33 results about "Evaluation Conclusion" patented technology

The invention relates to the technical field of asset assessment. The asset evaluation intelligent system based on the AI technology is provided, and the system comprises the steps that web crawler capture processing is conducted on a data source of a public network channel, and a first type of heterogeneous data is obtained; aPI interface docking processing is carried out on the data source of the enterprise internal system, and second-class heterogeneous data is obtained; performing logic splicing processing on the first type of heterogeneous data and the second type of heterogeneous data to generate multi-source heterogeneous asset evaluation data; performing model matching processing on the asset value characteristic matrix based on the asset type, and selecting an estimation model architecture; performing interpretability analysis processing on the dynamic evaluation parameters to generate a key factor contribution degree and an interpretable evaluation conclusion; and performing document processing on the contribution degree of the key factor and the interpretable evaluation conclusion to generate an interactive evaluation document so as to improve the multi-source data integration capability, enhance the risk dynamic evaluation adaptability and strengthen the real-time relevance and interpretability of the evaluation document.

The invention belongs to the technical field of open source intelligence assessment, and provides an open source intelligence assessment method and device based on context information, and the method comprises the steps: responding to an open source intelligence assessment task, and determining at least one cognitive dimension; acquiring context information corresponding to the intelligence; under the constraint of the context information, retrieving corresponding entity information and relation information thereof from an intelligence knowledge graph according to each cognitive dimension; and according to the entity information and the relationship information of each cognitive dimension, obtaining a first dimension score of the cognitive dimension, and integrating the score and the context information to generate an evaluation conclusion.

The invention relates to the technical field of information security, in particular to a penetration test effect evaluation method and system based on an attack mode framework, and the method comprises the steps: firstly, constructing an evaluation model comprising a target layer, a criterion layer and an index layer; the evaluation indexes under each criterion are weighted through a large language model, and the indexes are quantitatively scored by experts. And for each criterion, on the basis of a grey theory, calculating a comprehensive evaluation vector of the criterion by using index scores, weights, a preset comment set and a whitening weight function so as to obtain a quantitative score and a qualitative evaluation result. And finally, calculating a final comprehensive evaluation result of the target layer by integrating the quantitative score vectors and the criterion weights of all the criteria. During decision making, the final result needs to be combined with the qualitative evaluation conclusion of each criterion so as to formulate a comprehensive and targeted security reinforcement strategy. According to the invention, the accuracy of penetration test effect evaluation is improved.

The invention relates to the technical field of skill cultivation and evaluation, in particular to a network technology skill cultivation and evaluation system based on a practical training platform, in the network technology skill cultivation and evaluation system, an operation behavior sequence monitoring module collects an operation behavior sequence including a real-time monitoring sequence and a complete recording sequence; the abnormal behavior evaluation and verification module analyzes the monitoring sequence in real time in the evaluation process, compares the monitoring sequence with a normal operation behavior sequence model to generate dynamic analysis data, and performs overall comparison on a complete record sequence and an evaluation standard sequence in combination with the data after the evaluation is finished to generate a verification result; and the decision module finally outputs a skill evaluation conclusion by applying a predefined decision model according to the dynamic analysis data and the verification result, so that full-process and multi-dimensional accurate evaluation from real-time dynamic anomaly monitoring to post global integrity verification is realized, and the accuracy and credibility of the evaluation conclusion are remarkably improved.

An intelligent question and answering method, comprising: performing retrieval on the basis of a first user query in a current conversational turn, so as to obtain first retrieved knowledge required in the current conversational turn; processing the first retrieved knowledge and the first user query to obtain response text; on the basis of the first retrieved knowledge, the response text, and user feedback to the response text, performing a reflective evaluation on the first retrieved knowledge, so as to obtain a reflective evaluation conclusion, the reflective evaluation conclusion being used for indicating quality of a cited snippet in the first retrieved knowledge, and the cited snippet being a snippet referenced by the response text; when the quality of the cited snippet does not satisfy a requirement, optimizing, on the basis of the cited snippet, second retrieved knowledge required in a next conversational turn, the second retrieved knowledge being obtained by performing retrieval on the basis of a first cited snippet and a second user query in the next conversational turn. The method allows for erroneous knowledge in retrieved knowledge to be corrected in a timely manner, thereby reducing the probability of introducing erroneous knowledge, and improving answer quality of neural network models.

The invention discloses a construction and evaluation method for an applicant drug clinical test quality management system, and aims to solve the problems that traceable evidence chains are lacked among applicant clinical test quality system elements, evidence materials and evaluation conclusions, evaluation is difficult to quantify, and closed-loop improvement based on evaluation results is difficult. According to the method, quality system element data, evidence material data and evidence source data are obtained and standardized, and a time heterogeneous evidence graph comprising element nodes, evidence nodes, version nodes, responsibility nodes and evidence source nodes is constructed; executing graph neural network representation learning on the evidence graph to generate a relationship likelihood parameter and an evidence credibility likelihood parameter; constructing a Bayesian factor graph containing conflict factors, and executing belief propagation to obtain element compliance posterior distribution and evidence credibility posterior distribution; and further calculating a compliance score, an uncertainty score and a coverage rate score to form a three-dimensional score result, and extracting an evidence chain to generate traceable output, thereby achieving the technical effects of automatic establishment of the evidence chain, compliance quantitative evaluation, conflict evidence suppression and evaluation result driven closed-loop improvement.

The invention relates to a method and device for generating evaluation questions in the field of spaceflight science and technology and a medium, and the method comprises the steps: judging whether corpora of a first corpus meet correlation conditions of the field of spaceflight science and technology or not according to a subject classification table of the field of spaceflight science and technology; removing the corpora which are judged to be satisfied from the first corpus, and putting the corpora into another corpus to obtain a second corpus; according to the subject classification table, classifying the corpora of the second corpus according to subjects; extracting a first preset number of corpora from the corpora of each subject; according to the first preset number of corpora extracted from all the subjects, aerospace science and technology field evaluation questions meeting the number balance condition are generated. According to the method and the device, the defect that the evaluation conclusion is easy to deviate due to unbalanced distribution of the number of evaluation questions in the field of spaceflight science and technology in related technologies is relieved.

The invention discloses a verification optimization method and system for a DDR3 data eye pattern training result. According to the method, after single training is completed, firstly, phase adjustment values of a plurality of DQ signals in the same group are obtained, then the discrete degree statistical magnitude of the phase adjustment values is calculated, and an outlier threshold value is determined based on an outlier detection algorithm. And comparing the statistical magnitude with a threshold value, and outputting an evaluation conclusion according to a preset rule. If the result is unreliable, the system automatically adjusts and optimizes training parameters and reinitiates training according to the abnormal mode of the statistical characteristic quantity; if the result is reliable, the configuration is locked immediately. The objective quantitative evaluation of the training result is realized, and the abnormal configuration is effectively identified and eliminated. Through a closed-loop mechanism of automatically adjusting parameters and retraining, manual intervention is greatly reduced, and debugging precision and efficiency are improved.

The invention discloses a damping material support performance evaluation method and system, and relates to the technical field of damping material support performance evaluation, and the method comprises the steps: obtaining a finished damping material support, a modular clamp, a calibration part and a loading spectrum library; the clamping calibration piece applies calibration excitation and collects calibration response data, clamping boundary parameters are determined, and a boundary compensation parameter set is generated; clamping a finished product bracket, applying a preset clamping condition, and determining a test excitation sequence corresponding to the preset working condition set according to the loading spectrum library; applying a test excitation sequence to acquire support response data, and performing compensation based on the boundary compensation parameter set to obtain equivalent evaluation data; calculating a dynamic equivalent parameter set based on the equivalent evaluation data and constructing a finished product performance fingerprint spectrum; and outputting an evaluation conclusion according to the finished product performance fingerprint, wherein the evaluation conclusion comprises a non-risk working condition set judgment result and a risk working condition set judgment result. According to the method, the comparability and the recheckability are improved, the misjudgment risk is reduced, and the consistency evaluation and model selection reliability is enhanced.

The application provides a risk assessment method and device, electronic equipment, medium and program product, which can be applied to the field of artificial intelligence technology. The method comprises the following steps: obtaining evaluation data of a to-be-evaluated object; generating corresponding preliminary evaluation conclusions by using a plurality of first agents, wherein the plurality of first agents evaluate based on different risk dimensions; determining the association relationship between the preliminary evaluation conclusions; generating at least one adversarial challenge based on the association relationship, wherein the adversarial challenge at least comprises a challenge point and a target preliminary evaluation conclusion identifier associated with the challenge point; for each adversarial challenge, using the target preliminary evaluation conclusion identifier to output a response conclusion by using a target first agent, wherein the response conclusion comprises at least one supplementary information, and the supplementary information is used to form a response to the challenge point; and generating a risk evaluation result based on the preliminary evaluation conclusion, the adversarial challenge and the response conclusion.

The embodiment of the invention provides an evaluation method and device of an intelligent question answering system, computer equipment, a computer readable storage medium and a computer program product, and belongs to the technical field of artificial intelligence. The method comprises the following steps: acquiring an ith theme type test case belonging to the same target theme, wherein the theme type test case comprises a test question and an expected answer corresponding to the test question; inputting a test question in the ith theme type test case into the intelligent question-answering system, and obtaining an output answer of the intelligent question-answering system; calling a plurality of judgment models by taking the expected answer and the output answer as input so as to obtain a plurality of groups of evaluation conclusions; each group of evaluation conclusions comprises a plurality of evaluation conclusions output by the judgment model corresponding to the group for a plurality of preset index dimensions; and for each index dimension, determining a final evaluation conclusion of each index dimension based on the evaluation conclusions of all theme class test cases under the target theme. According to the technical scheme, the evaluation accuracy and comprehensiveness can be improved.

The invention relates to the technical field of spaceflight measurement and control communication, in particular to a receiving performance evaluation method for a spaceflight ground measurement and control system. The method comprises the steps of task data acquisition and preprocessing, G / T value time sequence calculation, multi-dimensional receiving performance evaluation, evaluation conclusion generation and the like, and realizes multi-dimensional evaluation of index dimension, capability dimension and trend dimension. A quantitative and qualitative aerospace ground measurement and control system receiving performance evaluation conclusion is formed and comprises a qualitative evaluation conclusion (normal, decline and deterioration), a quantitative score F (score 0-100) and a trend evaluation conclusion (decline and deterioration estimation date: T2), an image-text evaluation report is formed, and decision-making assistance is provided for equipment scheduling use and repair maintenance.

The invention discloses a sucker rod corrosion fatigue influence factor analysis method based on double-model fusion, and belongs to the field of safety assessment of oil exploitation equipment. In order to solve the problems that in the prior art, analysis dimensions are limited, and prediction precision and model interpretability are difficult to consider at the same time, the method comprises the steps that firstly, a high-dimensional parameterized data set is generated through finite element simulation; secondly, analyzing and visualizing the independent nonlinear influence trend of each factor by adopting a generalized additive model (GAM); meanwhile, a high-precision XGBoost prediction model is constructed, and the global importance, local dependence and complex interaction of each factor are quantified in combination with an SHAP method; and finally, fusing double-model results to carry out cross validation and comprehensive evaluation. According to the method, high-precision prediction and high interpretability are combined, comprehensive insight of a multi-factor coupling effect is realized, and the robustness and credibility of an evaluation conclusion are remarkably improved through a built-in verification mechanism.

The application discloses a kind of structured thinking chain security evaluation methods and systems for large model generation content, the method includes the following steps: S1: input data preparation;Measured large model receives user query and outputs response content, the response content is used as the input of evaluation model;S2: the evaluation model uses structured thinking chain evaluation model, evaluation model carries out multidimensional analysis according to predefined XML template, generates structured reasoning chain;S3: structure causal model carries out logical self-consistency test to the structured reasoning chain, calculates the probability distribution of each dimension judgment;S4: calculate composite reward, according to composite reward function, calculate evaluation score, output final evaluation conclusion and structured reasoning chain.The white-box evaluation method of the application is combined by structured thinking chain (SCoT) and structure causal model (SCM), realizes that logic is self-consistent, and data utilization is high.

This application relates to a performance evaluation system for a turbine speed control system oriented towards uncertainty handling, including an uncertainty source quantification module, a functional evaluation module, a hardware evaluation module, a comprehensive evaluation module, and a result output module. The uncertainty source quantification module is used to identify three types of uncertainty sources in the operation of the turbine speed control system: data noise, operating condition fluctuations, and fault determination ambiguity, and quantify their impact. The functional evaluation module and the hardware evaluation module respectively conduct evaluations with uncertainty correction on the functional execution effect and hardware equipment status of the speed control system. The comprehensive evaluation module integrates the evaluation results of the two modules and dynamically allocates weights. The result output module outputs evaluation conclusions containing credibility and uncertainty source tracing. This application provides scientific support for preventive maintenance and accurate fault location of turbine speed control systems, effectively improves the robustness and engineering adaptability of evaluation results, and ensures the stable operation of the speed control system under complex operating conditions.

The invention relates to the technical field of ecological environment assessment, and discloses a grassland resource asset value assessment method based on regionalization adaptation and dynamic monitoring, and the method comprises the following steps: S1, obtaining and processing grassland resource multi-source data; s2, parameter uncertainty quantification and key sensitive area diagnosis; and S3, generating a self-adaptive non-uniform evaluation grid. According to the method, the uncertainty of the key ecological parameters is quantified, and the key sensitive area is diagnosed based on the value distribution entropy, so that weak links and high-influence areas in the evaluation result can be effectively identified, and the overall reliability of the evaluation conclusion is improved; on the basis, a spectrogram clustering algorithm fusing ecological similarity, sensitivity and type constraint is utilized to generate a self-adaptive evaluation grid, the defect that a traditional fixed partition mode is difficult to adapt to spatial heterogeneity and dynamic change is overcome, and the spatial accuracy of evaluation is remarkably improved.

The invention relates to the technical field of commercial password application security assessment, and particularly discloses a commercial password application security assessment report generation system and method, which can realize standardized management of password assessment evidence pictures and support dynamic reference through classified collection of variable and constant data. And the data processing module calculates a comprehensive score according to a specific algorithm, generates an evaluation conclusion, ensures data consistency in combination with asset verification, and finally outputs a complete secret evaluation report in a fixed format. According to the method, full-process automation of data acquisition, processing, verification and output of the secret evaluation report is realized, report generation efficiency and accuracy are improved, and report normalization and data consistency are guaranteed.

The application discloses an AI-assisted attack tree-based vehicle safety evaluation method, device, equipment and medium, and belongs to the technical field of computers. The method comprises the following steps: automatically generating an attack tree containing a dynamic risk level based on a vehicle architecture by adopting a hierarchical intelligent agent workflow; extracting a threat scenario from the attack tree and generating a structured description; matching a historical knowledge base by using a retrieval enhancement generation technology, and generating an asset list and a threat score by a large language model; and generating an evaluation conclusion containing a safety disposal suggestion by comprehensively combining the attack tree and the score result. The technical scheme can automatically generate an attack tree based on a vehicle architecture and link intelligent scoring, finally output a safety evaluation conclusion containing specific disposal suggestions, and significantly improve the evaluation efficiency, accuracy and engineering practicability.

The invention relates to a test case quality evaluation method and system based on reflection distillation. The method comprises the following steps: inputting an initial test case data set into a teacher model, and judging logic to generate a thinking chain; inputting the thinking chain into the evaluation model, verifying reasoning validity, calculating a consistency score, and screening out high-quality thinking chain data; taking the high-quality thinking chain data as a fine tuning data set, and performing fine tuning training on the student model to obtain a test case quality evaluation model; receiving a to-be-evaluated test case, and performing element disassembly on the to-be-evaluated test case to obtain a precondition, an execution action and an expected result; similarity matching is carried out on the basis of the execution action and actions in an action vector library, and historical test cases corresponding to the candidate actions are obtained to serve as candidate evidences; condition evaluation and sorting screening are carried out on the candidate evidences, and support evidences are obtained; and inputting the to-be-evaluated test case and the support evidence into the student model, and outputting a test case quality evaluation conclusion.

The application discloses a kind of based on parameterization model's precision retention evaluation method of roller linear guide pair, belong to high-end numerical control machine tool and precision equipment manufacturing technical field.It includes: step 1: establish the precision degradation theory model of roller linear guide pair.Step 2: set the parameter system required for evaluation.Step 3: execute parameterized simulation analysis.Step 4: extract precision degradation law and establish mapping relationship.Step 5: generate evaluation conclusion and optimization strategy.Compared with prior art, the present application has the following significant advantages: efficient prediction, low cost;The established degradation theory model couples elastic deformation, wear and creep, more in line with the actual complex failure physical process of guide pair, improve the accuracy of long-term prediction.The present application can not only be used for performance estimation and scheme comparison in new product design stage, but also can be used for state evaluation, residual life prediction and maintenance planning of in-service machine tool guide, and the application scenario covers the whole life cycle.

The invention discloses a model interpretation-based binary code similarity detection robustness evaluation method, which comprises the following steps of: training a mixed regression model as a locally interpretable agent model, and introducing a fusion lasso regularization item to approximate a local decision boundary of a target model; according to the method, the internal structure and parameter information of the target model do not need to be obtained, black box evaluation is achieved, and the limitation of an existing gradient-based method in a real black box scene is solved. According to the method, the proxy model is used for obtaining the feature importance weight, the instruction feature mapping module is innovatively introduced, the module can process basic block or graph node features of different granularities and accurately map the basic block or graph node features to specific instructions, direct positioning from model decision making to key instructions is achieved, and the method is high in practicability and easy to popularize. According to the method, the evaluation result is quantified through multi-dimensional indexes such as the attack success rate and the disturbance efficiency, so that the evaluation conclusion is more scientific and reliable.

The invention relates to the technical field of automatic analysis and verification, in particular to an intelligent examination system and method for a dam safety evaluation report. Performing multi-modal preprocessing on the target safety evaluation report to obtain preprocessed report data; performing chapter content fine-grained identification on the preprocessed report data to obtain chapter identification data; performing deep semantic understanding on the chapter identification data to obtain key safety evaluation data; identifying preset standard specification data, and constructing a hierarchical review structure to perform content integrity review and process rationality review on the key safety evaluation data; key safety evaluation data of the dam historical safety evaluation report is used as historical reference data; performing data trend analysis on the key safety evaluation data of the target safety evaluation report according to the historical reference data; based on content integrity review, process rationality review and data trend analysis, reliability rating of the evaluation conclusion is judged; according to the invention, risk early warning is carried out through reliability rating of the safety evaluation report.

The invention relates to the technical field of neural networks, and discloses a data interpretable prediction method and device based on a tree regular variational encoder, and the method comprises the steps: encoding target high-dimensional data to a submerged space through a variational automatic encoder, and obtaining latent variable representation; performing regularized alignment processing on the latent variable representation based on the agent decision-making tree so as to enable a distribution structure of the latent variable representation in the submerged space to be consistent with a rule path of the agent decision-making tree, and generating a regularized submerged space; performing classification prediction based on the regularized subsurface space, and outputting an interpretable prediction result; performing feature attribution analysis processing on the prediction result to generate a feature importance sequence; and performing interpretability verification on the prediction result based on the feature importance ranking and the rule path to obtain a corresponding interpretability evaluation conclusion. According to the method, the prediction performance and the interpretability of the prediction result can be considered at the same time in a high-dimensional data scene, and the credibility of the interpretable result is guaranteed.

The invention provides a large language model test and code generation quality evaluation method, device and equipment, and relates to the technical field of artificial intelligence, in particular to the cross technical field of large language models, software engineering automation and code quality evaluation. According to the specific implementation scheme, the method comprises the following steps: constructing a structured evaluation data set; based on the structured evaluation data set, driving the to-be-evaluated large language model to execute a first evaluation task and a second evaluation task; respectively extracting corresponding executable code blocks from responses of the large language model to the first evaluation task and the second evaluation task; executing the extracted code block in a code execution sandbox, and collecting an evaluation index corresponding to the task; and analyzing the evaluation indexes of the first evaluation task and the second evaluation task, and outputting a performance evaluation conclusion of the large language model based on the index difference.

The application discloses a lead-zinc tailing processability evaluation method and system, relates to the technical field of tailing recycling, and comprises the following steps: obtaining a lead-zinc tailing sample, detecting the lead-zinc tailing sample, obtaining basic performance data corresponding to the lead-zinc tailing sample, inputting the basic performance data into a trained lead-zinc tailing process performance prediction model for prediction, obtaining process performance prediction information output by the lead-zinc tailing process performance prediction model, obtaining process requirement parameters of a target product, comparing the process performance prediction information with the process requirement parameters of the target product, obtaining a comparison result, generating a processability evaluation result of the lead-zinc tailing sample for the target product according to the comparison result, and establishing a prediction and evaluation system based on data driving, predicting the process performance of the lead-zinc tailing, and generating the processability evaluation conclusion. The application overcomes the limitations of traditional experience judgment, and improves the pertinence and economy of tailing resource utilization.

The invention relates to the technical field of energy storage systems, in particular to a digital twinning-based network construction type energy storage system evaluation method, and aims to solve the problems that an existing evaluation method is incomplete in scene coverage and low in precision and does not fully consider multi-physical domain coupling and component performance degradation. The method comprises the following steps: constructing a full-dimensional digital twinborn model containing multi-physical domain coupling, component-level degradation and power grid interaction; real-time operation, environment and historical data of the system are collected and preprocessed; a self-adaptive dynamic calibration algorithm is adopted to ensure that the model is consistent with a physical system in state; establishing a multi-dimensional evaluation index system covering network construction capability, operation economy and the like; carrying out multi-scene parallel simulation based on the calibration model; indexes are quantified through a bidirectional LSTM neural network, and trend pre-judgment is carried out; and a report containing an evaluation conclusion, risk early warning and optimization suggestion is generated, and simulation verification can be carried out on an optimization scheme. According to the method, evaluation comprehensiveness and accuracy are improved, and trend pre-judgment is supported.

The application provides a pipeline defect evaluation method and system based on multi-source data fusion, including the following steps: collecting data of the pipeline body operation state by using relevant acquisition devices, and marking the pipeline state and fault type, for constructing a training set and a test set. The pipeline static data is preprocessed, including data cleaning, standardization and feature extraction. The bird swarm algorithm is used to optimize the extreme learning machine, the static data is analyzed and the pipeline defect is evaluated. The image data of the pipeline body is preprocessed, including image conversion, enhancement and denoising. The VGG16 model is pre-trained based on the evaluation of the girth weld defects. The D-S evidence reasoning algorithm is used to fuse the defect evaluation conclusions obtained by the static data analysis and the girth weld image analysis at the decision level, and the final evaluation result is obtained. This method effectively improves the accuracy and reliability of the pipeline defect evaluation, and has wide application prospect and important practical value.

The invention discloses a hull structure damage assessment and repair scheme intelligent generation method. The method comprises the following steps: acquiring an image file of hull structure damage; matching the image file with the CAD model of the hull structure through an image recognition technology, and determining and identifying the damage range of the hull structure; the damage range is mapped to a ship body structure CAE model, structural units, loads, boundary conditions and grids in the damage range are automatically processed, the residual strength of the ship body structure is checked, and an evaluation conclusion is generated; using suggestions are generated in combination with the damage range and the evaluation conclusion to assist in decision making, and meanwhile a repairing scheme is generated in combination with a hull structure CAD model. According to the method, the image is matched with the ship structure related model, the damage range is quickly determined, the ship structure damage residual strength is quickly evaluated, quick technical support is provided for field decision making, and meanwhile, a ship structure damage repair scheme is quickly and intelligently generated, the repair period is shortened, and quick recovery and use are achieved.

The invention relates to the technical field of thermal power generating unit operation and evaluation, and provides a thermal power generating unit emergency standby state evaluation method comprising the following steps: step 1, constructing a multi-dimensional evaluation index system; 2, defining a key parameter and a standard limit value thereof for each evaluation dimension to form an evaluation index matrix; 3, distributing a basic weight for each evaluation dimension, and introducing a dynamic correction coefficient to carry out adaptive adjustment on the basic weight to obtain a dynamic weight of each evaluation dimension; 4, calculating a single score of each evaluation dimension, carrying out weighted fusion on the single score of each evaluation dimension and the dynamic weight of the single score, and calculating a comprehensive evaluation score of the unit; and step 5, outputting an evaluation conclusion and a targeted improvement suggestion. According to the scheme, a multi-dimensional evaluation system capable of covering metal parts, electricity, environmental protection, energy consumption, water consumption and peak regulation capacity is constructed, a dynamic weight distribution mechanism is introduced, and the accuracy and reliability of emergency standby capacity judgment are remarkably improved.

The application relates to the technical field of comprehensive evaluation of geriatrics, and discloses a scale missing item processing method and system for comprehensive evaluation of the elderly, which comprises the following steps: acquiring multi-source evaluation related data, aligning the multi-source evaluation related data to preset items of a comprehensive evaluation scale for the elderly, identifying missing items in the scale, dividing the missing items into multiple types, performing hierarchical completion according to the divided types, constructing a confidence level calculation model, and generating a confidence level of an evaluation conclusion; based on a preset key item set and an expansion condition, performing adaptive evaluation scheduling, preferentially completing key item evaluation, and only when the expansion condition is met, triggering non-key item evaluation or reevaluation suggestion; through the above method, automatic identification, hierarchical completion, confidence level labeling and adaptive scheduling of missing items in the comprehensive evaluation scale for the elderly are realized, the number of triggering times of unnecessary evaluation items is reduced, and the traceability of the missing processing result is improved.