key points of human posture, and combining temporal analysis algorithms to achieve automatic recognition and quantitative scoring of movements; then, a recording module, a feedback module, and a report generation module are integrated to provide video acquisition, real-time analysis, and personalized guidance functions; finally, the system is continuously improved through an iterative optimization path of data acquisition, model training, system integration, and experimental verification. This invention realizes the transformation of physical education teaching assessment from a subjective experience-driven model to a data-driven model, supports multi-dimensional movement scoring and deviation localization, provides objective and unified evaluation results, and offers immediate and accurate feedback, effectively improving teaching guidance efficiency and students' movement skill levels.

The invention belongs to the technical field of combination of a green building design evaluation technology and a multi-objective optimization algorithm, and provides a green building design scheme evaluation system based on a multi-objective optimization algorithm, and the system comprises the steps: screening quantifiable and optimizable green building design targets, and recognizing the strong conflicts between the targets; if the strong conflict exists, the anti-interference capability of a to-be-selected optimal solution set obtained through multi-objective optimization on weight fluctuation is checked through a judgment module; if the anti-interference performance of the solution set is poor, a weight sensitive target is positioned through sensitivity analysis; a reliable reference weight is generated by combining subjective weighting, objective weighting and sensitive target characteristics; and finally, giving consideration to the comprehensive performance and stability of the scheme through priority calculation, and outputting the priority of the to-be-selected scheme, thereby solving the problems of subjective weight setting and poor anti-interference performance of the scheme in the existing green building design evaluation, remarkably improving the scientificity and reliability of the evaluation result, and providing precise support for decision making of the green building design scheme.

The invention discloses an automobile instrument software design evaluation policy information extraction method and system, a medium and equipment. The method comprises the following steps: constructing design evaluation policy annotation data containing noise; screening a clean set and a noise set from the design evaluation policy annotation data based on a k-fold cross validation method; combining the clean set and the noise set based on a weighted weak supervision loss function to perform weighted training on an information extraction model to obtain a trained information extraction model; and performing information extraction on the to-be-identified automobile instrument software design evaluation policy document by utilizing the trained information extraction model. According to the method, through a two-stage adaptive noise processing framework, high-confidence-coefficient cleaning samples are screened based on loss values, and then noise sample training is fused through a weighted weak supervision loss function. According to the method, the extraction precision in a noise environment is remarkably improved, the dependence on expert labeling is reduced, and efficient compliance development of automobile software is facilitated.

The invention provides a core accomplishment-oriented Chinese teaching design evaluation method and device, and relates to the technical field of data processing, and the method comprises the steps: obtaining Chinese teaching design text data; inputting the Chinese teaching design text data into a pre-trained Chinese teaching design evaluation model to obtain scoring results of the Chinese teaching design text data in multiple dimensions; wherein the Chinese teaching design evaluation model is obtained by training a large language model based on a historical Chinese teaching design text data set through a pre-established Chinese teaching design evaluation system oriented to subject core accomplishment; the Chinese teaching design evaluation system oriented to the subject core accomplishment comprises a plurality of evaluation indexes of multiple dimensions. Through the method provided by the invention, automatic evaluation of Chinese teaching design is realized, and teachers are assisted in improving teaching design quality and teaching level.

To provide a system and method for generative design of an energy storage device.SOLUTION: A method automatically builds at least one model of an energy storage device. The building is based on a design parameter space and employs a machine learning process. The method automatically performs a simulation of the energy storage device using the design parameter space, a design evaluation space, and the at least one model built. The performing produces at least one prediction. The method automatically evolves at least one of the design parameter space and the design evaluation space. In an event the at least one prediction indicates that a product design objective or model design objective has been achieved, the method automatically converges on the design parameter space evolved, thereby completing a generative design of the energy storage device and, otherwise, repeats the building, performing, and evolving.SELECTED DRAWING: Figure 1A

This design evaluation system comprises: a feature quantification unit that, on the basis of a first trained model obtained by learning a first correspondence relationship between a material image indicating the design of a material and a feature amount obtained by quantifying an appearance feature of the design, quantifies the appearance feature of the design of a material indicated by a material image inputted as an evaluation target and generates feature amount information indicating the feature amount of the evaluation target; and a sensibility quantification unit that, on the basis of a second trained model obtained by learning a second correspondence relationship between the feature amount and a sensibility amount obtained by quantifying a sensibility word representing the appearance of the design, quantifies the sensibility word corresponding to the feature amount of the evaluation target indicated by the inputted feature amount information and generates sensibility amount information indicating the sensibility amount of the evaluation target.

The invention discloses a handwritten Chinese character evaluation method based on components, and relates to computer graphics. Comprising the following steps: 1) carrying out random movement and size change adjustment on part of parts of given handwritten Chinese characters; 2) using unchanged standard layout handwritten Chinese characters and the data generated in the step 1) as a data set, and pre-training a Chinese character component reconstruction module; 3) extracting layout characteristics of handwritten Chinese characters by using a pre-trained Chinese character component reconstruction module; 4) performing handwritten Chinese character layout evaluation by taking the layout characteristics of the handwritten Chinese characters as input and combining a layout evaluation module to obtain an evaluation conclusion and a modification suggestion; according to the method, the defects of the prior art in the aspects of Chinese character evaluation modes and evaluation result display are overcome, meanwhile, the period of traditional manual design evaluation templates is effectively shortened, the cost is reduced, and the method has wide application prospects in the fields of calligraphy education, font design, character recognition and the like.

The invention discloses a double-half-inner-ring angular contact ball bearing evaluation method based on a deflection unsteady state condition, belongs to the technical field of double-half-inner-ring angular contact ball bearing design and manufacturing, and aims to solve the problem that an existing bearing size design evaluation method is carried out under the steady state condition. Establishing a double-half-inner-ring angular contact bearing kinetic analysis model by adopting a bearing kinetic analysis tool, and outputting contact stress and sliding speed of each position of a raceway; carrying out a sample block test to obtain a sliding speed and a contact stress, and drawing a criterion diagram of a bearing failure boundary meeting a deflection unsteady state condition; calculating the product of the contact stress and the sliding speed, comparing the product in a criterion diagram of a bearing failure boundary, judging that the structure size is feasible when the value of the contact stress * the sliding speed is less than the bearing failure boundary, and judging that the structure size is feasible when the value of the contact stress * the sliding speed is greater than or equal to the bearing failure boundary. And judging that the initial structure size does not meet the deflection unsteady state condition. The method is used for bearing design and manufacturing.

The extrapolation degree of the candidate design of the target substance is evaluated. A design evaluation device includes: a reference design determination unit configured to determine a reference design indicating a recipe of a target substance manufactured using two or more material substances; a candidate design determination unit configured to determine a candidate design indicating a recipe different from the reference design; a level determination unit configured to determine a level indicating an extrapolation degree of the candidate design on the basis of a classification indicating a difference between the formula of the candidate design and the formula of the reference design; and a result output unit configured to associate and output the candidate design with the rank.

The invention discloses an EEG-based old-age institution visual environment cognitive performance evaluation method, relates to the technical field of environment monitoring, and aims to solve the problem that the cognitive performance of a scheme cannot be effectively pre-judged and optimized in the prior art. Objective quantitative evaluation of the cognitive performance of the visual environment of the pension institution is achieved, the problem that a traditional design evaluation method is low in evaluation accuracy is solved, and then effective pre-judgment and optimization of the cognitive performance of the scheme are achieved.

This invention discloses an intelligent control optimization method for cryogenic liquid evaporators. The method includes modeling a multi-valve control unit for the cryogenic liquid evaporator, designing an adaptive evaporator loss function, designing a centralized execution evaluation architecture for the multi-valve control unit, prioritizing experience replay training, and optimizing evaporator control. This invention belongs to the field of intelligent control, specifically referring to an intelligent control optimization method for cryogenic liquid evaporators. This scheme constructs precision control reward terms, energy consumption penalty terms, and safety penalty terms to balance control precision and system synergy. Based on the adaptive evaporator loss function design, conflict penalties are used to avoid contradictions in action logic, thereby improving the stability of control optimization. The degree of action conflict is incorporated into the experience priority calculation, prioritizing the learning of conflict scenarios. Importance weights and collaborative penalty designs are added to evaluate network loss, significantly reducing the probability of action conflict. Importance sampling is used to correct distribution offsets, thereby improving the reliability of control optimization.

The invention discloses a system interface design evaluation method and device, a storage medium and electronic equipment, and the method comprises the steps: collecting system interface data and a design rule, and setting a static evaluation index and a dynamic evaluation index according to the design rule; evaluating the system interface data based on the static evaluation index and the dynamic evaluation index to obtain a static evaluation value and a dynamic evaluation value; obtaining a human error evaluation value of the system interface based on the cognitive possibility model; and constructing a comprehensive score model according to the static evaluation value, the dynamic evaluation value and the human error evaluation value, and obtaining a design evaluation result according to the comprehensive score model.

The invention relates to the technical field of green building design, and discloses a digital intelligent green building design evaluation method. The method comprises the steps of collecting a multi-dimensional design parameter data set of a target building, wherein the multi-dimensional design parameter data set comprises spatial layout, material attributes, energy consumption and environment interference event parameters; energy consumption characteristic parameters, structural characteristic parameters and interference characteristic parameters corresponding to environment interference events are separated from the data set; establishing dynamic correlation characteristics of energy consumption and structural characteristic parameters, and extracting cooperative adjustment characteristics of environmental interference events on the two parameters; generating a comprehensive evaluation feature set representing the sustainability difference of the building based on the features, and calculating environmental performance score distribution; and according to the distribution optimization multi-dimensional design parameter data set, an optimization energy consumption and structural characteristic curve is separated. According to the method, through multi-dimensional parameter analysis and dynamic association mining, the comprehensiveness and accuracy of evaluation are improved, and a direction is indicated for design optimization.

The present invention discloses an automated evaluation system for cotton cloth design, comprising a data acquisition module, a preprocessing module, a database module, an import module, a data processing module, and a data output module; the preprocessing module is used to denoise and enhance real-time image data; the import module is used to match index thresholds of cotton cloth; and the data processing module is used to generate an evaluation report and an opinion report; the present invention collects parameter information of newly designed cotton cloth in different states, processes and analyzes different parameter information to quantitatively deduce the cotton cloth design, judges the rationality and practicality of different parameter designs, and automatically evaluates them, thereby effectively improving the efficiency of cotton cloth design evaluation; at the same time, the whole process unifies the measurement standard, thereby ensuring the consistency and accuracy of the evaluation and avoiding errors caused by human subjective judgment.