199 results about "Quality assessment" patented technology

The invention discloses a resting electroencephalogram quality evaluation method and system based on double-branch comparative learning, and the method comprises the steps: collecting an original EEG signal X, carrying out the data preprocessing and data enhancement, generating two different enhanced views, transmitting the two different enhanced views to a double-branch encoder in parallel, respectively extracting a time domain waveform and a time-frequency domain rhythm feature, and carrying out the deep fusion, the fused features X1 and X2 are sent to a projection head, and through a self-supervised contrast learning mechanism, the network weight is optimized by using contrast loss; a small amount of labeled fine-tuning data sets including X and corresponding labels y are adopted, after flowing through a pre-trained double-branch encoder, the fine-tuning data sets are directly sent to a classification head connected with the back of the double-branch encoder so as to output a prediction result of an input data segment, and a mixed loss function including classification loss and comparison loss is adopted for training in the optimization process. According to the invention, a complete online real-time quality control system is established, and end-to-end real-time closed loop from data acquisition to quality evaluation is realized.

The invention discloses a comprehensive quality evaluation method and system for Beidou deformation monitoring observation data in a complex environment, and relates to the technical field of satellite navigation positioning, and the method comprises the steps: carrying out the comprehensive evaluation of an elevating angle, a signal-to-noise ratio, an epoch difference and an environment model of the observation data of a Beidou single station, and determining the initial quality of the data; based on the double-difference observation values among the multiple stations, the data quality evaluation precision is further improved by combining a double-difference ambiguity resolving method; according to the method, ionosphere delay, three-difference errors and inter-epoch difference errors are used for data fusion, a more accurate quality evaluation result is obtained, and finally, the data are optimized through comprehensive analysis based on the quality evaluation results of various types of data so as to ensure accurate positioning and deformation monitoring. The technology aims at improving the accuracy and reliability of data quality evaluation by comprehensively analyzing influence factors of Beidou observation data in a complex environment, and provides support for geological disaster monitoring, smart city construction and other high-precision application scenes.

The embodiment of the invention discloses a test question output method and device based on multi-model collaboration, electronic equipment and a medium. A specific embodiment of the method comprises the steps of performing multi-source data fusion on a multi-source test question association data set, and then performing knowledge point association analysis to obtain a dynamic test question knowledge point graph; performing multi-hop graph reasoning query on the dynamic test question knowledge point graph to obtain a test question associated knowledge point set; performing model collaborative generation on the test question generation request information and the test question associated knowledge point set to generate an initial test question set; performing quality evaluation on the initial test question set to obtain a test question quality evaluation value set; dynamically adjusting the initial test question set to obtain an adjusted test question set and storing the adjusted test question set; and carrying out expansion adaptive test paper group rendering display on the adjusted test question set to obtain a test question test paper and printing the test question test paper. According to the embodiment, the test question quality can be improved, the test question generation efficiency is improved, the test question generation duration and display duration are shortened, the test question display quality is improved, and waste of display resources is reduced.

The invention discloses an interpretable multi-dimensional CI index dynamic scoring and risk assessment method, and belongs to the technical field of compliance risk assessment. According to the method, firstly, multi-source heterogeneous compliance data are collected, cleaned, subjected to caliber alignment and subjected to quality evaluation, and standardized data are obtained; a hierarchical index semantic system is constructed based on the standardized data, and a core index set is obtained through multi-dimensional screening; then, constructing a multi-dimensional dynamic threshold, calculating a weighted score in combination with the core index set, quantifying a score confidence interval through a Bayesian confidence quantification model, and mapping a risk level to form complete score data; and finally, carrying out interpretability analysis on the complete scoring data, and generating hierarchical interpretation texts and visual display contents. According to the method, systematization, precision and transparency of compliance risk assessment are realized, the suitability and credibility of a scoring result are improved, and reliable support is provided for compliance decision making.

The invention discloses a thermal power plant multi-source heterogeneous data quality evaluation method and system, and relates to the technical field of data evaluation, and the method comprises the steps: carrying out the comparison based on an alignment path length, a preset path length threshold value and a deviation threshold value, obtaining a consistent data flow and a conflict data flow, the method comprises the following steps: respectively calculating an accuracy index, an integrity index and a timeliness index for consistent data flow and equipment log data, obtaining a three-dimensional quality index matrix, weighting the three-dimensional quality index matrix through an entropy weight method, and calculating a comprehensive quality score of the multi-source heterogeneous data in combination with a real-time weight value of the multi-source heterogeneous data, and comparing the comprehensive quality score of the multi-source heterogeneous data with a preset score threshold to obtain a quality evaluation result, and directly generating a conflict alarm signal for a conflict data flow. According to the evaluation method based on the data indexes, not only is the sensitivity to data logic contradictions enhanced, but also context awareness and weight optimization in the evaluation process are realized.

The invention provides a geoscience specimen digital acquisition and management system, and relates to the technical field of geoscience specimen acquisition, and the system comprises a data acquisition module which collects specimen attribute fields, specimen photos and process metadata; the data quality evaluation module is used for performing bidirectional modulation on the basic weight based on the dependency relationship set of the fields to obtain an effective weight, and calculating an integrity score based on the effective weight; calculating an accuracy score, a specification degree score and a timeliness degree score; calculating to obtain a comprehensive quality score; the data optimization suggestion module is used for generating an optimization suggestion according to the score of each dimension and the score of the comprehensive quality; and the data storage management module is used for storing specimen attribute fields, specimen photos, process metadata, quality evaluation results and optimization suggestions. According to the method, the quality problem can be recognized in time, targeted optimization suggestions can be automatically generated, and intelligent priority recommendation is provided for field acquisition based on historical quality statistical data, so that the quality and efficiency of geoscience specimen data acquisition are improved.

The invention relates to the technical field of giant salamander by-product quality evaluation, and discloses a giant salamander by-product source functional factor quality evaluation system and method, and the method comprises the steps: obtaining a giant salamander by-product sample, carrying out the pretreatment, and extracting a functional factor to obtain a to-be-detected sample; identifying and quantitatively determining specific markers based on biochemical analysis and molecular detection means to generate a marker data set; inputting the marker data into a multi-modal feature fusion module, extracting activity and stability features, and performing time sequence dynamic modeling; activity and stability independent evaluation branches are set, and corresponding score values are generated; dividing functional domains according to functional factor categories, constructing an evaluation curve based on historical data, substituting a target sample into each functional domain and an adjacent functional domain to generate functional domain evaluation results, and performing weighted fusion; and finally, carrying out weighted integration on the activity score, the stability score and the comprehensive evaluation value, and determining a final quality evaluation result.

The invention provides a test data quality evaluation method and system based on machine learning, relates to the technical field of data processing, and solves the technical problems of low data quality evaluation efficiency, non-systematic evaluation dimension and lagging abnormal response of vehicle test data in the prior art. The method comprises the following steps: acquiring test data of test equipment through a data acquisition interface; the test data comprises real-time test data and historical test data; performing quality evaluation on the test data based on a preset evaluation dimension by using the trained data quality evaluation model; the evaluation dimensions at least comprise data integrity, data accuracy, data consistency and data validity; and generating a data quality evaluation report according to an evaluation result, and performing exception marking and early warning pushing on the data which is evaluated to be unqualified.

The invention provides a quality control system based on a multivariate exponential weighted moving average control chart, and belongs to the technical field of quality management and statistical process control. The system comprises a quality data acquisition module, a data preprocessing module, a statistical process control module, an anomaly judgment module and a quality evaluation and improvement module, and is used for monitoring and controlling the production process of a product with a plurality of quality characteristics in real time. The method comprises the following steps of: acquiring quality data of a plurality of quality characteristics in a production process by a system, preprocessing the quality data, and constructing a multivariate quality characteristic data set; estimating a mean vector and a covariance matrix in a controlled state based on the historical quality data, performing weighted updating on the multivariate quality data by adopting a multivariate exponential weighted moving average method, calculating a corresponding MEWMA statistic and generating an MEWMA control chart; and comparing the MEWMA statistical magnitude with a preset control limit to judge whether the production process is in an out-of-control state or not, and outputting early warning information and a corresponding quality evaluation result and improvement suggestion when abnormality is detected. The method can comprehensively analyze related information among multivariate quality characteristics, improves the sensitivity and accuracy of anomaly detection in the production process, and effectively improves the quality control level of the production process in the manufacturing industry.

The invention discloses a drilling quality and stratum characteristic inversion method and device based on dynamics and rock debris morphology and a medium, and relates to the field of artificial intelligence, and the method comprises the steps: synchronously collecting a drilling dynamics signal and a rock debris image sequence in a drilling process; extracting a drilling dynamics high-frequency characteristic spectrum based on the drilling dynamics signal; extracting a rock debris morphological parameter sequence based on the rock debris image sequence; inputting the drilling dynamics high-frequency characteristic spectrum and the rock debris morphological parameter sequence which are aligned in time into a pre-trained multi-task deep learning model; and a hole wall quality evaluation result and a stratum characteristic identification result synchronously generated by the multi-task deep learning model are obtained, the hole wall quality evaluation result is used for representing the hole wall state, and the stratum characteristic identification result is used for representing the stratum attribute. According to the method, the drilling quality and the stratum characteristics can be inversed in real time based on deep fusion and synchronization of the multi-source while-drilling information.

The invention discloses a high-reliability packaging quality assessment method and system based on multi-source process data, and relates to the technical field of quality assessment, and the method achieves the online sensing of the orientation consistency of casting ceramic particles through the nondestructive scanning of an unsintered green tape by adopting a terahertz spectrum. Material waste and procedure interruption caused by destructive detection are avoided; secondly, the cutting edge morphology is synchronously collected in the scribing process, geometric feature quantification is carried out, the recognition of the edge breakage state is improved from subjective qualitative judgment to objective quantitative characterization, and the detection consistency and precision are remarkably improved; the frequency spectrum coding data reflecting the internal quality and the shape coding data reflecting the external defects are associated point by point, and a two-dimensional mapping relation of the internal process parameters and the external processing results is constructed, so that whether the cutting edge breakage is derived from potential abnormity in a tape casting stage or not can be accurately traced; and a data closed loop is provided for backtracking optimization of process parameters.

The invention discloses a data quality comprehensive evaluation method and system, and the method comprises the steps: analyzing power collection data, and determining key data indexes related to a preset quality evaluation dimension; determining an index value of the key data index, and determining a sub-quality evaluation value of each preset quality evaluation dimension based on the index value; determining the weight of each preset quality evaluation dimension, and comprehensively determining an initial quality comprehensive evaluation value of the power collection data based on the sub-quality evaluation values and the weights; determining a prediction value coefficient of the power acquisition data, and correcting the initial quality comprehensive evaluation value to obtain a quality comprehensive evaluation value; and determining a quality grade of the power acquisition data based on the quality comprehensive evaluation value, and determining a rectification driving strategy of the power acquisition data. The power acquisition data can be comprehensively evaluated and quantitatively analyzed, the data quality condition is deeply known, the prediction value coefficient is introduced to determine the data application value, and a more reliable reference basis is provided for data decision making.

This invention provides a method and system for online monitoring and quality assessment of additive manufacturing processes based on multi-sensor fusion, relating to the technical field of additive manufacturing. The method includes: constructing a multimodal spatiotemporal dataset; extracting features from each physical modality data to obtain an independent feature representation set; inputting the independent feature representation set into a preset dynamic cross-modal attention network to calculate the mutual information correlation matrix and perform feature weighted reconstruction to obtain a deep fusion feature vector; inputting the deep fusion feature vector into a preset quality mapping model to output quality assessment index values ​​representing defect probability and defect classification; constructing a three-dimensional quality digital twin model; and outputting online monitoring and early warning commands and global quality assessment results based on the three-dimensional quality digital twin model. This invention completely eliminates the problem of spatiotemporal registration misalignment of multimodal data, achieving high-precision deep feature fusion and voxel-level panoramic three-dimensional quality space mapping and tracing.

The invention relates to the technical field of bidding information processing and document auditing, and discloses an intelligent bidding document quality evaluation and auxiliary generation system based on multi-dimensional detection, and the system comprises an information extraction module which is used for analyzing a bidding document file and extracting various nodes; the knowledge graph construction module is used for receiving the nodes and constructing a response evidence chain type knowledge graph; the risk assessment module is used for identifying a response path in the knowledge graph, judging a path mode and calculating a quantitative risk score; and the report generation module and the auxiliary generation module are used for generating a bidding document quality evaluation report containing specific modification suggestions according to the risk evaluation result. According to the method, a traditional manual auditing process is automated, multi-dimensional detection means such as text analysis, image detection and external credit query are comprehensively applied, quantitative evaluation of bidding response risks, accurate positioning of high-risk items and automatic generation of modification suggestions are achieved, and therefore the efficiency, depth and accuracy of bidding document quality auditing are improved.

This invention discloses a multi-scale time-frequency analysis method and system for ocean current meter signal processing, relating to the field of marine information processing. The method includes: S1: acquiring raw data and performing physical benchmark correction to obtain three-dimensional current velocity data and basic mass labels; S2: extracting first and second features from the three-dimensional current velocity data through spatiotemporal decoupling; S3: fusing and separating the three-dimensional current velocity data based on the first and second features to obtain an optimized set of flow field parameters, and generating the uncertainty of each parameter by combining the basic mass labels and error source information; S4: performing comprehensive quality assessment and grading based on the first and second features and the uncertainty, outputting a data product with mass labels and uncertainty information. By extracting spatiotemporal dual-dimensional features and using them to guide signal fusion and quality assessment, highly robust processing of ocean current data and end-to-end quality traceability are achieved, significantly improving the reliability of the data product.

The present application relates to the field of transportation, and particularly relates to a catenary detection data synchronization method and device. The method comprises installing a mileage detection device and a catenary detection device on a vehicle according to a preset spatial position, wherein the catenary detection device comprises a contact detection device and a non-contact detection device, the method comprises: acquiring mileage data and catenary data according to a data sampling interval, the catenary data comprises contact data acquired by the contact detection device and non-contact data acquired by the non-contact detection device; and storing the mileage data and the catenary data into data synchronization queues corresponding to the preset spatial position, respectively, to realize catenary data synchronization. The present application can quickly collect dynamic action parameters and geometric parameters of the pantograph-catenary system, and dynamically match the collected data in real time, which has guiding significance for safety warning, dynamic review, comprehensive quality assessment and fault diagnosis of the measured line.

This invention provides a method for processing real-device data of an intelligent agent and related equipment. The method includes: acquiring a target action acquisition task and a target action type; in response to the target action type being a long-term task, acquiring key continuous operation segment data in the long-term task; acquiring failure scenario data and corresponding recovery operation data during the execution of the target action acquisition task; identifying and deleting invalid static frames from the key continuous operation segment data and recovery operation data using a pre-trained invalid frame recognition model to obtain cleaned data; evaluating the quality of the cleaned data based on a preset data quality assessment model to obtain a quality assessment score; and training the target intelligent agent using the cleaned data in response to the quality assessment score meeting a preset quality score threshold. This application can effectively reduce the redundancy of real-device data for intelligent agents and enrich data diversity, thereby improving the generalization ability of visual language action models.

The invention relates to a historical case similarity-based quality evaluation method, which belongs to the field of software development, and comprises the following steps of: screening out a prior index and a posterior index; collecting data of each stage in the research and development process of each software historical version as historical cases; the specific numerical value of each index is counted; obtaining quality problem source data of the historical cases; taking the historical cases with the labels as input samples, performing model training by adopting an SVM classification algorithm, and constructing a quality problem classification model; constructing a prior index vector matrix for each historical case and the newly-added case, and calculating the similarity between the newly-added case and each historical case; automatically classifying the newly added cases and setting labels; calculating a risk value of each similar case; and averaging the risk values of all the similar cases to obtain a risk coefficient value of the newly added case. According to the method, the influence of manual subjective deviation on index interpretation can be avoided, and an objective quality evaluation standard is established through quantitative similarity analysis.

This invention belongs to the field of construction surveying technology and discloses a laser measurement and control system for tower-type vertical kiln masonry based on cross-shaped control lines. By acquiring temperature field distribution and dust concentration data along the laser propagation path, a dual compensation mechanism for refraction offset and scattering attenuation is constructed to achieve accurate measurement of the center point coordinates and high-precision establishment of four-directional foundation measurement and control line groups. This invention employs a blind zone intelligent identification mechanism, using the extreme values ​​of deformation modes corresponding to each harmonic component as the basis for blind zone determination. Through adaptive supplementation of measurement and control lines and minimum area circle fitting calculations, it accurately evaluates and generates masonry correction commands. This invention also establishes a layered vertical sequence of circle center coordinates, combines circumferential statistical methods to identify systematic trend offsets, triggers a benchmark re-measurement process, and effectively controls cumulative errors. This invention is comprehensive and accurate, enabling full-link measurement and control and quality assessment of the vertical kiln masonry process, effectively ensuring that the verticality and roundness of the kiln body meet design requirements.

The present invention provides a novel integrated platform that enables the verification of manufacturing processes for complex natural products, as well as the standardization and evaluation of complex natural products. This platform allows for the "system-wide" characterization of complex mixtures contained in therapeutic and / or health-promoting products based on or composed of natural substances, without limiting the approach to reductionist chemical approaches targeting single markers. This is achieved through the integration of qualitative and quantitative fingerprint characterization data metabolomics and physical and biophysical activity data for the mixtures, thereby providing a product characterization that forms the basis for verifying manufacturing processes that can ensure the reproducibility of efficacy and safety across different batches of the product. The analytical platform described herein enables the assurance of a complex natural product as a whole, by considering all the characteristics of its quality and efficacy, through a multidimensional analytical approach, in order to ensure reproducibility regarding efficacy and safety.

The invention discloses a data quality evaluation method, device and equipment, and relates to the technical field of data processing, and a preset rule generation model can generate different data quality rules according to the difference of input data tables, so that adaptation to different business scenes is realized. A preset rule generation model is constructed based on a Reflection Agent framework, the preset rule generation model is iteratively optimized based on an reflection signal, so that a better data quality rule is generated, the data quality rule is dynamically generated based on structural metadata of different data tables, an index calculation task is dynamically generated according to the target data quality rule, and the index calculation task is executed. The index calculation result of the to-be-evaluated data table is obtained in a targeted manner; and according to a target data quality rule, evaluating the index calculation result to obtain a more accurate data quality evaluation result of the to-be-evaluated data table. Therefore, the data quality evaluation accuracy is improved.

This invention relates to a six-DOF microgravity quality assessment method based on mission phase-driven approaches, belonging to the field of spacecraft control and ground simulation. It constructs a multi-physics fusion six-DOF microgravity quality index, divides the on-orbit service mission phase using a finite state machine and matches dynamic weights and assessment thresholds, combines digital twins to achieve virtual-real consistency verification, employs rapid independent component analysis and extended state observers to complete disturbance decoupling and contribution rate quantification, and uses a graph neural network long short-term memory network model and Shapley sum interpretation algorithm to achieve pre-experiment performance prediction and in-experiment anomaly diagnosis. This invention addresses the technical problems of existing microgravity ground simulation assessments, such as the disconnect between static thresholds and on-orbit mission requirements, the inability of a single index to cover multi-DOF coupling characteristics, the lack of virtual-real consistency verification, the inability to quantify and attribute disturbance sources, over-reliance on expert experience, and post-event offline assessment.

The invention provides a continuous integration test balance method and device based on dynamic quality evaluation and a medium, and belongs to the technical field of vehicles. The method includes monitoring a code submission event, capturing changes in the code submission event, forming change set data, and determining a quality assessment indicator from a plurality of data sources. And determining a comprehensive risk coefficient of current code integration by utilizing the set risk assessment model according to the quality assessment index and the change set data. And dynamically deciding a corresponding test execution strategy according to the comprehensive risk coefficient, driving a corresponding test suite according to the corresponding test execution strategy, and distributing a corresponding test task to obtain a test execution result. The efficiency of continuous integration testing is improved, and testing resources are optimized.

The present application relates to the technical field of data quality evaluation, in particular to a kind of automatic quality evaluation method of data set, comprising the following steps: extracting semantic fragment and vector information, clustering analysis covering structure, screening redundant sample, marking text difference, output consistency evaluation result.In the present application, the structured extraction and vector distribution measurement of semantic information enable high-precision identification of semantic coverage and structure density, vector overlap frequency and similarity threshold linkage enhance the discriminability of redundant detection, text position difference and semantic span joint labeling mechanism improves the systematicness of text consistency labeling, the synchronous comparison mechanism between text screening content and text difference labeling result ensures that the intersection sample of redundancy and deviation can be accurately identified, the dense clustering of cultural semantic content in vector space and the quantitative analysis ability of structure deviation point are synchronously improved, and the comprehensive dimension and logical closure of evaluation result are realized optimization.

The invention provides a method for constructing a data quality evaluation model, and belongs to the technical field of quality evaluation, and the method comprises the steps: collecting a to-be-evaluated data set, and obtaining a business demand and an application scene of the to-be-evaluated data set; determining an assessment index vector of the to-be-assessed data set based on the business demand and the application scene of the to-be-assessed data set; based on the to-be-evaluated data set and the evaluation index vector, determining a training quality feature matrix and a test quality feature matrix; and constructing a quality evaluation model based on the training set and the training quality feature matrix, and evaluating and optimizing quality evaluation performance based on the evaluation index vector, the test set and the test quality feature matrix. The method can enhance the capturing capability of the model for the data quality characteristics, achieves the high-precision evaluation of dynamic weighting, improves the intelligence and stability of the quality evaluation model, and improves the adaptability of the model in the quality management and analysis scenes of complex data.

The invention discloses a multi-modal medical data conjoint analysis system based on artificial intelligence, and particularly relates to the field of data analysis. Comprising a data acquisition module, a data quality evaluation module, a data dynamic association module, a heterogeneity feature fusion module, a model real-time optimization module, a data feedback iteration module and a data abnormal feature detection and analysis module. The data acquisition module is used for acquiring multi-modal medical data and executing modal specificity preprocessing on the multi-modal medical data; the data exception feature detection and analysis module is used for constructing an exception association model based on the optimized model and generating an exception association analysis report; according to the method, the problems that in the prior art, the data quality sensitivity is high, and the stability of an analysis result is poor due to the fact that the method is easily influenced by image artifacts, signal noise and text errors are solved by combining modal specificity preprocessing and quality self-adaptive correction with the quality index and the targeted correction formula.

The present application relates to the technical field of machine learning, and particularly relates to a preclinical drug experiment quality evaluation method based on machine learning, comprising: obtaining a plurality of experiment samples, each experiment sample comprising a dose level and an observation value of at least one biological index; determining a trend residual of each biological index of each experiment sample; determining a biological index evaluation weight sequence composed of quality evaluation weights of all biological indexes; determining a composite distance between any two experiment samples; determining a quality evaluation index of each experiment sample; and evaluating the quality of preclinical drug experiments based on the quality evaluation index of each experiment sample. By constructing a dose effect trend function, combining the trend residual and the weight, establishing a composite distance model fusing pharmacological trends, realizing adaptive selection of neighborhood parameters, improving the accuracy and stability of anomaly detection, overcoming the limitations of traditional machine learning LOF algorithm, and enhancing the evaluation reliability.

This application proposes an intelligent question-answering analysis method and system for electronic evidence data based on a large model. The large model inputs user requests for intent recognition. Based on the intent recognition results, the large model invokes a case knowledge base, a big data analysis function invocation module, and a Text2SQL module for intelligent question-answering analysis. The Text2SQL module semantically supplements and understands the user's colloquial query requests, correcting the intent recognition results. The big data analysis function invocation module routes user requests to the corresponding function categories based on the content characteristics of the user requests and executes the invocation. The case knowledge base optimizes and sorts the search results and summarizes the results using the Retrieval Enhancement Generation (RAG) technique. The evaluation agent in the dynamic fence module performs quality assessment on the question-answering analysis results and returns a satisfactory result. This application significantly improves the accuracy of electronic evidence collection systems in locating relevant case data and reduces the complexity of electronic evidence collection work.

A method for the quality-assessed metering by means of a pipetting device, the method including the following steps:—carrying out a metering process by moving a pipetting plunger, —recording the time curve of the working-gas pressure in a pipetting channel during the metering process, —comparing the curve of the working-gas pressure on the basis of the recorded time curve of the working-gas pressure with a predetermined target pressure value range, and—outputting a quality assessment of the metering process on the basis of the result of the comparison step; in order to improve the quality assessment, the control device carries out the following further steps while the pipetting channel is filled exclusively with working gas as fluid:—carrying out corrective-pressure determination by moving the pipetting plunger, and—measuring a pressure of the working gas in the pipetting channel as a corrective pressure during the corrective-pressure determination, wherein, prior to the comparison step, the control device corrects the time curve of the working-gas pressure recorded during the metering process on the basis of the corrective pressure measured during the, so that the comparison step is carried out using the corrected time curve of the working-gas pressure.

The application discloses a knowledge base and multi-agent collaborative academic review automatic generation method, system, device and storage medium, relates to the field of artificial intelligence and natural language processing technology, and comprises the following steps: adopting a mixed extraction mechanism to extract an agent to original unstructured literature, and constructing a bottom-layer JSON knowledge base containing technical indexes and quantitative data; mapping the knowledge base information to a preset target hierarchical structure through a planning agent, and generating a global review outline; adopting a block retrieval enhancement strategy, a writing agent carries out chapter text synthesis and graph generation under the constraint of forced binding quantitative evidence; comparing the source text and the generated text through entity lexical analysis, and performing quality evaluation.The application adopts the above-mentioned knowledge base and multi-agent collaborative academic review automatic generation method, system, device and storage medium, effectively improves the local fact accuracy and global logical coherence of the generated literature, and realizes automatic and high-fidelity compilation of the academic review.