865 results about "Evaluation system" patented technology

The invention provides an underwater tunnel shield construction excavation face stability evaluation method, system and equipment, and the method comprises the steps: determining an excavation face stability evaluation index based on a shield construction excavation face instability mechanism; dividing the stability of the excavation surface into a plurality of grades, establishing each grade space, and determining the quantitative interval of each evaluation index in each grade; calculating a combined weight of each stability evaluation index by adopting a combined weighting method, and taking the combined weight as a judgment basis of the influence of the evaluation indexes on an evaluation result; and constructing an ideal point evaluation function by adopting an ideal point method so as to represent the membership degree of the to-be-evaluated object to each grade, calculating the grade membership degree of the to-be-evaluated section in each grade of the evaluation system, and determining the stability grade of the excavation surface in the construction process of the evaluation section.

The invention discloses an offshore pile foundation evaluation system construction method based on evolutionary algorithm optimization and PIML linkage. A standardized engineering data set is obtained; parameterizing the pile-soil coupling dynamics boundary condition, the pile-soil interaction specification criterion and the limit state and use state criterion, and storing the parameters as a physical constraint set; obtaining an initial performance evaluation model; generating an alpha evolutionary optimization performance evaluation model; outputting a bearing capacity-settlement relation index, a dynamic and static stiffness degradation curve index and a residual bearing capacity probability distribution index to form a structural performance index set; generating a credible structure performance index set containing confidence boundaries and extracting an influence degree sorting result; and automatically generating a structured evaluation report according to a verification result. The model output has physical consistency and engineering interpretation all the time, and the problem that a traditional pure data driving model is unreliable in result under data sparsity and environment sudden change is remarkably solved.

The invention belongs to the technical field of urban rail vehicle detection and maintenance, and discloses a full-process optimization method and system for polygonal wear of a metro vehicle wheel. The method comprises the following steps: firstly, constructing a digital twin-driven train rigid-flexible coupling dynamic model, carrying out global sensitivity analysis, establishing a sensitivity index model, screening key dynamic performance indexes, and carrying out batch simulation to construct a dynamic response database; feature extraction and classification model training are carried out on the index data, multi-layer wavelet packet decomposition is carried out on the one-dimensional vibration signals, and a multi-channel feature vector is constructed and input into a one-dimensional residual network model; inputting actually acquired data into the trained model, calculating a relative close degree to generate a comprehensive index and a grading result, and generating turning repair suggestions based on grading; meanwhile, multi-source monitoring data are collected, a long-short-term memory network is used for predicting the abrasion evolution trend, finally, turning repair suggestions and trends are integrated, an accounting model and an evaluation system are constructed, and an optimal maintenance decision is generated through a multi-target optimization algorithm.

The invention discloses a building equipment maintenance demand linkage detection and evaluation system and method, and relates to the technical field of intelligent operation and maintenance of building equipment, and the system comprises an isomorphic equipment cluster division module, a group data flow collection module, a group behavior baseline learning module, an individual deviation degree calculation module, and a maintenance decision generation module. Equipment with consistent function attributes and operation environments is classified into the same cluster, data are classified according to different working conditions on the basis of historical operation data provided by a group data flow acquisition module, group average operation parameters, parameter fluctuation ranges and probability distribution characteristics under each working condition are calculated, a dynamic group behavior baseline model is generated, and the dynamic group behavior baseline model is established. With the continuous updating of new data, the normal behavior mode change of the equipment under different working conditions can be reflected in real time, and when the individual deviation degree calculation module calculates the individual equipment deviation degree according to the model, the accuracy and timeliness of the calculation result can be ensured.

The invention relates to the technical field of intelligent education, in particular to an AI-based nursing teaching scene construction and evaluation system. According to the system, a dynamic case script is generated through historical case data, and a virtual patient physiological model is constructed; creating three teaching scenes of community follow-up visit, emergency rescue and traditional Chinese medicine nursing fusion by using a mixed reality technology; a personalized learning portrait of the student is constructed through a pre-test, and accurate matching between the ability of the student and the teaching content is formed; a double-track system skill training module is built, and AI tutor guidance and teacher guidance are integrated; and an ability evaluation system based on operation data is established, and intelligent pushing of adaptive training content is realized. The problems that in traditional nursing teaching, scene authenticity is insufficient, personalized training is insufficient, and clinical thinking is difficult to cultivate are solved, and the clinical operation skill and decision judgment ability of students are improved.

The invention discloses a multi-objective optimization water resource allocation scheduling method, which combines an LSTM-Prophet hybrid prediction model with an attention mechanism, synchronously captures climate periodicity, policy regulation sensitivity and industrial development tendency characteristics in water demand prediction, and improves water demand prediction precision through a genetic algorithm. Through an improved NSGA-III algorithm and a PSO collaborative optimization mechanism, the convergence problem of a high-dimensional target space is effectively solved in combination with a chaotic mapping technology, and the multi-target optimization efficiency is improved by 40% in cooperation with a dynamic weight adjustment strategy of deep reinforcement learning. And multi-scenario simulation verification of a scheduling scheme is realized by utilizing deep coupling of a digital twin system and a three-dimensional GIS, and a real-time feedback correction mechanism of a Markov decision process is realized. A finally constructed entropy weight-TOPSIS multi-dimensional evaluation system is combined with AR visualization and intelligent contract traceability technologies, and a Pareto optimal solution giving consideration to social fair, economic benefits and ecological integrity is provided for decision makers.

The invention relates to the technical field of spatial data analysis, and particularly provides a commercial vitality space-time prediction and dynamic evaluation method based on multi-modal feature fusion, which comprises the following steps: collecting geographic space, text semantics, real-time behavior and economic attribute multi-modal heterogeneous data, and cleaning and desensitizing to obtain a structured data set; features are extracted from the single-modal data, and commercial vitality core feature vectors are obtained through cross-modal attention fusion in combination with an emotion-vitality association model; inputting the result into a space-time sequence prediction model, and outputting a commercial activity prediction result in a future preset time period; combining real-time updated data to construct a quantization-quality two-dimensional evaluation system, and performing real-time calculation by using a stream processing framework to obtain a dynamic evaluation result; and displaying and outputting a structured evaluation report through a visual panel. According to the method, timely and reliable decision support is provided for scenes such as business district operation optimization, business investment decision and regional economy monitoring, and the precision and real-time requirements of business vitality analysis are comprehensively met.

The invention relates to the technical field of occupational simulation training evaluation, and discloses a system for college student occupational scene simulation training evaluation. The system comprises a task analysis unit, a dynamic behavior modeling unit, a multi-mode interaction management unit, a state evolution tracking unit, a strategy adaptation unit and a comprehensive evaluation generation unit. The system receives the initial scene description and generates a structured task framework, and behavior parameters are configured for student roles; constructing a multi-modal interaction atlas by fusing aligned voice, action and sight line data in real time; identifying a key decision node, and dynamically tracking a team state evolution path; matching and adjusting strategies in a behavior rule base according to the evolution sequence, and outputting an adaptive intervention scheme; and finally generating a multi-dimensional structured evaluation report. According to the system, deep insight and dynamic and objective intelligent evaluation of complex interaction behaviors in the simulation training process are realized.

The invention provides an electromagnetic safety test system and method for switch integrated equipment, and relates to the technical field of power system switch equipment. The system is used for solving the problems that multi-parameter dynamic coupling analysis of a traditional test is insufficient, transient and gradual interference is difficult to capture by a fixed window, and comprehensive quantitative evaluation is lacked, and comprises a test module, an interference analysis module and a test evaluation module, calculating a total coupling interference variable; the interference analysis module draws a change curve based on the total coupling interference variable, analyzes a threshold over-area, a threshold duration and a peak value, and generates an interference risk index; and the test evaluation module calculates a total evaluation value and a threshold super-window proportion by combining the risk indexes of the windows, and divides safety levels. According to the invention, multi-parameter dynamic coupling simulation is realized, different types of interferences are accurately captured, a full-period dynamic evaluation system is constructed, and the accuracy of electromagnetic safety performance judgment of equipment is improved.

The invention discloses a multi-modal electrical equipment defect sample and cross validation evaluation system, and relates to the technical field of electrical equipment defect detection, and the system comprises a sample generation module which is used for carrying out the generative adversarial training according to a preset defect parameter vector and an environment working condition vector, and obtaining a modal defect sample library; the feature fusion module is used for performing cross-modal feature decoupling and graph attention fusion according to the modal defect sample library, and constructing a modal relation graph by separating the identity features of the target power equipment from the defect features to obtain joint feature representation; and the cross validation module is used for performing four-order cross validation operation according to the joint feature representation and the real defect data set to obtain a quantitative evaluation result. The technical problems that multi-modal samples are deficient, the cross-modal feature fusion effect is poor, identity features and defect features are difficult to separate, and the model evaluation reliability is low in existing electrical equipment defect detection are solved.

The invention discloses a low-speed unmanned vehicle artificial intelligence decision and performance evaluation system, and belongs to the technical field of low-speed unmanned vehicle scheduling, and the system comprises a generation module which is used for generating a scene novelty index through monitoring the instantaneous offset of an environment parameter; the topology module is used for constructing a dynamic scene topology according to the scene novelty index, and the dynamic scene topology comprises nodes and edge weights generated based on the scene novelty index; the decision-making module is used for converting the edge weight into a decision-making rule according to the dynamic scene topology and generating a decision-making strategy; the performance calculation module is used for tracking the deviation between the vehicle motion parameter and the control instruction during the execution of the decision strategy to calculate a performance disturbance coefficient; and a topology calibration module. The method can effectively adapt to unrecorded edge scenes such as sudden congestion and severe weather superposition, improves the generalization ability of a system to complex scenes exceeding a pre-training range, and reduces decision errors caused by insufficient scene adaptation.

The invention discloses an intelligent photoelectric control system dynamic parameter self-optimization method for intelligent manufacturing, and relates to the technical field of intelligent manufacturing and intelligent photoelectric control, and the method comprises the steps: sensing and preprocessing multi-dimensional parameters, collecting three types of parameters, and carrying out adaptive filtering, batch standardization and abnormal value elimination. Dynamic characteristic modeling and parameter mapping are carried out, an improved LSTM model is constructed, parameters are screened, and an incidence matrix is established; performing multi-objective optimization decision and parameter optimization, dynamically adjusting the weight in combination with working condition prediction, and performing optimization by adopting an NSGA-II algorithm; real-time parameter configuration and closed-loop control are carried out, parameters are converted into OPCUA instructions, the OPCUA instructions are transmitted through a TSN, and a double-collection-point closed loop is constructed; performance evaluation and self-adaptive correction are carried out, a five-dimensional evaluation system is established, and the model is corrected online when the deviation rate exceeds a threshold value. According to the method, multi-dimensional parameter accurate sensing and dynamic modeling are realized, parameter configuration is optimized, system precision is improved, the method is adaptive to a complex manufacturing scene, and long-term operation performance is ensured.

The invention relates to the technical field of model construction, can be applied to business scenes such as financial science and technology, and discloses a double-track evaluation driven optimization processing method, device and equipment and a medium, and the method comprises the steps: constructing a double-track evaluation system, analyzing training data, generating optimized training data, and building a target model; generating a capability index by utilizing a double-track evaluation system and multi-role agent simulation, and generating an alignment analysis result in combination with reinforcement learning performance; obtaining real service feedback indexes to form a training evaluation index set, and determining an attribution link; and generating training adjustment information based on the attribution link and driving the target model to be retrained to obtain an optimized target model for processing the service input data and outputting a service processing result. According to the method, the evaluation result and the service feedback are linked to realize a training closed loop, so that the capability gap of the model can be identified and corrected, and the compliance, the reliability and the application effect of the model in the actual service are improved.

The invention relates to the technical field of intelligent education, and discloses an innovation ability dynamic evaluation system and method based on multi-dimensional data fusion analysis, and the system comprises a process data collection module, a literacy feature extraction module, a dynamic evaluation engine and a teaching intervention engine. The method corresponds to the system. According to the method, multi-dimensional, dynamic and accurate evaluation of the innovation ability of the students is realized by constructing a complete data processing and evaluation closed loop, and specifically, performance characteristics of the students in innovation practice can be comprehensively captured by fusing and analyzing online and offline multi-modal process data; by means of a multi-mode cross-domain time sequence fusion model, dynamic tracking of the innovation ability development trajectory is achieved; finally, a personalized teaching intervention strategy is generated based on an evaluation result, a complete teaching closed loop of evaluation-diagnosis-intervention is formed, and the accuracy and teaching efficiency of innovative talent training are improved.

The invention discloses an intelligent error correction and style optimization system and method for business English writing, and relates to the technical field of natural language processing and artificial intelligence, and the system comprises the following components: a user interaction module, a data storage module, a language ability evaluation module and a self-adaptive error correction feedback and style optimization module. Through a multi-dimensional evaluation algorithm based on dynamic weight adjustment, historical writing data of a user is deeply analyzed, an evaluation system including four dimensions of grammar errors, vocabulary application, sentence structures and business profession is constructed, the system comprehensively evaluates the language ability of the user, and the user experience is improved. And the evaluation weight is dynamically adjusted according to the capability improvement rates of the users in different dimensions, so that the language capability levels of the users are more accurately divided, and the system can provide personalized error correction feedback and style optimization suggestions for the users with different language levels based on the evaluation result.

The invention discloses an urban sewage pipe network system operation and maintenance effect evaluation system and method, and belongs to the technical field of sewage pipe network operation and maintenance management. According to the method, through four core models of data quality scoring, improved AHP-entropy weight method combination weighting, pipe network toughness approximate quantification and public security risk Bayesian updating, robust evaluation of data incomplete and error scenes is realized; and in combination with a core-maneuvering-wide area three-layer three-dimensional perception network and a four-step progressive data governance process, outputting a system health index with a confidence interval, designing a double-layer performance payment function associated with data quality, and generating a data short board diagnosis report. According to the method, the problem that efficiency quantification is difficult in the landing of an effect-based payment mechanism is solved, an enhanced closed loop of evaluation, data improvement and accurate operation and maintenance is constructed, a scientific quantification basis is provided for performance management of the sewage pipe network, and the method is suitable for operation and maintenance evaluation of the urban sewage pipe network under various data conditions.

The invention relates to the technical field of automatic driving simulation, in particular to a performance evaluation method, system and device of an automatic driving simulation platform and a medium, and the method comprises the steps: constructing a simulation environment and a simulation vehicle equipped with a sensor, an actuator and a vehicle-mounted calculation unit simulation model; establishing a multi-dimensional platform evaluation system covering security, efficiency, response time and data processing capability, defining a specific sub-index, an evaluation method and a target data item needing to be collected for each dimension, and setting a comprehensive evaluation rule; running a closed-loop simulation test in the simulation platform, and synchronously collecting all target data; calculating evaluation results of the sub-indexes one by one based on the collected data; and judging whether the overall performance of the platform reaches the standard according to comprehensive evaluation rules. According to the invention, systematization, standardization and quantitative evaluation of the performance of the automatic driving simulation platform are realized, the reliability and comparability of a test result are improved, and an effective basis is provided for optimization of the simulation platform.

The invention discloses a method for evaluating the security performance of an information system. The method comprises the following steps: S1, matching an asset security baseline and compliance; s2, a dynamic security situation awareness step; s3, a multi-agent collaborative evaluation step; according to the information system safety performance assessment method, through real-time perception and trend prediction, the crossing from static assessment to dynamic early warning is realized, the adaptability to dynamic risks is improved, meanwhile, a multi-dimensional assessment system integrating asset compliance, dynamic situation and service toughness is established, and the assessment efficiency is improved. According to the method, firstly, multi-agent collaboration and topological correlation analysis are adopted, system key paths and deep correlation risks are accurately recognized, the risk recognition depth is improved, finally, evaluation full-process automation and model self-learning optimization are achieved, the evaluation efficiency is greatly improved, and the method is suitable for large-scale popularization and application. And the continuous advancement of the method is ensured.

The invention discloses an intelligent industrial system real-time defect detection and visual quality evaluation system and method based on a self-supervised visual Transform, and relates to the technical field of automatic visual detection and defect recognition in industrial manufacturing. The characterization capability of the visual Transform and the labeling efficiency of self-supervised learning are combined, and effective visual detection independent of labeling training data is realized. A hybrid Transform-convolutional backbone network, a multi-target SSL strategy and a hardware optimization deployment mechanism are integrated, so that the method is simultaneously suitable for high-precision defect positioning and real-time processing of an edge manufacturing system. And the abnormal scoring and positioning module is used for identifying and spatially positioning defects of different scales and types by utilizing the learned potential features. By providing a solution which is extensible, does not need to be labeled and has real-time capability, long-term challenges in the field of industrial visual inspection are solved, and the requirements of industrial 4.0 and 5.0 ecosystems are met.

The invention relates to the technical field of artificial intelligence and man-machine interaction, and discloses a dynamic evaluation system and method for robustness of a graphical user interface agent, and the system comprises a task execution environment module, an abnormal triggering mechanism module, a state verification module, a data acquisition module and an evaluation index calculation module. The intelligent agent enters a simulation environment and completes a circulation interaction process of perception-decision-execution-feedback, abnormal events are monitored, automatically triggered, abnormal scene generation and recovery are carried out, state verification is carried out, whether tasks are successfully completed or not is judged, multi-source data acquisition of continuously recording the tasks in the whole operation period is carried out, and the task execution time is shortened. The method has the beneficial effects that the evaluation environment is converted from a static state to a dynamic state, the authenticity is obviously improved, a semantic-driven exception injection mechanism is introduced, context-aware interruption is realized, the evaluation is more objective, and the method has good expandability and reusability.

The invention relates to the technical field of artificial intelligence safety and large language model evaluation, in particular to a dynamic unbalance benchmark evaluation system and method oriented to large language model safety alignment, and aims to solve the problems of model safety alignment evaluation distortion and alignment paradox caused by a traditional static benchmark test. The system comprises a meta-learning-driven adaptive sample generation module which adopts a meta-learning framework to construct a sample generator, automatically identifies and aligns weak links by monitoring a large model response mode and generates a new test case; the two-dimensional correlation index calculation module is used for calculating deception detection rate (DR) quantization intention deviation and capability retention (PR) evaluation core capability reservation; the time sequence evolution and threshold value self-adaption module is used for dynamically adjusting the threshold value according to the training stage; the multi-level imbalance scene library comprises 700 + imbalance scenes and is divided into four levels; and the evaluation-feedback-optimization closed loop module feeds back an evaluation result to the multi-layer cascade supervision system in real time, and dynamically adjusts a training strategy according to the DR and the PR.

The invention discloses an evaluation system of a medical large model, which comprises an evaluation report generation module, the compliance evaluation module, the basic model deployment evaluation module, the basic model discrimination module, the general safety evaluation module, the medical safety evaluation module, the application service deployment evaluation module, the timeliness evaluation module, the performance evaluation module and the page safety evaluation module are in communication connection with the system. The evaluation report generation module generates an evaluation report based on evaluation results of the compliance evaluation module, the basic model deployment evaluation module, the basic model discrimination module, the general safety evaluation module, the medical safety evaluation module, the application service deployment evaluation module, the timeliness evaluation module, the performance evaluation module and the page safety evaluation module. The evaluation system can be used for evaluating the performance and safety of the whole life cycle of the medical large model, and is simple to operate and relatively high in efficiency.

The invention discloses a metering performance analysis method, system and device for a single charging pile, and a storage medium, and relates to the technical field of charging pile metering performance analysis, and the method comprises the steps: collecting the operation data of a charging pile, calculating the error between an actual metering value and a theoretical estimation value of the charging pile, and calculating the calculation result; the total deviation degree of the error is quantized through a mean square error method; based on the Bayesian theory, carrying out uncertainty evaluation on the calculated errors, and integrating the distribution characteristics of the errors; constructing a multi-index comprehensive evaluation system, performing anomaly detection on the charging pile, generating an identification result of an abnormal charging pile and root positioning information of metering deviation, formulating an operation and maintenance strategy and a calibration period, and optimizing the multi-index comprehensive evaluation system; according to the method, the metering performance of the charging pile can be comprehensively evaluated, a deviation source is accurately positioned, an error overrun pile is identified, error fluctuation is reduced, the evaluation accuracy and consistency are improved, and powerful support is provided for operation, maintenance and calibration.

The invention specifically relates to a facility intelligent operation and maintenance decision-making method based on big data and artificial intelligence, and relates to the technical field of industrial facility operation and maintenance and intelligent decision-making, and the method comprises the steps: simulating the multi-dimensional operation state of a corresponding physical facility in a future preset time period after each candidate decision is executed; according to the method, the dynamic optimization of the operation and maintenance decision is realized by relying on the multi-scheme deduction capability of the digital twin and combining the non-dominated sorting genetic algorithm II and the dynamic weight evaluation system; the maintenance cost, the fault risk and the production efficiency are balanced by generating five types of candidate schemes, simulating the fault probability, the cost and the production efficiency within seven days and dynamically adjusting the weight based on the equipment health index, the production load and the like; a closed-loop feedback mechanism corrects model parameters through actual data, incremental learning and reinforcement learning continuously optimize decision experience, fault shutdown loss is reduced, operation and maintenance requirements in different scenes are met, and practicability and adaptability of the method are enhanced.

The invention discloses a service outsourcing personnel performance dynamic evaluation system based on federated learning and multi-source data fusion, and the system comprises the following modules: a data collection and preprocessing module which is used for collecting and preprocessing multi-source performance data; the feature extraction module is used for extracting performance features of the outsourcing personnel; the sparse data fusion module is used for feature screening and feature level splicing; the federated learning training module is used for training by adopting federated learning to generate a global performance evaluation model; the encryption protection module is used for encrypting the global performance evaluation model by adopting a Paillier encryption algorithm and a differential privacy technology; the dynamic evaluation module is used for dynamically adjusting and evaluating the performance of outsourcing personnel through an improved multi-arm bandit algorithm; and the performance log generation and visualization module is used for generating and visualizing a performance dynamic evaluation log. According to the method, federal learning and multi-source data fusion are combined, and safe, dynamic and accurate outsourcing personnel performance evaluation is realized.

The invention relates to the technical field of motor performance evaluation, in particular to a permanent magnet direct current pump motor performance evaluation method and system based on cloud computing. Comprising the steps of obtaining current waveform data and vibration waveform data, and obtaining a first criterion based on the current waveform data and the vibration waveform data; obtaining a second criterion according to the vibration waveform data; obtaining a third criterion according to the vibration waveform data; obtaining a fourth criterion according to the current waveform data and the vibration waveform data; judging the causal correspondence between the current step and the output change of the control loop to obtain a fifth criterion; continuously observing the change trend of high-frequency components in the vibration waveform data to obtain a sixth criterion; based on the first criterion, the second criterion, the third criterion, the fourth criterion, the fifth criterion and the sixth criterion, obtaining a classification result; and confirming measures according to a classification result. The problems of repeated false alarm, invalid maintenance and system reliability evaluation deviation caused by the fact that a permanent magnet direct current pump motor performance evaluation system cannot distinguish real mechanical faults under a low-speed cruise working condition in the prior art are solved.

The invention relates to the technical field of pollution source supervision, and discloses a pollution source off-site supervision method and system based on big data, and the method comprises the steps: building a dynamic verification rule library through coupling the operation beat data of enterprise key equipment and the total power consumption fingerprint data, and comparing the matching degree between the actual energy consumption and the expected mode in real time and generating a process credibility index. According to the invention, conversion from traditional pollution discharge data verification to production process stability verification is realized, and an unforgeable supervision trust mechanism is constructed through the physical relevance between the equipment start-stop signal and the power utilization characteristic; and meanwhile, the judgment accuracy under the extreme working condition is ensured by utilizing environmental parameter dynamic compensation and micro-differential pressure bypass verification, and finally a quantifiable accumulation enterprise environmental protection credit evaluation system is formed.

The invention relates to the technical field of energy storage optimization, and discloses an energy storage scheduling method and system based on digital twinning and reinforcement learning. The method comprises the steps of constructing an energy storage scheduling optimization model, performing multi-battery cooperative control, constructing a power grid twinborn model, optimizing the energy storage scheduling optimization model, and realizing energy storage scheduling based on the optimized energy storage scheduling optimization model. The system corresponds to the method. According to the application, an intelligent closed-loop architecture of'decision-verification-evaluation-learning 'is established, pre-security verification of the strategy is realized through a digital twin technology, quantitative auditing is performed on the strategy aftereffect through a multi-objective performance evaluation system, and finally, the evaluation result is used as an evolution feedback signal to drive online optimization of a core decision model, so that the decision-verification-evaluation-learning efficiency is improved. And the problems of mismatching and performance degradation of the existing energy storage scheduling model are fundamentally solved.

The invention discloses an assembly deviation interval rapid prediction method based on fuzzy comprehensive evaluation, and belongs to the field of assembly deviation interval prediction in the digital assembly process of aviation complex structural parts. The method comprises the following steps: constructing a simulation model of a target assembly body, executing Monte Carlo analysis, and extracting a manufacturing deviation value and an assembly deviation simulation value of a sampling sample; constructing a manufacturing deviation uncertainty quantitative model based on an assembly success rate function, wherein the assembly success rate function is obtained through probability density function integration and is defined as a fuzzy membership function; establishing an evaluation system containing three indexes of'over-small, moderate and over-large ', and calculating an assembly deviation comprehensive evaluation value; constructing a point cloud mapping model based on the assembly deviation simulation value of the sample and the assembly deviation comprehensive evaluation value, and fitting an assembly deviation prediction interval through a linear boundary function; and the accuracy of the prediction interval is verified through measured data. The method can rapidly and accurately predict the assembly deviation interval, reduces the calculation amount, and reflects the individual difference.

The invention relates to the technical field of vocational ability evaluation, in particular to a student-based vocational ability evaluation system and method, and the system comprises a data collection module which is used for collecting multi-source data and evaluation data of students; the vocational ability modeling module is used for establishing a vocational ability index system based on a vocational ability model comprising profession, method and social ability dimensions; and the data processing and calculating module is used for preprocessing the multi-source data, calculating ability scores of professional and method dimensions according to the vocational ability index system, and calculating ability scores of social ability dimensions through natural language processing, emotion calculation or behavior log analysis. In the invention, the multi-source data and the evaluation data of the trainees are collected, and the vocational ability model including profession, method and social ability dimensions is established, so that the problem that the comprehensive vocational ability of the trainees is difficult to comprehensively reflect due to the fact that most traditional vocational ability evaluation adopts questionnaires or single evaluation is solved.