2712 results about "Quality data" patented technology

The invention relates to the technical field of twinborn model simulation, and discloses a twinborn model simulation method and system for hot working of large forgings. The method comprises the following steps: collecting and preprocessing process parameters, quality data and environment information in a multi-source manner, and obtaining hot working characteristic data; performing correlation analysis to construct a process knowledge graph; calculating the distribution of a temperature field, a stress field and an organization field by using a self-sensing multi-field coupling var value neural network; comparing and analyzing to obtain deviation data and correction parameters; adjusting a network parameter optimization prediction result; and executing multi-objective optimization calculation, and generating a whole-process technological parameter and a control instruction. According to the method, full-process multi-physics field coupling calculation from smelting, casting, forging and pressing to heat treatment can be achieved, model parameters are dynamically adjusted according to real-time production data, the optimal process scheme is generated, and therefore the manufacturing quality and efficiency of large forgings are improved.

The invention discloses a labeling task assignment method and device based on artificial intelligence, and the method comprises the steps: obtaining historical behavior data, and constructing a multi-dimensional user portrait; receiving a task description document, a data sample and a quality requirement document to obtain a multi-dimensional task feature vector; based on the multi-dimensional user portraits and the multi-dimensional task feature vectors, a matching degree score is calculated through a multi-objective optimization algorithm, and an optimal task allocation scheme is generated; optimizing the task structure through a fireworks algorithm based on student t distribution, and generating an optimized task unit structure; real-time monitoring is carried out through the anomaly detection model and the quality prediction model, and quality control measures are triggered; model parameters are updated through a reinforcement learning algorithm, and a personalized feedback and capability improvement strategy is generated. According to the method, accurate matching between the annotators and the tasks is realized, the processing efficiency of complex tasks is improved, the annotation quality is improved, the expansibility and the response speed of a platform are enhanced, and an effective solution is provided for large-scale and high-quality data annotation.

The invention relates to the technical field of hot working control, and discloses a hot working management and control method, system and device based on digital twinning and a storage medium. The method comprises the steps of obtaining technological parameters and quality data of smelting, casting, forging and pressing and heat treatment procedures through a multi-sensor fusion internet-of-things data acquisition system, constructing a hot working full-process data set and a multi-dimensional technological data platform, achieving parameter mapping and grid conversion through a self-adaptive interface algorithm, establishing a collaborative simulation platform, and obtaining a multi-dimensional technological data platform. And constructing a digital twinborn model and deploying the digital twinborn model to an edge computing gateway to generate a visual management and control system. And calculating a process parameter combination and a resource priority allocation scheme. According to the invention, on the basis of obtaining a large amount of process data in real time, cross-process collaborative optimization and intelligent decision can be realized, especially under the conditions of multi-physics field coupling and complex time-varying characteristics, how to effectively monitor and adjust process parameters in real time and ensure the optimization of product quality and production efficiency.

The invention provides an aluminum alloy auxiliary frame welding accurate positioning and quality control method and system. The method comprises the steps that S4, welding gun track control parameters are updated through a dynamic adjustment instruction, laser tracking data are fused to optimize the welding path angle in real time, and the accurate movement track of each section of welding seam is determined; s5, acquiring real-time thermal expansion data during welding execution, monitoring temperature field distribution of a welding seam area by adopting an optical measurement technology, and judging whether thermal deformation adjustment exceeds a preset threshold value or not; s7, micron-sized welding spot positioning operation is executed according to the stable positioning state, the dynamic path optimization result is fused to generate a final welding instruction, and consistent welding seam quality data is output; and S8, after weld joint quality data are obtained, weld joint deviation and geometric consistency are detected through a laser tracking technology, whether welding quality meets design requirements or not is judged, and a quality verification report is generated. According to the method, the precision and consistency of intelligent welding of the aluminum alloy auxiliary frame are remarkably improved.

The invention relates to the technical field of remote control, and discloses a remote control method and system based on gold wire bonding equipment, and the method comprises the steps: obtaining a high-precision multi-dimensional detection data set; processing the multi-modal original monitoring data to obtain a gold thread quality feature vector matrix; processing the multi-dimensional feature vector set to obtain unified quality index feature mapping; processing the uniform quality index feature mapping to obtain a multi-stage defect detection report; processing the historical quality data and the process parameters to obtain a parameter optimization suggestion report; processing the multi-source monitoring data to obtain an augmented reality interaction system; and the augmented reality interaction system receives an operation decision of a remote operator and remotely controls the gold wire processing equipment. By means of the remote control method and the high-precision remote control system based on the gold wire bonding equipment, millisecond-level response control over gold wire machining equipment is achieved, the instruction execution accuracy is improved, and operation delay is reduced; and potential quality problems are warned in advance.

The invention relates to the technical field of intelligent manufacturing, discloses a papermaking equipment fault tracing method and system based on a process knowledge graph, and discloses the papermaking equipment fault tracing method and system based on the process knowledge graph. The method and the system comprise data acquisition and preprocessing, papermaking process knowledge graph construction, fault event detection and matching, fault tracing and propagation path reasoning, and maintenance scheme recommendation and optimization. The method overcomes the limitation that the traditional method is difficult to capture the cross-equipment, cross-process and cross-time sequence deep causal association and fault propagation path of the papermaking equipment. By constructing a comprehensive papermaking process knowledge graph, equipment operation state data, process parameter data, production quality data, equipment structure principle, process flow knowledge, fault mode knowledge, maintenance experience and other heterogeneous knowledge are subjected to deep fusion and semantic association, so that the system can exceed the correlation of the data surface; and an internal mechanism and a propagation chain of the fault are deeply excavated.

The present invention discloses a digital management method and system for a water conservancy project construction process, and belongs to the technical field of water conservancy projects. The method specifically comprises: real-time collection of relevant data from a water conservancy project construction site, and preprocessing, by comparing the actual construction progress with the planned progress, calculating the construction progress deviation, and analyzing the future construction progress trend, analyzing the construction quality data, training a prediction model through historical data, identifying construction quality hazards, combining the construction progress and quality, resource conditions and external environmental factors, intelligently scheduling construction resources, building a risk prediction model, and evaluating the risks in the construction process; in the context of complex water conservancy projects, it is possible to achieve process progress and quality management, reasonable resource scheduling and risk control, and better ensure the smooth progress of the project.

The invention relates to the technical field of circuit detection, discloses a power transmission line thermochromic wire clamp heating early warning method and system, effectively solves the problem of data acquisition distortion in a strong electromagnetic environment, and improves the accuracy of state evaluation through multi-source data fusion. The dynamically adjusted early warning model reduces the risk of false alarm and missing alarm caused by equipment aging, the intelligent decision support module shortens the fault handling response time, the data closed-loop mechanism ensures the reliability of the system in the whole life cycle, and the reliability of the system in the whole life cycle is improved through multi-sensor cooperative monitoring and edge calculation processing. And the influence of environmental factors on data acquisition is reduced. The two-channel transmission architecture guarantees the data transmission integrity under different network conditions, the CRC verification mechanism effectively recognizes and corrects transmission errors, a high-quality data basis is provided for a subsequent early warning model, the abnormal data recollection mechanism avoids data missing caused by single collection failure, and continuous and stable operation of the monitoring system is ensured.

The invention discloses an industrial machine vision recognition and analysis system, relates to the technical field of machine vision recognition, and is used to solve the problem of poor industrial machine vision recognition. The invention combines total variation denoising, contrast-limited adaptive histogram equalization, random geometric transformation and other technologies to enhance image diversity, improve the system's generalization ability for images in different environments, extract image texture and edge features, provide high-quality data for subsequent analysis, use unsupervised learning to extract image potential features and patterns, and locate abnormal areas through template matching, so as to adaptively discover defects in the production process, dynamically update the model based on a small amount of labeled data, optimize feature representation in real time, and adapt to environmental changes. Finally, semi-supervised learning and pseudo-label generation technology are used to expand the training set, improve the generalization ability of the model, maintain efficient learning when labeled data is limited, reduce manual labeling costs, and improve the accuracy of automated quality inspection.

The invention relates to the technical field of offshore area monitoring, in particular to an offshore area pollutant diffusion real-time tracking and traceability monitoring system. The method comprises the following steps: a data perception and fusion unit collects and preprocesses data, carries out space-time calibration and multi-source fusion, and outputs a standardized fusion space-time sequence; the pollutant diffusion simulation and prediction unit calculates a flow velocity field, simulates a diffusion process and predicts a trend; the pollutant traceability positioning unit tracks a pollution path and inverts pollution source parameters; the emergency decision support unit determines a risk level and generates an emergency plan. And through a space-time alignment algorithm and a multi-source fusion mechanism, the data consistency and reliability are improved, a high-quality data basis is provided for subsequent processing, and the monitoring accuracy is guaranteed.

The invention relates to the technical field of bridge construction, solves the technical problem that progress or quality data cannot be updated in real time due to difficulty in direct synchronous data feedback between a construction site and a management system, and particularly relates to a BIM-based intelligent two-dimensional code bridge construction quality and progress management and control system. The geometric information, the construction progress and the quality standard of the bridge structure are recorded in detail, and a unique two-dimensional code is generated for each construction node. A constructor obtains real-time node information by scanning the two-dimensional code, updates the construction progress and uploads quality detection data, and analyzes and feeds back the construction data in real time, so that efficient management and control of the construction progress and quality are ensured. The system overcomes the defects in the prior art, has the characteristics of timely information updating, accurate data, automatic management, high traceability and the like, and can remarkably improve the management efficiency and the quality control level of bridge construction.

The invention discloses a process parameter tracing and quality collaborative management system for ceramic production, and relates to the technical field of ceramic production, the management system comprises a step of obtaining a plurality of process data and quality data in a ceramic production process, and each group of process data comprises a raw material ratio, a forming pressure, a firing temperature and a firing time. According to the process parameter tracing and quality collaborative management system for ceramic production, a specific process parameter deviation link can be quickly positioned by reversely tracing to a raw material source from a finished product quality problem and combining batch association identifiers and data records of all links; the quality problem solving efficiency is improved, and batch loss caused by traceability lag is avoided; besides, a process quality association relationship constructed by the module is established, a quality detection result is closely associated with process parameters, and multi-dimensional analysis is performed, so that a quality problem can be fed back to process parameter adjustment in time, and targeted measures can be taken according to an analysis result.

The invention discloses a self-adaptive photovoltaic tracking control method and system, and the method comprises the steps: collecting and fusing multi-source data, detecting the operation environment of a photovoltaic system in real time, and carrying out the real-time processing of the collected environment data; constructing a self-adaptive photovoltaic tracking control model, training the self-adaptive photovoltaic tracking control model, and dynamically adjusting a tracking strategy of the photovoltaic panel according to historical data and real-time environment change; a dynamic error compensation mechanism is introduced, errors of a tracking system are corrected in real time, a multi-objective optimization strategy is designed, and tracking modes are automatically switched according to different environmental conditions. The system comprises a multi-source data acquisition module, a photovoltaic tracking control module and a tracking mode switching module. According to the invention, the precision and efficiency of operation monitoring of the photovoltaic system are improved; high-quality data input is provided for a photovoltaic tracking control model, and the decision-making ability of the model is improved; the intelligent and automatic photovoltaic panel tracking strategy is realized, and the power generation efficiency of a photovoltaic system is improved.

The invention relates to the technical field of energy storage battery health state monitoring and dynamic management and control, and discloses an energy storage battery dynamic management and control method and system.The energy storage battery dynamic management and control method comprises the steps that energy storage battery state data and environment interference data are collected through a multi-modal sensor network, and a multi-modal feature set is obtained; processing the preprocessed multi-modal feature set by using a multi-layer time sequence convolutional network, extracting time sequence features of the health state of the battery, and meanwhile, realizing abnormal noise detection and suppression; establishing a mapping relation between environmental interference and prediction reliability, representing a prediction result as a confidence interval, and calculating a reliability index; dynamically adjusting parameters and a structure of the prediction model, and generating a health state prediction result and reliability evaluation thereof; generating an energy storage battery dynamic management and control strategy with safety margin; through a multi-mode signal acquisition and preprocessing technology, the battery state and environment interference information are effectively captured, and a high-quality data basis is provided for health state prediction.

The invention relates to the technical field of deformation early warning of a coal mine TBM tunneling roadway, in particular to a simulation and monitoring method for intelligent early warning of large deformation of the coal mine TBM tunneling roadway. According to the technical scheme, the method comprises the steps of data acquisition and preprocessing, feature mining and fusion, intelligent early warning model construction and real-time monitoring and early warning, and the data acquisition and preprocessing specifically comprises the steps that multiple types of sensors are installed on a coal mine TBM tunneling roadway site to acquire multiple data, three-dimensional reconstruction is conducted on image data, and timestamps and space coordinates of the data are unified; a multi-dimensional spatio-temporal data set is formed by using wavelet transform denoising, and feature mining and fusion comprises the steps of extracting geologic structure image data features by using 3D-CNN and capturing spatio-temporal change features of other data by using LSTM. According to the method, high-quality data is provided, meanwhile, the model is more suitable for complex working conditions and more accurate, the generalization ability of the model is enhanced, real-time monitoring and early warning are timely, effective and visual, decision making is facilitated, and safety accidents and economic losses can be effectively reduced.

The invention relates to a prefabricated building quality defect tracing system and method applied to the field of building informatization, and the system comprises a user interaction layer, a data processing and storage layer, a data collection layer and a function layer, and the data collection layer is used for collecting key quality data of prefabricated building components in different links; the user interaction layers interact with each other through a network and the function module layer; the data processing and storage layer performs encryption, hash operation, scattered storage and index verification on the quality data transmitted by the data acquisition layer by using an intelligent contract, an IPFS network and a block chain; the function module layer provides support for each unit of the user interaction layer to trace quality defects, judge responsibility and take prevention measures, and the system can collect the quality data of the whole process of the prefabricated building, comprehensively manage the quality tracing process, strengthen multi-party collaboration and sharing, and improve the quality of the prefabricated building. And it is ensured that tracing of the quality defects of the fabricated building has comprehensiveness, transparency and non-tampering performance.

The invention discloses a tunnel full-period construction feature information fusion and quality tracing method and system, and the method comprises the steps: converting the construction data of a tunnel full period into construction feature information through constructing a digital twinborn model of tunnel construction, and carrying out the dynamic weighting according to the importance of different construction feature information, performing fusion to generate high-dimensional construction feature data; representing the high-dimensional construction feature data as a graph structure; analyzing the graph structure by using a graph neural network algorithm GNN to obtain an anomaly detection result of the construction equipment; and constructing the construction data into a knowledge graph, tracing quality data in the construction process by using a knowledge graph reasoning technology, and analyzing potential quality risks in combination with the anomaly detection result, so as to realize risk early warning and full life cycle quality management of the construction process. According to the embodiment of the invention, more comprehensive and accurate data support can be provided for construction management, and the safety of the construction process and the efficiency of quality management are improved.

The invention discloses a wireless network high-reliability transmission method based on double-link redundancy, which relates to the technical field of wireless communication, and comprises the following steps: establishing double communication links, and collecting link quality data of the double communication links; performing weighting operation on the link quality data according to a preset weighting coefficient to obtain a first link score and a second link score, and judging the first link score and the second link score; issuing an original data packet and a backup data packet of the original data packet to the dual communication link according to a judgment result, and collecting a data transmission state in a receiving time window; and when it is monitored that the original data packet is lost in the receiving time window, extracting a backup data packet of the original data packet for data recovery, and recalculating the link score of the original data packet. Through organic combination of multi-dimensional link quality evaluation, link selection of weighted calculation, double-link parallel transmission and rapid data recovery, the network resource utilization efficiency is improved while the data transmission reliability is guaranteed.

The invention discloses a geological mineral exploration data extraction method and system, and relates to the technical field of geological mineral exploration data extraction, and the method comprises the steps: carrying out the data collection of a target region through a multi-source data collection method, obtaining satellite remote sensing data, unmanned aerial vehicle aerial survey data and ground geophysical prospecting data, and obtaining an original data set; preprocessing the collected data by adopting a preliminary data processing method, removing noise and correcting errors to obtain a high-quality data set; integrating data from different sources in the high-quality data set by adopting a weighted fusion algorithm to generate a comprehensive database and obtain a comprehensive data set; performing high-resolution geophysical exploration on the fused comprehensive data set by adopting a geophysical exploration technology to obtain underground structure information; analyzing the exploration data by adopting a geophysical inversion technology, and identifying the position of a potential ore body to obtain an ore body distribution map; the method is based on the comprehensive data set, underground structure information and the ore body distribution diagram.

The invention relates to the technical field of data processing, and discloses an assembly type slope support engineering digital quality tracking method and system. The method comprises the following steps: establishing a digital twinborn model for slope data; assigning an identifier to the prefabricated part and collecting quality data; performing real-time measurement, comparison and evaluation on the assembly process; monitoring the connection node and analyzing to obtain a health score; analyzing environment and load influence based on the score; and mining data association to obtain a quality evaluation and prediction model. According to the invention, all-around quality monitoring, evaluation and prediction of component manufacturing, field assembly, node connection and a service process are realized, so that the defects of fragmentation, passive and apparent quality control in the prior art are overcome, and the overall quality management level of an assembly type slope support project is improved.

The invention provides a cross-platform network speed measurement and adaptive path switching method and device and a storage medium. The method comprises the following steps: receiving a target domain name and a multi-source DNS server list; extracting historical network quality data corresponding to the environment identifier and each candidate IP address, inputting the historical network quality data into a link quality prediction model to obtain a prediction score of each candidate IP address, and screening according to a preset threshold to obtain a target IP subset; performing active detection of limited times on each IP address in the target IP subset to obtain a detection index; calculating the comprehensive score of each IP address based on the prediction score and the detection index, determining the optimal IP address with the highest comprehensive score, and recording the DNS server address of the optimal IP address; and establishing a network connection path according to the optimal IP address, and providing a DNS server address of the optimal IP address for a service layer. According to the method and the device, the cross-platform maintenance cost can be reduced, the real-time performance and the performance overhead are considered, the path is dynamically optimized, and rapid connection switching is realized.

The invention provides an intelligent electronic assembly quality control system based on AOI (automatic optical inspection) and SPI (solder paste printing inspection) integration, and belongs to the field of electronic information equipment flexible production lines. Comprising an AOI module, an SPI module, a data integration module, a feedback control module and a data storage and transmission module. All the modules carry out high-frequency data transmission through an industrial Ethernet; according to the system provided by the invention, the precision of quality detection is improved, and the accurate recognition of defects is realized through the data association of the AOI and the SPI module and the anomaly detection of the SVM and the CNN algorithm. Real-time process optimization is achieved, the feedback control module automatically adjusts printing and mounting process parameters according to the quality data model, and the product consistency is remarkably improved. The method improves the traceability and safety of the data, achieves the efficient storage and safety access of the detection data through distributed cloud storage and data encryption transmission, and supports the quality problem traceability.

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention provides an urban construction and traffic engineering investigation and design enterprise product quality management system and method. The system comprises a cross-project type quality data fusion module, a dynamic defect evolution prediction module, an intelligent layered spot check engine, a three-dimensional quality situation deduction module, a conference knowledge graph construction module, a result influence evaluation module and a self-adaptive file management system. All the modules are connected through a unified data bus to form a closed-loop management process of quality data acquisition, analysis, decision and feedback. According to the system, through the functions of multi-modal data fusion, defect prediction, intelligent spot check, situation deduction, knowledge precipitation, achievement evaluation, file management and the like, the whole-process intelligent management and control of the project quality are realized, and the management efficiency and the decision scientificity are improved.

The invention relates to the technical field of iron and steel smelting, in particular to a continuous casting quality data processing method based on multi-modal cognitive reasoning, which comprises the following steps of: synchronously acquiring multi-source sensor data, performing time alignment, format standardization, noise filtering and normalization processing, extracting image / thermal image spatial features through CNN (Convolutional Neural Network), and calculating the continuous casting quality data according to the image / thermal image spatial features. The LSTM is combined with frequency domain analysis to extract vibration / sound time sequence features, cross-modal alignment fusion is realized through feature vectorization, quality prediction, anomaly detection and real-time deployment are realized based on a deep neural network and an attention mechanism by fusing process knowledge, and then slab defect causes are accurately identified by integrating a multi-modal reasoning result. Through time synchronization, format unification, noise filtering and data filling, the problem of heterogeneity of multi-source sensor data is solved, and time dislocation and noise interference are eliminated; abnormal data are cleaned by an outlier removal technology, the reliability of input data and the model robustness are improved, and efficient cooperative processing of multi-modal data is ensured.

The invention provides a construction robot operation path optimization method, and belongs to the technical field of automatic scheduling and path optimization, and the method comprises the steps: 1, distributing an optimal operation sequence for each robot based on a construction task; 2, acquiring BIM data and field environment information, and determining an initial construction path of each robot in combination with the optimal operation sequence of each robot; 3, the initial construction paths of all the robots are analyzed, and the construction path of each robot is determined based on the path analysis result; 4, the position and speed of each robot are collected in real time in the construction process, the robot operation coverage rate, shutdown time and construction quality data are periodically counted, and then the path planning coefficient of each robot is determined; and 5, iteratively optimizing the path planning parameter of each robot based on the path planning coefficient of each robot until a preset efficiency improvement target is met. And the overall construction efficiency and the intelligent level are obviously improved.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

The invention provides a data correction method and system combined with lattice structure additive manufacturing process characteristics, and the method comprises the steps: firstly employing a Newton iteration method, taking the radius of a pillar as a variable, optimizing the relative density of an iteration target and generating a lattice structure geometric model under the condition that a process constraint condition is satisfied, and extracting actual geometric parameters after forming through CT scanning, the elastic modulus is corrected through the pillar diameter deviation and the defect volume fraction, a compression failure mechanism is combined, a density-related failure criterion is introduced, a nonlinear relation with the relative density is constructed through specific energy absorption data, material plasticity parameters are inversely optimized, and a defect coupling evaluation model is constructed according to the surface powder sticking rate and the pillar diameter deviation. And establishing a Gaussian mixture model based on a stress-strain curve to screen out abnormal data, and finally fusing the parameters to construct a data correction model. The dot matrix structure design precision can be improved, and then a high-quality data basis is provided for performance prediction-structure design two-way feedback.

The invention discloses a tea quality data safety management monitoring method and system, and particularly relates to the field of intelligent agriculture, and the method comprises the following steps: I, collecting various tea quality data of tea planting, processing, warehousing and logistics processes in real time, and integrating historical quality evaluation data as a comparison reference source; iI, calculating the reliability weight of each piece of tea quality data in the current environment in real time, performing weighted fusion processing on each piece of data, and performing homomorphic encryption on the fused data; according to the method, privacy leakage is effectively prevented, the adaptability of a detection model is enhanced, the real-time response speed in the detection process is increased, the detection accuracy and the automation level are improved, the reliability and stability of multi-source heterogeneous data fusion are improved, the robustness and the intelligent error correction capability of the whole monitoring system are enhanced, and the monitoring efficiency is improved. And multi-chain linkage, data consistency verification and non-tampering evidence storage are realized.

The invention discloses a depression risk screening optimization method based on large and small model linkage. The method comprises the following steps: selecting depression related indexes, obtaining interviewee questionnaire data, and preprocessing the data to obtain a scale source database; a depression risk prediction model is constructed, and PHQ-9 measurement results are compared for model training and verification; training a dialogue strategy module of a semantic analysis enhanced fine-tuning training large language model, constructing answer mapping through dynamic question generation and dialogue flow control, and converting a natural language of a user into standardized data required by a small model; training a man-machine interaction reinforcement learning model, generating a depression risk screening result based on a small model, inviting a user to carry out recognition degree evaluation, and dividing feedback into two types of recognition and question; for different feedbacks, strengthening or correcting the current interaction strategy and prediction logic, and storing the audited data as high-quality data to a training database by the system for subsequent large model fine tuning; and outputting a result and performing result interpretation and suggestion by using the semantic analysis reinforced fine-tuning large language model. Hierarchical early screening of depression risks is carried out based on large and small model linkage.