98 results about "Quality management system" patented technology

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 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 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 discloses a wind power generation ring forging three-dimensional design and quality management system based on 5G communication, and belongs to the technical field of wind power equipment manufacturing. The system comprises a modeling module used for constructing a parameterized three-dimensional design model and outputting design parameters used for quality evaluation and defect identification; the process data acquisition module acquires process data such as temperature, pressure and vibration of the key structure area in real time; the defect identification module fuses the image and the signal data, and outputs defect types, positions and sensitivity influences thereof; the quality evaluation module performs quantitative scoring on the product quality based on the scoring function and the disturbance response parameters; the feedback regulation and control module identifies the dominant deviation and generates a regulation and control instruction when the score is abnormal; and the 5G communication module realizes high-speed data interaction between the modules. The method realizes closed-loop control of design, manufacturing, detection and evaluation, improves the manufacturing quality and efficiency of ring forgings, and is suitable for manufacturing scenes of large-size and high-reliability wind power structural members.

The invention provides an efficient quality management system for a cement production line, and the system comprises an online analysis device which is used for obtaining the chemical component data of a raw material and the technological parameters in the production process; the data processing unit is used for preprocessing the chemical component data and the process parameters to obtain standardized data; the mathematical model is obtained based on historical production data and product quality target training and used for predicting product quality according to the standardized data; the data acquisition module is used for continuously acquiring real-time data in the production process and forming a dynamic data set; the model optimization unit is used for carrying out iterative optimization on the mathematical model by utilizing the dynamic data set; the automatic equipment is used for executing raw material ratio adjustment and process parameter optimization according to the optimized control strategy; and the quality detection equipment is used for detecting the final product in real time, acquiring key index data and comparing the key index data with a preset quality target to form a closed-loop feedback mechanism.

The invention discloses a full-link quality tracing method driven by batch of a process card, relates to the technical field of intelligent manufacturing and quality tracing, and aims to automatically generate a dynamic process card batch number through PDA code scanning and work reporting and realize full-process seamless association and tracing from material picking, film spraying, bag making to inspection. According to the method, a defect propagation path prediction mechanism is innovatively introduced, all associated batches are automatically locked when a certain batch is detected to be bad based on the BOM topological relation, the traditional tracing time is compressed from the hour level to the second level, and the quality problem response speed and accuracy are greatly improved. Meanwhile, the system intelligently recommends the optimal reworking path in combination with historical reworking data, the reworking time can be reduced by about 40%, and the production efficiency and the resource utilization rate are remarkably improved. According to the technology, real-time acquisition, dynamic analysis and intelligent decision support of quality data are realized, and core technical support is provided for an enterprise to construct an efficient, transparent and traceable full-link quality management system.

The invention relates to the technical field of data management of intelligent manufacturing, and particularly discloses a 12-shaft PCB drilling machine production data acquisition and quality management system, which is characterized in that high-speed sensors are mounted on shafts of a drilling machine to acquire speed and pressure data in a drilling process in real time; a speed change rate characteristic value and a pressure stability characteristic value are extracted by adopting a method of combining differential speed analysis with Haar wavelet transform, zero-sequence pressure processing and principal component analysis, the two types of characteristic values are fused into a drilling quality characteristic vector, the drilling quality characteristic vector is input into a quality analysis model based on gradient boosting tree training, and the drilling quality is obtained. Intelligent evaluation and prediction of the drilling quality are realized, when quality abnormity is detected, the system automatically generates early warning information, and operation parameters of the drilling machine are dynamically adjusted in combination with a PID control algorithm, so that a closed-loop quality control mechanism is formed.

This invention relates to a fabric defect detection and traceability system based on edge computing and computing power scheduling, applicable to intelligent quality control in textile production processes. The system includes a data acquisition unit and a modeling unit. The data acquisition unit acquires fabric images and environmental data through multispectral imaging equipment and process parameter sensors, and constructs a time-aligned multimodal feature tensor. The modeling unit combines fabric material characteristics and uses unsupervised contrastive learning to extract texture features and generate potential texture fingerprint vectors. The detection unit uses a target detection network with a channel attention mechanism to identify fabric defects and outputs their location, type, and severity. The traceability unit analyzes the correlation between defects and process parameters through temporal causal reasoning, constructing a causal graph. The optimization unit generates process optimization vectors based on the causal graph and risk score, and realizes visualization and edge control feedback, thereby constructing a closed-loop quality management system for real-time defect control and interpretable traceability.

The invention relates to the field of quality management optimization, and discloses a vaccine clinical test quality management system optimization method and system, and the method comprises the steps: obtaining vaccine clinical test data of a target test group, building an interaction information network according to the extracted case sharing test information, carrying out the node graph convolution propagation of the interaction information network, and obtaining a target test group; a structural information embedding vector is obtained, a structural outlier center of vaccine clinical test data is identified, abnormal type hierarchical analysis is performed on the structural outlier center to obtain a quality deviation classification result, and a local structural abnormal cluster is identified according to an acquired abnormal propagation path. And generating a local quality control strategy according to the quality deviation classification result and the local structural anomaly cluster, and optimizing the vaccine clinical test quality management system. According to the method, the structural outlier center in the vaccine clinical test data can be effectively captured and the test data interaction diagram structure can be constructed in the vaccine clinical test data lacking a natural interaction network.

The invention discloses an intelligent accurate data quality management system, method and device, and relates to the technical field of data quality management. When a rule is changed, a rule updating event is generated and published through Kafka, when the rule is updated, the difference between a new rule and an old rule is detected, and when the difference exceeds the expectation, a rollback mechanism is automatically triggered, so that the accuracy of the data quality management is improved. Verifying the preprocessed data by using a preset rule, classifying the abnormal data to obtain a plurality of abnormal data classes, and automatically correcting the classified abnormal data; identifying and classifying abnormal data which still cannot be corrected, migrating an isolated data class obtained by identification to an isolated queue from a main data stream, retaining the abnormal data which cannot be processed immediately in the isolated queue, and regularly reminding a data receiving end to process the abnormal data; and the exceptions which cannot be processed in real time are continuously left in the isolation queue, and a data receiving end is notified at a constrained reminding frequency so as to ensure that the potential high-risk exceptions are not accumulated for a long time.

The invention discloses an automobile safety part welding quality management system, and relates to the field of quality detection. Comprising a data acquisition layer for acquiring multi-dimensional data of a weldment by using weldment quality detection equipment; the data processing layer is used for processing the collected data, a defect evaluation model is arranged in the data processing layer, processed output is input into the defect evaluation model, and a weldment quality evaluation result is output; and the defect classification module is used for classifying the weldments with defects according to the output quality evaluation result. By establishing the standardized welding data management system, on one hand, welding quality evaluation can be carried out after welding multi-dimensional data are collected and fused, the welding quality evaluation precision is improved, on the other hand, welding defective weldments are classified, analysis and welding process optimization are facilitated, and the welding quality is improved. And meanwhile, distributed storage is adopted for data storage, and dynamic space optimization can be performed on each storage unit, so that data over-limit loss is prevented, and the data read-write operation fluency of the system is also improved.

The invention discloses a water conservancy project construction progress and quality management system and method, and belongs to the technical field of water conservancy project construction progress and quality management, and the method comprises the steps: S1, obtaining real-time construction parameters, a design reference value and environment load data; s2, based on the real-time construction parameters and the design reference value, calculating to obtain a comprehensive construction deviation index; s3, combining the building information model data and the comprehensive construction deviation index to construct a digital twinborn model containing an initial defect; s4, based on the digital twinborn model containing the initial defect, coupling the comprehensive construction deviation index and the environmental load data, and calculating to obtain an accumulated damage factor; and S5, determining a long-term health degree score based on the accumulated damage factors, and laying an accurate mathematical foundation for subsequent full life cycle performance prediction.

The invention relates to the field of engineering management, and particularly discloses a quality management system and method for engineering construction, and the method comprises the steps: firstly carrying out the preprocessing of a BIM model, and constructing a reference frame integrating visual features and spatial geometric information; when a problem image shot by a mobile terminal at a construction site is received, a computer vision and space calculation technology is introduced, a two-dimensional field image is analyzed into an accurate pose of a shooting camera in a three-dimensional BIM space, and sight line tracing is automatically performed in a BIM digital model based on the accurate pose. And intelligently inferring and retrieving candidate components which are highly matched with the perspective of the problem. And finally, on-site unstructured problem data and the unique identifier of the BIM component are firmly bound through lightweight interaction confirmation with the user, and a standardized structured problem record is obtained. Therefore, the problems of low manual positioning efficiency and unstable association can be solved, and the whole-process intelligent closed loop of quality problem data from acquisition to structured storage is realized.

Systems and methods for improving query creation and AQM are provided. A user interface is provided that allows an analyst to generate and test queries. After the analyst provides a query, one or more subsets of previous call center interactions are selected to test the provided query using methods such as a large language model or string vectorization. Once the subsets of interactions are selected, an LLM is used to evaluate the query against the subset. The evaluations may indicate interactions which were positive and which interactions were negative and may include quotes from the interactions that support the evaluations. The evaluations are presented to the analyst for review, and the analyst may revise the query in view of the evaluations. Once the analyst is satisfied with the performance of the query, the query may be used by the LLM to perform AQM for new interactions in the call center.

The invention discloses a positioning and anchoring device and method for a foam core mold and a reinforcement cage of a hollow slab girder, the device comprises a connecting part connected with the foam core mold, an anchoring part for anchoring the reinforcement cage, a positioning part positioned on a pedestal and a bolt connecting pair, and quick installation and reutilization are realized through a threaded connection and bolt fixing technology. According to the method, a real-time monitoring system is introduced, a correction instruction is generated in combination with data collection and a closed-loop control algorithm, constructors are assisted in accurately adjusting the position of the reinforcement cage, and dynamic errors in the pouring and vibrating process are effectively restrained. And meanwhile, a data-driven quality management system is constructed, and digital recording and process optimization of the construction process are realized. According to the invention, multiple construction scenes are compatible through modular design, the positioning precision and the construction efficiency are remarkably improved, the material waste is reduced, and a reliable guarantee is provided for the structural safety and durability of the prefabricated hollow slab beam.

The invention discloses a quality management system evaluation and authentication system based on multi-source heterogeneous data processing, and belongs to the technical field of quality management. A quality management system evaluation and authentication system based on multi-source heterogeneous data processing comprises a data acquisition module, a data preprocessing module, a data fusion module, a data analysis module, a quality management system grading model, an evaluation and authentication module and a user management and interaction module. According to the invention, the problems of single data source, need of manual acquisition of evaluation data and poor evaluation efficiency in evaluation authentication of the existing quality management system are solved. Through automatic acquisition and processing of the multi-source heterogeneous data, the comprehensiveness and accuracy of the data are remarkably improved, the labor cost is greatly reduced, the evaluation efficiency and quality are improved, and a more reliable quality management evaluation result is provided for enterprises.

The embodiment of the invention provides a quality management system authentication contract review code matching model construction and prediction method and a quality management system authentication contract review code matching model construction method. Comprising the following steps: collecting CNAS39 large-class code system data, business license operation range expression data of each industry, historical review case data and policies and regulations which are published by a supervision department and are about the consistency of an authentication range and a business license range, and constructing a professional knowledge base according to the collected data and the policies and regulations; pre-training a language model by using the professional knowledge base to obtain a pre-trained language model; and training data is constructed according to the historical review case data, the pre-training language model is finely adjusted by adjusting hyper-parameters, and a code matching model for quality management system authentication contract review is obtained.

The present invention discloses a three-dimensional design and quality management system for wind power ring forgings based on 5G communication, which belongs to the field of wind power equipment manufacturing technology. The system includes: a modeling module for constructing a parametric three-dimensional design model and outputting design parameters for quality assessment and defect identification; a process data acquisition module for real-time acquisition of process data such as temperature, pressure, and vibration in key structural areas; a defect identification module for fusing image and signal data, and outputting defect type, location, and its sensitivity impact; a quality assessment module for quantitatively scoring product quality based on a scoring function and disturbance response parameters; a feedback control module for identifying dominant deviations and generating control instructions when the scoring is abnormal; and a 5G communication module for realizing high-speed data interaction between modules. The present invention realizes closed-loop control of design, manufacturing, testing, and evaluation, improves the manufacturing quality and efficiency of ring forgings, and is suitable for the manufacturing scenarios of large-scale, high-reliability wind power structural parts.

The application discloses a large-size photovoltaic cell doping / oxidation / annealing process adaptive quality management system, which comprises a data acquisition layer, an intelligent decision-making layer and a control execution layer; the data acquisition layer collects process parameters through doping, oxidation and annealing PLC systems, constructs a time-space aligned process data cube, and outputs a multi-dimensional feature matrix containing time x process x parameter; the intelligent decision-making layer analyzes the multi-dimensional feature matrix by using a random forest hidden crack classification model and outputs a hidden crack risk grade; the control execution layer comprises a dynamic compensation strategy composed of a diffusion furnace temperature control module and an annealing cooling compensator, is used for generating an OPC UA instruction according to the hidden crack risk grade, and triggers an annealing furnace PID regulator through the OPC UA instruction to dynamically adjust process parameters of the annealing furnace; the application solves the data island problem by integrating multi-process PLC data and the random forest model, realizes high-precision hidden crack prediction, and significantly improves the quality detection efficiency.

The application relates to the technical field of quality management systems, and particularly discloses a textile dyeing production quality management system based on multi-source data monitoring, which comprises the following modules: a critical recognition module that constructs a continuous airflow field model and identifies a vortex core area in combination with dynamic modal decomposition; a migration analysis module that maps the vortex core area to a dye concentration gradient field and identifies a vortex dominant linkage; a migration analysis module that couples and constructs an airflow-dye coupling transfer function, quantifies migration intensity and obtains a migration intensity index; an inter-cluster extraction module that extracts color difference surge clusters based on the migration intensity index and quantifies features; and a convergence control module that judges and controls effects through a convergence regression model, so as to realize dynamic convergence regulation. The present application integrates multi-source data and physical models, realizes intelligent monitoring of the whole process from migration risk early warning, driving mechanism identification to active control, and improves the uniformity of textile dyeing and the stability of production.

The invention provides a quality data multi-index cross-domain NLP enhanced retrieval and computing power sharing method, which comprises the following steps of: establishing a group headquarter cross-domain quality intelligent platform as a central nervous, and configuring an intelligent retrieval service and data exchange engine basic capability unit for each hierarchical subject; secondly, the basic-level quality management system is connected to the quality intelligent service system to form hierarchical association; and finally, realizing cross-domain retrieval and computing power sharing through intelligent identification, matching functions and cross-node data transmission. According to the method, the data integration problem is solved through the quality information exchange directory, and comprehensive integration and efficient utilization of the quality data are realized; the service collaboration problem is solved through cross-domain data interaction and collaboration, and the collaboration efficiency and the product quality control precision are improved; the technical adaptation problem is solved by deploying a platform and an engine and sharing basic computing power, and stable interaction of data and deep application of the AI technology are guaranteed.

The invention discloses a large-size photovoltaic cell doping / oxidation / annealing process adaptive quality management system. The system comprises a data acquisition layer, an intelligent decision-making layer and a control execution layer. The data acquisition layer acquires process parameters through a doping, oxidation and annealing PLC (Programmable Logic Controller) system, constructs a space-time aligned process data cube, and outputs a multi-dimensional feature matrix containing time * process * parameters; the intelligent decision-making layer analyzes the multi-dimensional feature matrix by using a random forest subfissure classification model, and outputs a subfissure risk level; the control execution layer comprises a dynamic compensation strategy composed of a diffusion furnace temperature control module and an annealing cooling compensator, and is used for generating an OPC UA instruction according to the subfissure risk level, triggering an annealing furnace PID regulator through the OPC UA instruction, and dynamically regulating process parameters of the annealing furnace; according to the method, the multi-process PLC data and the random forest model are integrated, the problem of data islands is solved, high-precision subfissure prediction is achieved, and the quality detection efficiency is remarkably improved.

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

The invention relates to the technical field of engineering quality supervision, in particular to an oil and gas field infrastructure engineering quality supervision method and supervision system. The invention discloses an oil and gas field infrastructure project quality supervision method and supervision system. The supervision system comprises a project quality management system, and a project data transmission system, a project progress summarization system and a project risk early warning system which are respectively connected with the project quality management system. Omnibearing engineering data processing and analysis of the oil and gas field infrastructure engineering are realized, so that a more comprehensive and accurate quality optimization model can be analyzed, the working efficiency of oil and gas field infrastructure engineering quality supervision is enhanced, supervision personnel can obtain more objective, accurate and comprehensive data, and the working efficiency of the oil and gas field infrastructure engineering quality supervision is improved. A superior supervision unit can know the project progress more visually, the problem that front-line supervision personnel decide the quality of supervision work according to experience supervision and personal quality is avoided, and the purpose of standardized, informationized and refined supervision of oil and gas field infrastructure project quality is achieved.

The invention provides a business and quality management system, and the system comprises an acquisition monitoring module which is used for collecting quality related data in a product production process in real time, carrying out the analysis of the quality related data, and carrying out the monitoring of an abnormal event in the product production process based on an analysis result; the exception handling module is used for detecting an abnormal event under the condition that the acquisition monitoring module monitors the abnormal event, and performing exception early warning based on a detection result; the analysis and display module is used for analyzing and counting the quality related data and / or the detection result to generate a related report and displaying the report; and the quality tracking module is used for acquiring the full life cycle data of the product based on the output of the acquisition monitoring module, the exception handling module and the analysis display module so as to track the product quality. By integrating the functions of real-time monitoring, exception handling, full-life-cycle quality tracking and the like, an efficient, intelligent and comprehensive quality management system is provided for enterprises, and the product quality and the production efficiency can be improved.

The invention discloses a semen sample quality management system based on federated learning and dynamic time sequence prediction, which is characterized in that a data acquisition module (100) is used for cleaning received original semen sample detection data, a federated learning cooperation module (200) adopts an improved Paillier homomorphic encryption algorithm to carry out encryption transmission on local model gradient parameters, and a dynamic time sequence prediction module (200) is used for carrying out dynamic time sequence prediction on the local model gradient parameters; the dynamic time sequence prediction module (300) is used for extracting quality characteristics with a time sequence dependency relationship from detection data of a semen sample and carrying out online incremental training on a prediction model; the multi-modal data fusion module (400) is used for extracting a genome feature sequence of a semen sample and analyzing environmental parameters stored in the sample; and the quality evaluation decision module (500) fuses the time sequence prediction model and the genome feature sequence. According to the method, multi-center data collaborative modeling is achieved through a federated learning architecture, the online incremental learning ability is achieved in combination with the multi-modal data fusion technology, the prediction model can be continuously optimized, and the method effectively adapts to the change of sample features.

A method for detecting solid content in a traditional Chinese medicine extraction quality management system is disclosed. This invention addresses the problems of existing detection methods, which not only affect extraction efficiency but also suffer from low accuracy and are prone to errors. The steps of this invention include installing two solid content detection instruments based on different principles on the extraction tank; installing a jacketed pressure gauge on the steam pipeline of the extraction tank; installing a thermometer inside the extraction tank; installing a tuning fork switch on the filter of the extraction tank; and installing a flow meter after the pump on the variable frequency delivery pump of the extraction tank. The two solid content detection instruments transmit the detected data to a central processing unit (CPU). The jacketed pressure gauge, thermometer, and flow meter transmit the collected data to the CPU. The CPU analyzes the collected data to generate a trend curve of solid content change over time. This invention belongs to the field of traditional Chinese medicine extraction technology.

The invention relates to the technical field of quality management systems, and particularly discloses a textile dyeing production quality management system based on multi-source data monitoring, and the system comprises a critical recognition module which constructs a continuous airflow field model, and recognizes a vortex core area in combination with dynamic modal decomposition; the migration analysis module maps the vortex core area to a dye concentration gradient field and identifies vortex-dominated linkage; the migration analysis module constructs an airflow-dye coupling transfer function in a coupling manner, quantifies migration intensity and obtains a migration intensity index; the inter-cluster extraction module extracts chromatic aberration surging clusters and quantifies features based on the migration intensity index; and the convergence control module judges a control effect through a convergence regression model to realize dynamic convergence regulation and control. According to the invention, multi-source data and a physical model are integrated, full-process intelligent monitoring from migration risk early warning, driving mechanism identification to active control is realized, and the dyeing uniformity and production stability of textiles are improved.

The invention relates to the technical field of sponge titanium production processes, in particular to a sponge titanium production process anomaly automatic analysis and disposal method based on an MES (Manufacturing Execution System), which comprises the following steps of: firstly, integrating DCS (Distributed Control System), LIMS (Laser Information Management System) and quality management system data through the MES, and collecting key process parameters and quality data to form a complete data chain; establishing a dynamic early warning model, and combining SPC, machine learning and ARIMA models to realize abnormal real-time early warning and trend prediction; abnormal root causes are automatically analyzed by means of an AI model, a report is generated, and treatment measures are accurately pushed through a graph database knowledge base; and finally, monitoring-diagnosis-disposal-knowledge precipitation closed-loop management is formed. Advanced prevention and control of production abnormity are achieved, diagnosis accuracy and disposal efficiency are improved, repeated occurrence of quality defects and similar problems is reduced, and the intelligent level and operation efficiency of titanium sponge production are remarkably improved.

This invention presents a rapid method for detecting moisture in raw materials and fuels. Addressing the problems of low efficiency, long cycle time, large human error, and significant data feedback delays associated with existing methods using manual oven drying in centralized laboratory testing, this invention provides a systematic solution for real-time, automated moisture detection at the physical testing end. The core of this method lies in the following: after raw materials are sampled upon arrival at the plant, moisture samples and analytical samples are separated. The moisture sample is directly placed in a multi-station fully automated moisture analyzer, where weighing, drying, and calculation are automatically completed. The test results are automatically uploaded to the enterprise's quality management system in real-time via a data interface for immediate production use. Simultaneously, the analytical sample is sent to the laboratory for testing according to the original process. This invention, through a combination of equipment automation and process reconfiguration, achieves on-site, parallel, automated, and real-time moisture detection, significantly improving detection efficiency and accuracy, and providing timely and reliable data support for stable blast furnace production and cost control.