5260 results about "Quality control" patented technology

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 provides a cast workpiece three-coordinate structured light rapid measurement system, and relates to the technical field of industrial automatic measurement, and the system comprises a calibration parameter module which is used for calibrating a standard ball, building an initial conversion relation between a structured light coordinate system and a three-coordinate measuring machine world coordinate system, and collecting temperature, humidity and vibration data in real time; dynamically updating the conversion relation to form calibration parameters; and the measurement path planning module is used for dividing the casting workpiece into a plurality of measurement areas based on the calibration parameters, planning a motion path of the three-coordinate measuring machine and driving the motion platform to move to each measurement position. According to the invention, full-process automation from calibration, measurement to modeling analysis of the casting workpiece and the advantage of comprehensive quality analysis are realized, and a reliable three-dimensional measurement and quality control solution is provided for the casting industry.

The invention relates to a digital integrated quality management system based on multi-source data fusion, and belongs to the technical field of industrial internet and quality management. A data acquisition layer of the system obtains real-time and static multi-source heterogeneous data through a multi-source adapter; the data processing layer is used for cleaning, converting and standardizing the acquired data; the intelligent analysis layer performs deep analysis and prediction on the data by using an adaptive quality prediction model, an anomaly detection module and a root cause analysis engine; the application service layer displays a quality trend and an anomaly detection result through a visual billboard, and provides credible tracing and collaborative decision-making functions; and the feedback closed layer adjusts system processing logic according to the decision support data to form closed-loop quality control. According to the method, real-time fusion and efficient utilization of multi-source data are realized through a dynamic routing technology, an adaptive quality prediction model and a block chain evidence storage mechanism, and the intelligent level and decision-making efficiency of quality management are remarkably improved.

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

The invention relates to the technical field of data processing, and discloses a quality control method and system based on buoy observation data. The method comprises the following steps: performing multi-dimensional anomaly detection processing on preprocessed standard format data to obtain an anomaly detection report marked with abnormal and suspicious data points; performing multi-source data fusion and cross validation processing on the data in the anomaly detection report to obtain an initial data quality score; performing intelligent correction processing on the anomaly detection report to obtain a corrected data set and uncertainty estimation data corresponding to the corrected data set; performing consistency analysis processing on the corrected data set in a space-time dimension to obtain a space-time consistency score and abnormal region marking data; and carrying out comprehensive evaluation processing on the abnormal region mark data to obtain a target data quality grade identifier and a target quality report, and carrying out quality control strategy analysis to obtain a target quality control strategy. According to the invention, the accuracy of quality control based on buoy observation data is improved.

The invention discloses a casting surface treatment quality evaluation method and system, and relates to the technical field of intelligent manufacturing and quality control, and the method comprises the steps: carrying out the multi-modal data collection of a casting, and carrying out the preprocessing; analyzing the preprocessed data, and constructing a comprehensive quality evaluation data set; a quality evaluation model is constructed, abnormal conditions in surface processing are identified, and a quality evaluation result is generated; performing quality classification on the casting according to a quality evaluation result, generating an evaluation report, and providing quality state judgment and related optimization suggestions; the casting surface treatment process is adjusted and optimized, the overall manufacturing quality is improved, and continuous improvement of quality evaluation is achieved. According to the method, the detection precision of the casting surface treatment quality and the process optimization capability are improved. By adopting data-driven closed-loop control, quality abnormity can be accurately identified, optimization suggestions can be provided, intelligentization, stability improvement and long-term quality improvement of the manufacturing process are realized, the defect rate is effectively reduced, and the manufacturing consistency is improved.

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 discloses a multi-source process parameter mapping supervision system and method based on a big data model, and relates to the technical field of process parameter analysis. Initial process parameters in a process production line are collected, the initial process parameters are processed to obtain standardized process parameters, and the process production line is subjected to process stage division; analyzing a stage product deviation degree of the process stage, calculating correlation between the standardized process parameters and the product deviation degree of the process stage, analyzing the stage product deviation degree of the process stage, predicting the product deviation degree of the process stage in production, and predicting a product reject ratio in production based on the product deviation degree of the process stage. According to the method, the product deviation of each stage and the reject ratio of the whole product are continuously predicted, the production state is judged in real time, a dynamic quality control closed loop is constructed, and the pertinence and effectiveness of supervision are improved.

The invention relates to a production process optimization method, system and device for automobile parts and a storage medium, and the method comprises the steps: obtaining design parameters of an automobile brake disc, carrying out the process analysis based on the design parameters, and obtaining an initial process scheme; performing analog simulation on the initial process scheme to obtain simulation optimization parameters; actual measurement process parameters in the production process are obtained, matrix comparison analysis is conducted on the actual measurement process parameters and the simulation optimization parameters, a deviation matrix is obtained, link analysis is conducted according to the deviation matrix, and a list of links to be optimized is obtained; performing iterative simulation adjustment on the simulation optimization parameters according to the list of links to be optimized to obtain optimized process parameters; constructing a three-dimensional sample model based on the optimized process parameters, and performing quality analysis on the three-dimensional sample model to obtain a quality evaluation result; and performing scheme analysis based on the quality evaluation result in combination with the optimization process parameters to obtain a corresponding production process optimization scheme. The production efficiency and the product quality can be effectively improved.

The invention discloses an intelligent supervision system and method for environmental monitoring quality control, and particularly relates to the technical field of environmental monitoring, after environmental monitoring data is collected in real time through multiple types of sensors and formatted, the system analyzes fluctuation conditions of various types of data and identifies abnormal fluctuation hidden dangers; if the hidden danger is found, performing drift verification on the corresponding sensor, extracting noise and drift characteristics, and generating a data noise index and an accumulated drift index; based on the indexes, using fuzzy logic to divide the sensors into three classes of serious drifting, moderate drifting and low horizontal drifting; performing correction analysis according to a division result, and selecting local or full-category calibration operation to optimize the quality of the monitoring data; by introducing an intelligent evaluation mechanism, the drifting condition and the data noise level of each sensor can be monitored and analyzed in real time, which sensors need to be calibrated can be dynamically judged, local or full-category calibration is selected according to actual conditions, and long-term accuracy and reliability of environmental monitoring data are ensured.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention discloses a health food production line control method and system based on deep management, and relates to the technical field of food production control. A raw material feature database is established, a first analysis result is output as a raw material deviation evaluation reference, data of a current batch of raw materials are collected, differences are compared, and a raw material deviation result is output; the method comprises the following steps: collecting process data, analyzing whether the process data is abnormal or not by combining with raw material deviation, generating an adjustment suggestion, executing formula and parameter adjustment, triggering a self-repairing mechanism when the process data is abnormal, identifying an intermediate product state, evaluating a quality risk level and triggering early warning. Dynamic optimization of a formula and process parameters is achieved, meanwhile, an image and spectrum fusion early warning technology is introduced, the quality control ability of intermediate products is improved, finally, optimization parameter configuration is generated for the next batch through whole-process data chain construction and causal reasoning analysis, and self-adaptive control and quality steady-state improvement in a production closed loop are achieved.

The invention discloses an electronic medical record automatic quality control system and method based on a large language model, and the method comprises the steps: extracting medical record data from a medical information system, and carrying out the data preprocessing; and performing deep semantic analysis on the medical text by utilizing a large language model, and extracting key diagnosis and operation information. And performing semantic representation on the medical terms by fine tuning the pre-training language model and combining with a multi-layer attention mechanism. And constructing an ICD coding knowledge graph, and calculating the semantic similarity between the diagnosis terms and the ICD coding terms in the electronic medical record by using an embedded vector technology to realize automatic matching. And according to a predefined quality control rule, automatically checking the electronic medical record through the logic expression, and generating a quality control report and an improvement suggestion. And finally, visually displaying quality control and coding results, and outputting a quality control report. The electronic medical record quality control efficiency and accuracy are improved, and standardization and automation of quality control work are achieved.

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

The invention provides a thin film surface defect detection method and system, and relates to the technical field of defect detection.According to the thin film surface defect detection method and system, an intelligent secondary verification link is constructed by introducing a defect confidence evaluation mechanism based on form and energy distribution, so that the detection performance is fundamentally improved; according to the mechanism, real physical defects with regular forms and concentrated energy and pseudo defects caused by electromagnetic interference, instantaneous film wrinkles and the like can be accurately distinguished, and the problem of high false alarm rate caused by dependence on single signal strength in the prior art is effectively solved while the high detection rate of low-contrast defects is reserved; besides, the judgment model based on physical characteristics has natural robustness for background noise generated in high-speed motion, and an adjustable confidence threshold value endows the system with extremely high practical flexibility, so that the system can adapt to complex and changeable industrial environments and different quality control standards, and the method is suitable for large-scale popularization and application. And the accuracy, the reliability and the intelligent level of the whole detection system are obviously enhanced.

The embodiment of the invention provides a multi-source heterogeneous data integration method and device fusing a large model conversion operator. The method comprises the steps that heterogeneous data are collected from a multi-source heterogeneous data source, preprocessing operation of data cleaning is carried out, and preprocessed data features are obtained; inputting the preprocessed data features and the target format into a large language model, and generating a conversion operator including data structure analysis, field mapping and type adaptation; and based on the conversion operator, performing distributed parallel data conversion and integration in a distributed computing framework, and integrating and verifying the integrated data. According to the scheme, by introducing the intelligent reasoning ability of a large language model, the efficiency optimization of distributed calculation and the quality verification mechanism of the whole process, the core problems of the traditional data integration technology in the aspects of rule stiffness, high manual dependency and quality control deficiency are systematically solved.

The invention relates to the technical field of aluminum alloy cold stamping quality control, in particular to a multi-source data fusion and real-time optimization-based cold stamping quality control method, which comprises the following steps of: inputting a fusion feature space into a pre-trained LSTM (Long Short Term Memory) model, and associating a real-time stamping speed with a dwell time parameter through an attention mechanism; combined prediction of springback value deviation and defect probability is realized for the first time, a process parameter correction value is dynamically generated based on a prediction result, and a thermal expansion compensation algorithm and micron-sized mold compensation are combined, so that the quality regulation response speed reaches a millisecond level, the compensation precision is improved to + / -2 [mu] m, and the error is reduced by more than 50% compared with that of traditional PID (Proportion Integration Differentiation) control.

The invention provides a wire harness production quality control system based on artificial intelligence. The wire harness production quality control system comprises a wire harness data acquisition module, a wire harness knowledge graph module, a wire harness quality correlation analysis module, a wire harness quality causal reasoning module, a wire harness quality prediction module, a wire harness parameter optimization module and a wire harness decision support module. The system identifies complex association through a graph neural network, discovers problem root causes through time sequence analysis and causal reasoning, optimizes production parameters in combination with a virtual environment, and provides scientific decision support. According to the method, the technical problems of complex correlation identification, time-delay effect capture, causal relationship determination, parameter optimization and the like in wire harness production are solved, and intelligent quality control of the whole process is realized.

The invention relates to the technical field of printed circuit board assembly quality detection, and discloses a PCBA anomaly detection method and system based on three-dimensional modeling and AI fusion, and a medium. The method comprises the following steps: acquiring three-dimensional point cloud data of a PCBA board to be detected; generating a reference three-dimensional digital twin model according to a standard PCBA design drawing; carrying out spatial registration on the three-dimensional point cloud data and the reference three-dimensional digital twin model, obtaining the three-dimensional point cloud data, carrying out hierarchical processing on the obtained three-dimensional point cloud data, extracting geometric features of a welding spot region, contour features of an element region and surface features of a substrate region, and carrying out fusion to generate a feature vector group; constructing a generative adversarial model based on a preset semi-supervised learning framework and the normal PCBA sample vector group; and inputting the feature vector group into a generative adversarial model, and examining the abnormal vectors, the corresponding three-dimensional coordinates and the abnormal types in the feature vector group by the generative adversarial model to complete the abnormal detection of the PCBA board. The method is suitable for quality control of a high-density and miniaturized PCBA.

The invention provides a construction method of a composite trauma medical record intelligent quality control system, and relates to the technical field of multi-modal data intelligent processing, and the method comprises the steps: obtaining trauma medical record data containing a text record and a medical image, generating an image-text confrontation sample based on a shielding recombination mechanism, and carrying out the reconstruction of the image-text confrontation sample; and mapping the text and image features to a unified feature space by adopting a cross-modal embedding method to generate semantic association features. And the system maps the semantic features with a preset quality control rule, constructs a clinical event atlas, identifies missing information through causal effect analysis and anti-factual reasoning, and generates quality control defect data. And then, constructing a risk propagation model according to the number of defects and criticality, forming a thermal distribution matrix and an evolution path, calculating a medical record risk score, and finally realizing quality control alarm output and data writing. The method has the characteristics of high universality, high timeliness and high intelligence, and is suitable for quality control system construction tasks in various complex data environments.

The invention relates to the technical field of process parameter analysis, and discloses a process parameter analysis method and system for a foaming material forming process and a storage medium, and the method comprises the following steps: carrying out parameter collection and data statistics on an MPPO material under a 160 DEG C standard process condition to obtain process parameter reference data; carrying out a load pressure parameter orthogonal test on the material to obtain a load pressure process parameter database; forming temperature gradient analysis is carried out, sampling testing is carried out at the interval of 5 DEG C from 160 DEG C to 190 DEG C, and a temperature control curve is obtained; performing coupling analysis and pressure dynamic compensation to obtain a forming process parameter combination; carrying out wet weight, dry weight and size detection on a product produced under the forming process parameter combination, and carrying out multi-dimensional parameter calculation to obtain a quality control index; and performing association verification and parameter correction on the quality control index and the forming process parameter combination, and outputting target process specification data. The method improves the control precision of the process parameters.

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 an automatic NL2SQL data set construction method and system based on a large language model. The method comprises the steps that service-oriented SQL query data collection is carried out; the method comprises the following steps of: collecting SQL query statements related to business through a channel, and cleaning and standardizing an obtained SQL sample by adopting a data preprocessing technology; generating NL2SQL data based on a large language model; a mapping pair between the natural language and the SQL is generated by using a large language model, corresponding natural language description is generated through reverse derivation according to a specific business problem and a table connection mode, and the conversion process from the SQL to the natural language is realized; optimizing the quality of the NL2SQL data set; and dynamically updating a data set for private deployment. According to the method, an automatic SQL sample generation mechanism and a multi-dimensional quality control process are introduced, so that manual intervention is remarkably reduced, and the diversity and accuracy of a data set are improved.

The invention relates to the technical field of production line defect detection, in particular to an intelligent packaging production line defect detection method and system based on an image recognition model. The method comprises the following steps: carrying out packaging container surface defect analysis on an empty packaging container to generate a container inherent defect area; capturing a disturbance response time sequence image sequence based on the inherent defect area of the container after the liquid product packaging operation of the empty packaging container is completed; constructing a motion image recognition model, and performing motion area recognition on the disturbance response time sequence image sequence to obtain a time sequence motion area segmentation map; detecting internal and external impurity defects of the package according to the time sequence motion area segmentation map to obtain internal defect list data of the product; and when the product internal defect list data is non-empty, executing corresponding defective product removal control. High-precision intelligent identification of internal and external impurity defects of the liquid packaging product is realized through the image identification model, and the quality control level of a production line is remarkably improved.

The invention provides a disinfection equipment production and processing quality control method and system based on intelligent analysis. Precise control of the drying process is achieved through three-stage collaborative optimization, firstly, hot air distribution parameters and historical energy consumption data of the drying process are integrated, a hot air uniformity and energy efficiency collaborative optimization model is constructed, and a dynamic target parameter system is established. Secondly, temperature field distribution data are collected in real time in the equipment operation process, multi-dimensional dynamic correlation analysis is conducted on the temperature field distribution data and energy consumption data of the heat supply device, heat field abnormal fluctuation and energy consumption correlation characteristics are recognized through a machine learning algorithm, and finally a heating power dynamic adjusting instruction is generated based on an analysis result; and by adopting the synergistic effect of a differential control algorithm and a frequency conversion regulation technology, process requirements are accurately converted into power supply frequency regulation and control signals, and millisecond-level dynamic response and self-adaptive regulation of the heating power are realized. According to the technical scheme provided by the invention, the production and processing quality control efficiency and accuracy of the disinfection equipment can be improved.

The invention discloses a high-precision industrial quality control system based on an AI large model and an application method, which are characterized by being applied to detection and measurement of industrial products. Comprising a product data acquisition module in an industrial production process, a data preprocessing module, an AI large model suitable for industrial quality control, a quality detection module, a result feedback and decision support module, a control module and a system management module, the application of the AI large model suitable for industrial quality control comprises the steps of constructing the AI large model suitable for industrial quality control, calling the AI large model suitable for industrial quality control, executing the control system, carrying out multi-modal data fusion, and carrying out multi-modal data fusion. The product data acquisition module in the industrial production process acquires data of multiple modes for quality detection and measurement, the AI large model suitable for industrial quality control adopts a data fusion algorithm, and the feedback module provides a visual detection result. And visually displaying the detected quality problem to a user in the form of an image and a chart.

The invention discloses an AI quality control system based on digital twinning, and relates to the technical field of injection molding product production, and the system comprises a data acquisition module, a digital twinning modeling module, an AI quality detection module, a quality analysis module and an environment optimization module. The data acquisition module collects production and environment data in real time, the digital twin modeling module obtains a high-precision model by simulating the production process, and the AI quality detection module compares simulation data with standard data, calculates a forming deviation coefficient and adjusts process parameters. The quality analysis module detects the injection molding defect risk through the stress concentration coefficient, and the environment optimization module monitors the production environment and provides an optimization strategy when an early warning instruction is triggered. The system can monitor the production process in real time, dynamically adjust technological parameters, improve the production efficiency and the product quality and reduce the rejection rate.

The invention discloses a detection method, device and equipment in an RFID tag manufacturing process, and the method comprises the steps: obtaining stress monitoring data of a plurality of sections of a manufacturing assembly line, and carrying out the preliminary screening; multi-dimensional optical and thermal detection is carried out on the suspected abnormal labels, and deformation feature points are determined; carrying out partitioned microwave side emission test and impedance mismatch analysis on the tag with the real deformation risk, and evaluating the radio frequency performance; performing intelligent section repairing processing on the to-be-repaired label; and obtaining data of each section, performing multi-dimensional fusion analysis, obtaining optimized parameters, and performing real-time adaptive adjustment. According to the technical scheme, the RFID tag manufacturing process can be comprehensively monitored, abnormal tags can be accurately identified and positioned, intelligent repair and process optimization are achieved, the product quality and the production efficiency are effectively improved, the defective product rate is reduced, and an innovative quality control solution is provided for RFID tag manufacturing.

The invention discloses a hybrid deep learning model construction and output interpretation method for spaceborne GNSS-R sea surface significant wave height inversion. The method comprises the steps of obtaining CYGNSS L1-level GNSS-R data, an European mid-term weather forecast center (ECMWF) ERA5 reanalysis data set, IMERG rainfall product data and WaveWatch III SWH data; carrying out preprocessing and quality control on the acquired data; performing space-time matching on a data set obtained after quality control, and dividing the data set into a training set, a test set and a verification set according to a certain proportion; the method comprises the following steps of: constructing a hybrid deep learning model which is combined with efficient multi-scale attention and CNN-Transform-BiConvLSTM, and carrying out training and verification on the model; a MultiScaleWaveFormer model is created to deeply learn an SHAP interpreter, and the importance of the input features is calculated and visualized; the method comprises the following steps of: inputting an inversion result into a trained MultiScaleWaveFormer model to obtain an inversion result; and finally, comparing the SWH inversion result of the model with the SWH inversion result of the existing model, and evaluating the performance of the model. According to the invention, the five modules are combined, higher efficiency and functionality are realized, and the accuracy of spaceborne GNSS-R data inversion significant wave height is further improved.