47 results about "Evaluation Conclusion" patented technology

The invention provides an aviation support system efficiency evaluation method and system based on a combined weighting and cloud gravity center theory. The method comprises the following steps: determining an aviation guarantee efficiency evaluation object, and establishing an aviation guarantee efficiency evaluation index system; the method comprises the following steps: based on an aviation guarantee efficiency evaluation index system, acquiring data of quantitative indexes through simulation deduction, rating the qualitative indexes by experts, converting the qualitative indexes into quantitative data through an evaluation grade scale table, standardizing the index data, and calculating weights of the indexes by adopting a combined weighting method combining an analytic hierarchy process and an entropy weight method; the method comprises the following steps: determining digital characteristics of an index cloud based on aviation guarantee efficiency evaluation index data, calculating a comprehensive cloud gravity center, determining a cloud gravity center position in an ideal state based on standardized index data, and calculating a weighted deviation degree; and calculating an efficiency evaluation value of the aviation guarantee system based on the comprehensive weight and the weighted deviation degree of the aviation guarantee efficiency evaluation index, and forming a final evaluation conclusion.

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 present disclosure relates to the technical field of computer technologies and data processing. Disclosed are a data processing method, an electronic device, and a computer readable storage medium. The method comprises: performing data lineage analysis on the basis of identification information of a target machine and a target attribute of the target machine, and determining a plurality of data objects associated with the target attribute; performing feature extraction on the plurality of data objects to obtain a plurality of features, wherein the plurality of features are used for performing multi-source anomaly detection on the plurality of data objects; and performing data quality inspection on the plurality of data objects on the basis of the plurality of features to obtain a target inspection result, wherein the target inspection result is used for assisting in determining the evaluation conclusions of the target attribute across the plurality of data objects. The present disclosure solves the technical problems in the prior art of low accuracy of detection results and poor robustness of detection schemes resulting from detection based on single-source data.

The invention discloses a multi-agent-based examinee evaluation method and system and electronic equipment, and the method comprises the steps: designing different cue words, guiding a large language model to play a plurality of exclusive agent roles, including a cognitive evaluation agent, an emotion understanding agent and a mental health agent; the answer content of examinees is divided according to modules, and different parts are respectively input to different agents for processing: the cognitive evaluation agent receives logic and knowledge type questions, the emotion understanding agent receives open expression type questions, and the psychological health agent receives psychological test questions to respectively carry out analysis and evaluation; each agent outputs evaluation content in a structured mode, and the output content comprises an evaluation conclusion and a reason; a comprehensive scoring agent is introduced, and evaluation results of different agents are fused and analyzed. According to the invention, a multi-agent collaborative evaluation mechanism is utilized, and multi-view analysis of experts is simulated, so that the comprehensiveness, intelligence and personalized feedback capability of examinee evaluation are remarkably improved.

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.

The present application relates to the technical field of intelligent evaluation, and more particularly to a patent creative intelligent screening and evaluation method and system based on multi-agent cooperation, comprising: S1: receiving a patent creative text by a creative intelligent agent and performing semantic analysis, extracting a technical application scenario, an existing scheme pain point, a creative description and a core innovation point; S2: calling a semantic retrieval engine by a retrieval intelligent agent, performing parallel retrieval in multiple databases, and generating a retrieval result set; S3: performing evaluation reasoning by an evaluation intelligent agent from multiple dimensions of novelty, practicality, technical effect, scene difference degree and technical path coverage, and generating an evaluation conclusion; S4: feeding back the evaluation conclusion to a management system by a cooperative intelligent agent and pushing an evaluation notification; S5: monitoring new creations and triggering cyclic execution by the cooperative intelligent agent according to a preset scheduling rule. The present application realizes full-process automatic closed-loop processing, evaluation standardization and conclusion explainability, and significantly improves the patent creative screening efficiency and management cooperation.

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 multi-modal field adaptation safety evaluation method and system based on efficient parameter fine tuning, it is related to artificial intelligence safety evaluation technical field, comprising the following steps: S1.data to be identified is input into safety evaluation model;S2.safety evaluation model is to the data enhancement of input data to be identified using data and task characteristics, enhanced data is input into field feature encoder to obtain representation, representation is mapped to contrast learning space to capture core features;S3.data output by step S2 is processed by cross attention feature alignment module, after parameter fine tuning, enter GRPO reinforcement learning stage, generate safety evaluation conclusion;S4.output safe or dangerous safety evaluation conclusion, and output reasoning process.The application completely eliminates the risk of sensitive data leakage, improves the recall rate in multi-modal scene and greatly reduces the cost of computing power.

One or more embodiments of the invention provide a large language model evaluation method and device, equipment and a storage medium. The method comprises the steps of obtaining a sample containing a prompt corresponding to a target task, a first execution result obtained by executing the target task based on the prompt by a first large language model, and a second execution result obtained by executing the target task based on the prompt by a second large language model; obtaining an annotation evaluation conclusion annotated by the annotation party for the sample according to a good and bad comparison result of the first execution result and the second execution result; inputting the sample into at least one third big language model, so that each third big language model performs quality comparison on the first execution result and the second execution result based on the sample, and generating a model evaluation conclusion corresponding to the sample according to a quality comparison result; summarizing the annotation evaluation conclusion and the model evaluation conclusion into a total evaluation conclusion corresponding to the sample; wherein the evaluation conclusion is used for indicating a good and bad comparison result of the first execution result and the second execution result.

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 application discloses a kind of based on model paradigm feature matching's proxy model adaptive data quality evaluation method.The present application establishes the standardized technical rules of paradigm compatibility determination between proxy model and target model, fills the technical gap that proxy model selection lacks objective basis in third-party authentication scene;At the same time, proxy model selection is decided by team internally by default, and is converted into the paradigm compatibility determination rule system formed by four dimensions of output structure type, training target prototype, topological structure type and loss function type, so that any proxy model selection process for establishing the credibility of proxy model evaluation results when using proxy model to replace target model for data quality evaluation will not cause the technical rationality of evaluation conclusion to be unable to be verified externally due to the lack of paradigm compatibility determination rule system, so that the compliance and public credit of evaluation conclusion have technical basis support.

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 provides a fault-tolerant performance evaluation system and method for a satellite computing application accelerator, and the system comprises: a host computer configured to generate source data, fault injection parameters and a random number file; and a fault injection module configured on the satellite computing application accelerator to be evaluated, configured to inject faults into the source data according to the position indicated by the random number file and the RTL level fault type indicated by the fault injection parameters; and wherein the host computer generates a single event upset fault-tolerant performance evaluation report of the satellite computing application accelerator to be evaluated according to a pre-configured evaluation method based on the golden output, the application output, the fault injection parameters and the random number file. The application combines the fault injection module and the host computer into a general single event upset fault-tolerant performance evaluation system, and the fault injection module supports injection of multiple RTL level fault types, thereby improving the evaluation efficiency of the single event upset fault-tolerant performance of the satellite computing application accelerator and making the evaluation conclusion more reliable.

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 application discloses a power grid infrastructure project pre-evaluation automatic judgment method based on multi-dimensional data analysis, and comprises the following steps: constructing an index library of pre-evaluation technology, wherein the index library comprises the following modules: a planning library consistency evaluation module, a correlation equipment load rate condition evaluation module, a file integrity evaluation module, a general design consistency evaluation module, a similar project difference evaluation module and an economic rationality evaluation module; inputting the report file of a project to be evaluated into the index library, respectively calculating the construction necessity score, the technical feasibility score and the economic rationality score of the project to be evaluated, and finally summing the scores obtained, comparing the sum with a preset value, and giving a pre-evaluation conclusion according to the comparison result. The method can assist project evaluation experts in quickly judging the construction necessity of infrastructure projects, the feasibility of technical schemes and the economic rationality, and improves the efficiency of project pre-evaluation.