374 results about "Assessment methods" patented technology

The invention discloses a homework semantic evaluation method based on a multi-modal large model, and relates to the technical field of education intelligent evaluation, and the method comprises the steps: 1, normalizing multi-modal answers into a step sequence, a scoring key point map, an acceptable answer set and an initial scoring anchor point, and building a step and key point bidirectional index; 2, performing consultation according to teacher roles, generating candidate scores and key credits, and calculating a key point coverage degree, a consistency degree and a contradictory proportion; and step 3, reading the conditional reliability ledger, generating a comprehensive weight, aggregating the comprehensive weight to obtain a final score, and outputting an uncertainty index at the same time. 4, giving out hierarchical personalized explanation, determining a minimum correctable step, executing step-level echoing, and generating a training list; 5, carrying out anchor question online calibration and fusion parameter updating, and setting a trigger proportion upper limit and an early stop threshold according to a budget perception route; the link realizes unified measuring scale, steady decision, traceable learning and controllable cost.

The invention relates to the technical field of psychological health, and discloses a psychological health multi-modal evaluation method, system and device and computer equipment, and the method comprises the steps: obtaining multi-modal data, and carrying out the feature extraction of the multi-modal data, and obtaining multi-modal features; performing emotion-oriented cross-modal attention mechanism analysis on the multi-modal features to respectively obtain a preset-dimension emotion scale vector, an emotion semantic feature, a multi-dimensional emotion feature and an uncertainty quantitative feature; fusing the preset dimension emotion scale vector, the emotion semantic feature, the multi-dimensional emotion feature and the uncertainty quantitative feature to obtain a multi-modal fusion feature; and obtaining a mental health assessment result based on the multi-modal fusion features and a preset multi-task learning framework. Through a cross-modal attention mechanism, deep semantic fusion of five modals of vision, audio, physiology, text and behavior is realized, and intelligent mapping from original multi-modal data to accurate psychological state judgment is realized through a deep learning technology.

The invention discloses a land value evaluation method and system based on multi-source heterogeneous data fusion, and relates to the technical field of land economic evaluation. The invention discloses a land value evaluation method and system based on multi-source heterogeneous data fusion. The method comprises four steps of multi-source data acquisition and preprocessing, standardized evaluation index quantification, index weighted fusion and value calculation, and value distribution diagram generation and output. Firstly, four types of data of background status, planning, market feedback and social perception are acquired and preprocessed; secondly, constructing a hierarchical index system to quantify indexes; determining a weight through an analytic hierarchy process and calculating a land value index according to a preset formula; and finally generating a value distribution diagram in a thermodynamic diagram form. The method realizes standardization, high efficiency and visualization of land value evaluation, can be applied to the fields of city updating, land market monitoring, government planning decision making and the like, and provides scientific support for related decision making.

The invention discloses a multi-scale full-period rainfall flood toughness evaluation method, equipment, a medium and a product, and relates to the field of rainfall flood toughness evaluation. The method comprises the following steps: firstly, according to a rainstorm flood action period and three spatial scales of drainage basin-city-drainage partition, constructing a multi-scale full-period rainfall flood toughness factor library; collecting and calculating each element data in the element layer in the whole cycle process of coping with the rainstorm flood effect under three spatial scales of drainage basin-city-drainage partition; screening out a final multi-scale full-period rainfall flood toughness element library based on the correlation between the elements; calculating the weight of each element in the final multi-scale full-period rainfall flood toughness element library by using an entropy weight method; on the basis of weight calculation, a VIKOR multi-criterion decision-making method is used to calculate rainfall flood toughness under each spatial scale, and a geographic detector is used to analyze elements with most significant influence from two aspects of the spatial scale and a rainstorm flood action stage, so that multi-scale full-period scientific, efficient and accurate evaluation of rainfall flood toughness is realized.

The invention relates to the technical field of reservoirs, in particular to a reservoir area dynamic submerging assessment method based on DEM and digital twinning, which comprises the following steps: acquiring multi-source data of a reservoir to be measured, and generating an initial DEM based on topographic data; performing depression correction and hydrological analysis on the initial DEM to obtain corrected DEM and depression-free water flow direction data; taking the grid of the corrected DEM as a minimum judgment unit, comparing the elevation of the grid with forecast water level data to delimit a preliminary submerging area, and further screening out an effective submerging range which is hydraulically communicated with a reservoir area main body of the reservoir to be measured from the preliminary submerging area through a seed spreading growth algorithm based on depression-free water flow direction data; and carrying out spatial association and analysis on the effective submerging range and the basic geographic information data, automatically identifying a flooded object, quantifying risk loss, and triggering early warning according to a preset grading early warning rule. The method can improve the evaluation precision and quantify risk efficient early warning.

The invention discloses a multi-disaster-bearing body marine disaster risk assessment method based on a sensitive matrix, and relates to the technical field of marine science, the method comprises the following steps: defining a marine disaster assessment area, and determining multiple disaster-inducing factor types in the assessment area; and sorting the distribution characteristics of various disaster-bearing bodies in the evaluation area, and determining an evaluation object. According to the multi-disaster-bearing body marine disaster risk assessment method based on the sensitive matrix, the sensitive matrix of the disaster-inducing factors and the disaster-bearing bodies is constructed, the sensitivity difference of different disaster-bearing bodies to the disaster-inducing factors is quantified, the loss difference of different disaster-bearing bodies under similar disasters can be accurately described, the limitation of a traditional single disaster assessment method is avoided, and the risk assessment accuracy is improved. The method comprehensively analyzes the potential threats of disasters to different disaster-bearing bodies, improves the overall precision of risk assessment, and guarantees that the vulnerability degrees of the different disaster-bearing bodies are accurately reflected when the different disaster-bearing bodies face specific disaster-inducing factors through sensitive matrix normalization.

The invention discloses a sleep quality evaluation method and system and a storage medium, and the method comprises the steps: inputting electrocardiosignal segmentation data into a trained sleep staging and breath detection multi-task model, and outputting a preliminary sleep staging result and an apnea detection result; based on the acceleration segmented data, extracting posture features of each corresponding time period, inputting the posture features into a decision tree posture classifier to obtain sleeping posture categories of each time period, and correcting a preliminary sleep staging result and an apnea detection result based on a preset posture and heart rate combined correction rule; and finally, calculating the corrected sleep staging result and the apnea result based on a preset sleep scoring rule to obtain a sleep quality evaluation result. Therefore, accurate quantitative evaluation of sleep staging and sleep quality is realized, and a reliable basis is provided for accurate intervention and monitoring of subsequent sleep health.

The invention discloses a multi-variable measurement sensor state lightweight evaluation method based on knowledge distillation, and belongs to the technical field of electric digital data processing and multi-sensor data fusion. The method comprises the following steps: firstly, carrying out time synchronization and physical consistency constraint modeling on original data of multiple sensors, and extracting feature representation; a high-precision teacher model is trained at the cloud end, and the high-dimensional mapping relation of the sensor state is learned; intermediate features, soft output and uncertainty information of a teacher model are extracted to serve as distillation knowledge, lightweight student model training is guided, and effective migration of discrimination knowledge is achieved in combination with soft label constraint, feature alignment and an uncertainty guiding mechanism; and finally, compressing, quantifying and optimizing the student model, and deploying the student model to a vehicle-mounted end to realize real-time evaluation and dynamic updating of the states of multiple sensors. According to the method, the model complexity is greatly reduced while the evaluation precision is ensured through a knowledge distillation framework, and efficient and reliable state perception and fault-tolerant control support is provided for an intelligent driving system.

The invention provides a multi-dimensional and dynamic digital assessment system and assessment method. Comprising a support module (comprising an index knowledge base, an algorithm operation base and an evaluation model base), a multi-source index fusion center (for constructing a three-dimensional data model and integrating a multi-dimensional data resource pool), a dynamic rule configuration engine (for realizing flexible configuration through a visual interface, a model adaptation mechanism and double-track verification) and a core business module (for managing assessment indexes, evaluating indexes and evaluating indexes). And evaluating the model and plan). The support module lays a basic resource layer, a fusion center constructs a data base, a configuration engine realizes flexible verification, the business module drives full-process execution, and full-process digital management from index creation, model configuration to examination execution is realized. The problems of iteration lag, multi-scene adaptation redundancy, weak data verification and the like of a traditional system are solved, index legality is guaranteed through static grammar detection and dynamic simulation operation, data traceability is achieved through a temporary database and full-link recording, and the dynamic response capacity, scene compatibility and data reliability of the assessment system are improved.

The invention provides a dual-drive deepwater wellbore integrity failure risk evaluation method, and belongs to the technical field of deepwater drilling safety, and the dual-drive deepwater wellbore integrity failure risk evaluation method comprises the following steps: based on fault tree analysis in a bowknot model, identifying a wellbore integrity failure mode; constructing a differentiated evaluation path according to the failure mode, and quantifying a static equipment failure risk and a dynamic structure failure risk respectively; constructing a wellbore system-level failure probability model through a fault tree and Bayesian network dynamic mapping mechanism; based on event tree analysis, modeling is conducted on the safety barrier state after shaft integrity failure, accident consequences are deduced, and the occurrence probability of the accident consequences is calculated; and outputting the system-level failure probability, the key path contribution degree, the bottom event sensitivity sequence and the accident consequence probability, and solving the technical problem that the system-level failure risk is difficult to accurately predict and dynamically prevent and control because static empirical data and a dynamic mechanical mechanism are separated and cannot be coupled and updated in real time.

The invention provides a debris flow risk assessment method and system based on debris flow parameter probability inversion calibration, and relates to the technical field of geological disaster risk assessment. According to the technical scheme provided by the embodiment of the invention, an effective means is provided for scientifically identifying debris flow parameters, and the method is particularly suitable for setting single gullies which are limited in detailed observation and cannot directly measure the parameters. A traditional method depends on statistical parameters or deterministic back analysis, the former introduces errors due to poor regional adaptability, and the latter has the problem of non-uniqueness of parameters; according to the scheme, the parameter posteriori distribution is updated by combining the Bayesian framework with historical observation data (debris flow influence areas), so that the standard deviation of key parameters is generally reduced. In the embodiment of the invention, the influence area is taken as core observation information, the practical limitation that the flow depth and the flow velocity are difficult to observe in real time in the debris flow event is avoided, the observation information can be obtained through post-event field investigation or satellite images, and the scheme is high in practicability.

This disclosure provides a method for assessing the health status of bearings. The method includes: receiving multimodal data and prompting information about the bearing; performing feature extraction on the multimodal data using a first set of agents to generate a feature set about the multimodal data; performing a health assessment on the bearing using a second set of agents based on the feature set and prompting information about the multimodal data to generate multiple health assessment results about the bearing; and combining the multiple health assessment results using a third set of agents to generate a health assessment report about the bearing. According to this bearing health status assessment method, users can input multimodal data to perform bearing health status assessments, thereby improving the flexibility of the bearing health status assessment method. Furthermore, the bearing health status assessment method of this disclosure, by leveraging the reasoning capabilities of a large language model, can better understand user questions, thereby providing better maintenance suggestions and other outputs.

The invention relates to the technical field of medical data analysis, in particular to a clinical team cooperation ability assessment method and system based on multi-modal data dynamic fusion, and the method comprises the steps: collecting physiological signal data based on wearable equipment, collecting audio / video data through an environment sensor, and collecting physical operation data through simulated human equipment; adding a timestamp and a scene label; carrying out sampling frequency synchronization and time axis alignment on the multi-modal data by taking the first-aid event triggering time as a reference; calling an attention mechanism based on the first-aid scene type, and automatically adjusting the weight of each modal data; and integrating the technical operation score and the non-technical cooperation feature to generate an individual and team visual evaluation report. According to the invention, the problem of blank collection of team non-technical skill data in traditional training is solved, and the effects of quantitative evaluation and real-time feedback of collaboration capability are realized.

The invention discloses an earthquake and secondary disaster vulnerability assessment method in combination with multiple agents and deep learning, and the method comprises the following steps: (1), collecting and preprocessing data related to an earthquake and an infrasound disaster thereof, and obtaining multi-source static data; key features are extracted from the multi-source static data; step (2), constructing a hierarchical structure index system of vulnerability assessment by utilizing expert knowledge, constructing a judgment matrix by adopting an analytic hierarchy process, and calculating the weight of each index to obtain an AHP weight vector; step (3), multi-agent system construction and disaster propagation dynamic simulation; step (4), designing a GCN + Transform deep learning model, and combining the multi-source static data with the dynamic evolution data to construct a spatial-temporal feature tensor as the input of the GCN + Transform deep learning model; and step (5), outputting a vulnerability evaluation result. According to the method, the problems of the existing earthquake and secondary disaster vulnerability assessment precision and timeliness can be solved.

The invention discloses a daily life activity ability assessment method and system based on a multi-branch space-time fusion network. The method comprises the steps of collecting motion data of a to-be-evaluated object, performing segmentation, noise reduction and standardization preprocessing, and extracting time-frequency domain features and motion deviation degree, motion similarity index and bilateral symmetry index provided by the invention; obtaining a uniform Bhattacharyya index scale score; and taking the motion feature data set and the clinical score as input, performing training based on a multi-branch space-time fusion algorithm, constructing a multi-task prediction model, and realizing automatic evaluation of the ADL. According to the method, the multi-branch space-time fusion network is innovatively introduced, the relevance between tasks is fully utilized, the spatial feature extraction capability is enhanced in combination with the graph neural network, adaptive fusion of the features is realized through a cross-layer interactive attention mechanism, the model prediction precision and evaluation reliability are remarkably improved, and the method is suitable for large-scale popularization and application. And an effective technical support is provided for rehabilitation evaluation and personalized treatment of patients with dyskinesia and disability and dementia.

The invention relates to the technical field of power infrastructure construction management, and discloses a constructor ability assessment method and system based on an intelligent model, and the method sequentially comprises a multi-source data collection and preprocessing step, an intelligent assessment model construction and training step, and a dynamic early warning mechanism triggering step. The system corresponds to the method. According to the construction personnel capability assessment method and system based on the intelligent model, the multi-dimensional capability associated data is collected and subjected to standardized processing, the traditional data dispersion barrier is broken through, and a high-quality data basis is provided for assessment; secondly, an evaluation result is generated through three-level quantitative transmission, and the limitation that traditional evaluation depends on subjective experience is broken; besides, risk possibilities are output and signals are pushed by constructing an early warning model, so that risk prospective prevention and control are realized, and the actual requirements of electric power infrastructure construction on accurate capability assessment and safety management and control of constructors are met.

The invention relates to the technical field of ecological environment monitoring, in particular to an environment assessment method and device based on carbon footprint and regional ecological data. The method comprises the following steps: acquiring carbon footprint data and regional ecological data of a target region; performing dimension alignment on the carbon footprint data and the regional ecological data to obtain an associated data set associated with a preset grid of the target region; and based on the associated data set, through at least one of a time sequence similarity analysis strategy, a spatial distribution overlapping degree analysis strategy and a causal relationship influence analysis strategy, determining an environment assessment result of the target area. Compared with the prior art, a manual evaluation mode is replaced, and the analysis efficiency is improved. And on the other hand, the carbon footprint data and the regional ecological data are aligned according to the preset grids of the region, so that the problem of non-uniform comparison dimensions and indexes is solved. And meanwhile, three analysis strategies are provided for reflecting the internal relation of the two types of data, so that the data analysis precision is improved.

The invention relates to a large-span bridge steel box girder fatigue damage local and overall collaborative evaluation method and system. Acquiring strain responses of a reference section and a test section of the steel box girder before and during traffic service based on a dynamic load test, defining and calculating a strain ratio and a rigidity ratio of each monitoring point, and diagnosing local crack positions of the steel box girder by utilizing statistical characteristics of the strain ratios; establishing a mapping relation between the rigidity ratio and local damage such as crack length, expansion rate and tip position, and proposing a fatigue crack safety coefficient to realize collaborative quantitative characterization of local damage and overall safety; and constructing a genetic algorithm to optimize a support vector machine model, and developing a real-time evaluation system which takes the real bridge strain response as input and takes the local crack damage type and the overall safety level of the steel box girder as output. The defect that local damage diagnosis and overall performance evaluation of the steel box girder are mutually separated in a traditional method is overcome, and real-time diagnosis and safety evaluation of fatigue damage of the large-span bridge can be synchronously achieved.

The invention discloses an intelligent demand analysis and man-hour evaluation method and system, and belongs to the technical field of computer vision and artificial intelligence. Analyzing the multi-modal demand document containing the text, the picture and the table to generate structured demand data; based on the structured demand data and the system module description, performing module-level splitting on the demand to generate a module task set; retrieving an associated system implementation context for each task in the module task set based on a multi-dimensional knowledge base; fusing the structured demand data, the module task set and the system implementation context to generate a fine-grained technical task; based on a historical case index, calculating reference working hours for the fine-grained technical task; and cross-module cooperative correction and personalized correction based on the ability of the task executor are sequentially performed on the reference working hours to obtain final evaluation working hours, so that automatic deep analysis and accurate and personalized working hour evaluation of software requirements can be realized.

A rock burst risk dynamic assessment method based on a Bayesian method comprises the steps that a stope working face is divided into M * M regular grids, and each grid serves as an independent assessment unit; collecting geological and mining data, selecting a plurality of influence factors based on historical data and expert knowledge, and quantifying the plurality of influence factors by adopting a static evaluation model; determining weights of a plurality of influence factors by using an analytic hierarchy process, and calculating an initial risk score of each evaluation unit by using a comprehensive index method; normalizing the initial dangerousness score into prior distribution by adopting an S-type function, and constructing an initial dangerousness prior probability field; a power law attenuation model is introduced to calculate influence weights among the evaluation units, and an influence weight matrix is constructed; real-time monitoring data are obtained through a micro-seismic monitoring system, the risk probability of each evaluation unit is updated in real time based on the Bayesian theorem, and dynamic evaluation and evaluation result output are achieved. According to the method, the accuracy and the real-time performance of rock burst risk assessment can be effectively improved.

The invention provides a federated learning and privacy calculation-based cross-border earthquake risk assessment method, which solves the fundamental contradiction between data sovereignty and privacy security in cross-border data sharing by constructing a federated learning architecture fusing hierarchical desensitization and hybrid encryption. Through the multi-source data cooperative training and CNN-LSTM mixed disaster chain model, the problem that the assessment precision is insufficient due to the fact that a single country model cannot describe a complex cross-border disaster chain is solved; finally, a cross-border disaster scene is constructed, and the defect that a static evaluation tool is disjointed with an actual combat is overcome through a multi-agent dynamic policy simulation system; according to the method, the security collaboration under the condition that the data is not out of the scene is realized, the multi-country collaboration participation rate is improved by 60%, the risk assessment accuracy rate is improved to 92% or above, the policy assessment period is shortened from several months to several hours, a quantifiable scientific basis is provided for cross-border emergency decision making, and the joint prevention and joint control capability of regional earthquake disasters is effectively improved.

The embodiment of the invention provides a multi-dimensional evaluation method and system for power supply toughness, and the method comprises the steps: obtaining a modeling basic data set, and constructing a power grid digital twinborn body: constructing an initial power grid digital twinborn body; coupling simulation results of electromagnetic transient simulation and electromechanical transient simulation to obtain multi-time-scale hybrid simulation data, and correcting the twin; carrying out simulation by using prefabricated disturbance scene simulation data; calculating a multi-dimensional safety evaluation index of the digital twin of the power grid according to a simulation result; determining a multi-target reward function of the agent group; performing simulation by using the intelligent agent group to obtain optimization strategy parameters of the photovoltaic inverter and the energy storage converter; parameters of the digital twin of the power grid are simulated and adjusted again; simulation output data is obtained after simulation, and a multi-dimensional evaluation result of power supply toughness is obtained through a clustering algorithm. The problems of insufficient evaluation precision, long verification period and high test risk caused by lack of a high-fidelity model in the parallel operation AC power grid are solved.

The invention belongs to the field of rock mechanics and geotechnical engineering, and relates to a high-position long-distance landslide risk and vulnerability real-time evaluation method based on micro-fracture, which comprises the following steps: (1) monitoring earth surface deformation of a research area based on synthetic aperture radar data; (2) identifying a potential high-position remote landslide based on surface deformation feature analysis; (3) in combination with on-site geophysical prospecting information and geological profile characteristics, constructing a slope numerical model of the potential high-level remote landslide; (4) constructing a micro-fracture feature database based on micro-fracture time, space, quantity and energy; and (5) dynamically evaluating the risk and vulnerability of the potential high-position remote landslide based on micro-fracture characteristics. According to the evaluation method, on the basis of synthetic aperture radar data and ground surface deformation characteristic analysis, in combination with field geophysical prospecting information, geological profile characteristics and numerical calculation, the risk and vulnerability of potential high-level remote landslide are dynamically evaluated, the limitation of traditional static evaluation is overcome, and the evaluation efficiency is improved. And the prevention and control capability of high-position remote landslide disasters is obviously improved.

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

The application discloses a spoken language evaluation method and device, related equipment and a computer program product. The method comprises the following steps: obtaining the answer data of a testee, wherein the answer data comprises a question, the answer audio of the testee and a reference answer; identifying the answer text corresponding to the answer audio; obtaining the reasoning score of the testee by combining the answer text and the answer data and through a configured reasoning scoring model; obtaining a configured calibration model, wherein the calibration model is obtained by pre-training based on the answer text of a calibration testee, the reasoning score of the calibration testee and an expert score; the calibration testee is part of the testees participating in the current oral test; and obtaining the final score of each testee by using the calibration model to score according to the answer text and the reasoning score of each testee. Compared with the prior art which determines the score by simply calculating the similarity between the answer text and the reference answer, the oral evaluation result obtained by the application is more accurate.

This invention relates to the field of big data analytics, specifically to a method and system for intelligent analysis and assessment of risk perception bias among residents in post-disaster reconstruction areas. The method involves fitting risk indicators using multi-source data and a collaborative model, and calculating risk assessment values ​​based on the fitting results. It obtains basic information data, subjective perception data, and resident behavior data through structured questionnaires. Initial correction coefficients are matched to the subjective perception data based on the basic information data, and error analysis is performed using resident behavior data to determine whether dynamic compensation of the initial correction coefficients is necessary. This significantly improves the accuracy of the final obtained true subjective perception data. By assessing residents' risk perception bias from two perspectives—true subjective perception data and risk assessment values—the method ensures the accuracy and comprehensiveness of the final results, achieving intelligent analysis and accurate assessment of risk perception bias among residents in post-disaster reconstruction areas.

This invention discloses a multimodal document parsing and evaluation method and system for bidding and tendering audit scenarios. First, it collects the tender documents and bid documents to be audited to obtain a set of documents to be audited. Then, it identifies the document format attributes in the set of documents to be audited to obtain initial heterogeneous corpus. Next, it performs page-level multimodal detection on the initial heterogeneous corpus and obtains detection tags. This invention achieves the function of accurately constructing audit corpus from massive heterogeneous documents using a vision-enhanced hybrid parsing strategy, and then performing multimodal document parsing and evaluation using image processing algorithms. Furthermore, the dual-modal risk assessment model combining hard and soft rules not only solves the problems of low efficiency, incomplete coverage, and easy omission of hidden risks in existing manual review, but also solves the parsing difficulties caused by poor scanned document quality. This not only makes audit compliance review more intelligent and accurate, but also significantly improves the quality and efficiency of the review.

The invention provides a forest fire extinguishing task safety risk assessment method based on AHP-FCE, and belongs to the technical field of forest fire extinguishing safety risk assessment, and the method specifically comprises the following steps: S1, designing a forest fire extinguishing task safety risk index system; s2, constructing a forest fire extinguishing task risk quantitative evaluation model; step S21: determining a risk factor weight by an analytic hierarchy process; step S211: constructing a hierarchical structure model; step S212, constructing a judgment matrix; step S213, calculating the relative weight of each level of factors; step S22, risk quantitative evaluation is carried out by a fuzzy comprehensive evaluation method; step S221, constructing a comprehensive risk assessment scoring table; step S222, performing a fuzzy comprehensive evaluation method; according to the method, the analytic hierarchy process and the fuzzy comprehensive evaluation method are combined, so that the subjectivity of traditional experience judgment is avoided, and the complexity of multi-factor coupling analysis is solved.

The invention discloses a multi-process background field comprehensive evaluation method and a related device. The method comprises the following steps: constructing a multi-process characterization element set facing a power weather forecast demand; inputting the multi-process characterization elements in the multi-process characterization element set into a comprehensive evaluation model based on evaluation index classification and different standardization technologies to obtain each background field index value after evaluation; performing nonlinear score assignment on each evaluated background field index value by adopting a Gaussian attenuation method, and endowing each evaluated background field index value of each process in each background field with a weight coefficient; and calculating a comprehensive score of each background field according to the non-linear score assignment result of each background field index value after evaluation and a weight coefficient assigned by each background field index value after evaluation of each process in each background field, and evaluating each background field according to the comprehensive score of each background field. According to the method and the related device, the systematicness deviation between the background field screened by the method and the electric power weather forecast demand is relatively low.

This application relates to an interview assessment method, apparatus, computer device, storage medium, and program product based on a large language model. It obtains initial interview content for a target subject, then uses a large language model to perform completeness assessment, credibility assessment, ability internalization assessment, and preliminary ability assessment. Based on the preliminary ability assessment results, it conducts personalized follow-up questions to obtain the target interview content. Finally, it uses the large language model to perform a comprehensive ability assessment to determine the overall ability assessment result for the target subject. Through multi-dimensional and multi-level analysis, it improves the accuracy and objectivity of interview assessment; by utilizing the semantic understanding capabilities of the large language model, it achieves intelligent follow-up questioning and adaptive assessment, improving the intelligence level of interview assessment. This not only significantly reduces manual costs but also improves the efficiency of interview assessment. It achieves intelligent, standardized, and highly efficient interview assessment.