330results about "Registering/indicating quality control systems" patented technology

The invention provides a precision injection mold accessory production quality traceability management method and system, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: collecting material, process, equipment and environment data in real time through a distributed sensor; fusing multi-source data based on dynamic material characteristic parameters and process stability indexes, and quantifying melt flow and process fluctuation characteristics; constructing a mixed kernel function anomaly detection model to identify quality deviation; establishing a cross-process association map to reveal the space-time relationship among the raw materials, the process and the finished product; generating a three-dimensional traceability identifier containing the material hash, the process compression code and the block chain address; a block chain enhanced database is adopted to realize tamper-proof storage; and generating a visual traceability report through reverse analysis. Through dynamic modeling, cross-process association and block chain technologies, the problems of data isolation, detection lag and low traceability credibility in a traditional method are solved, the quality traceability efficiency and precision are remarkably improved, and precise injection molding full life cycle management is supported.

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 belongs to the technical field of film quality analysis and evaluation, and particularly relates to a flexible circuit board film pressing quality detection and analysis method and system. The method comprises the steps of generating a self-adaptive scanning path based on a design file and surface deformation data; performing multi-modal data acquisition and fusion along a path through optical surface scanning, ultrasonic tomography and an optical coherence tomography imaging assembly; potential defects are positioned, and size, shape, position and defect type probability values are quantized; judging the authenticity of the defect by adopting a three-stage mechanism of cross-modal probability comparison, physical characteristic logic verification and multi-frame time sequence verification; and performing weighted scoring on the real defect matching acceptance standard table, and performing disqualification marking or outputting a process optimization suggestion to the pressing equipment according to a scoring result. According to the invention, the imaging limitation of the transparent coating layer is broken through, the internal interface layering and the precise detection of the microbubbles are realized, and the closed-loop control from detection to production adjustment is formed.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention provides a glass processing dynamic monitoring method and system based on multi-source data fusion, and relates to the field of industrial artificial intelligence, the method comprises the following steps: continuously collecting sound signals generated during processing, and carrying out noise reduction processing on the obtained sound signals to obtain sound data after noise reduction; continuously monitoring the acoustic data after noise reduction, acquiring abnormal acoustic characteristics of the glass in the processing process, determining an accurate time point of the abnormal acoustic characteristics, and acquiring time information of the abnormal acoustic characteristics; acquiring a glass processing position at the time of the abnormal acoustic features according to the time information of the abnormal acoustic features, and acquiring image or video information of a glass processing area within the time range; according to the obtained abnormal acoustic characteristics and the image or video information of the glass processing area, whether defect information appears in the glass processing process or not is judged, so that real-time dynamic monitoring and defect accurate positioning can be realized in the glass processing process, and specific process parameter adjustment suggestions are generated.

The invention provides a filling process prediction method and device based on a discrete element method and data driving, and relates to the technical field of bulk material forming, and the method combines the physical modeling advantage of the discrete element method and the powerful prediction capability of a data driving method. The filling process of the granular material under different process parameters is simulated through a discrete element method, key quality indexes are obtained to serve as input of a data driving model, and the process parameters serve as output for training. The model not only can accurately predict the filling quality, but also can adjust the process parameters in real time according to the target quality index, so that the intelligent optimization of the filling process is realized. According to the method, the calculation efficiency is greatly improved, the generalization ability of the model is enhanced, the dependence on empirical data is reduced, a more efficient and accurate solution is provided for the filling process in the industries of building materials, pharmacy, powder metallurgy and the like, the product quality is improved, the production cost is reduced, and the research, development and application of novel materials and equipment are accelerated.

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention relates to the technical field of double-sided adhesive tape production, and discloses a double-sided adhesive tape processing method, which comprises the following steps: obtaining base material raw paper and an adhesive raw material, processing to form a pretreatment material set, and outputting an initial process parameter configuration table; generating a processing flow update instruction set according to the dynamic adjustment signal, and determining target processing parameters through hierarchical disassembly classification; and outputting a targeted process execution scheme in combination with the equipment operation information set. According to the method, a multi-dimensional process vector matching historical database is constructed by extracting raw paper gram weight characteristics, adhesive viscosity characteristics and drying temperature characteristics, and accurate parameter configuration is realized. And potential defects are further identified based on a real-time monitoring data curve, the parameter priority is dynamically adjusted, and the optimization process is judged through statistics and time sequence stability. The method solves the problems of parameter curing, defect response lag and insufficient equipment collaboration in a traditional method, and improves the production efficiency and quality stability of the double-sided tape.

The invention relates to the technical field of injection molding production process monitoring, and discloses an injection molding production monitoring method, device and system.The injection molding production monitoring method comprises the steps that multi-source sensing data is collected, and pressure time sequence data and material spectrum data in the injection molding process are collected; extracting pressure waveform features to obtain a complete pressure waveform feature set; performing material characteristic analysis, and forming a material characteristic fingerprint in combination with the pressure waveform characteristics and the spectral data; material batch similarity calculation: constructing a material batch fingerprint database, and calculating the similarity between the new batch of materials and the historical batch of materials; process parameter optimization: continuously improving the accuracy of parameter optimization through multi-objective tradeoff optimization and parameter verification and iterative optimization; quality problem root causes are analyzed, and material problems and process parameter problems are accurately distinguished; through fusion of pressure waveform analysis and a material characteristic monitoring technology, comprehensive monitoring and intelligent regulation and control of an injection molding production process are realized.

The invention relates to the technical field of image processing, in particular to an intelligent detection method and system for support turning. The method comprises the steps that sensor signals of the support in the turning machining process are obtained in real time; constructing a sensor topological graph; extracting real-time time sequence process features reflecting the dynamic response of the tool-workpiece-machine tool system in the sensor topological graph; inputting the real-time time sequence process features into a trained multi-task prediction model, and outputting prediction deviation values of a plurality of geometric dimensions of the support and a prediction surface defect mask pattern; and if all the predicted deviation values are within the corresponding tolerance threshold values and the ratio of the defect area of the predicted surface defect mask pattern to the total area of the bracket is smaller than a set area threshold value, judging that the expected quality state of the bracket is qualified. According to the scheme, the expected quality state of the support product can be comprehensively judged in real time.

The invention relates to a multi-dimensional aging test data analysis system and method, and aims to realize real-time acquisition and intelligent analysis of multiple performance indexes in the aging process of electronic products. The system comprises an information acquisition module, a test scheduling module, a data processing module, an analysis module, an abnormity judgment module, a report module and a communication module. Cooperative work among the modules is realized based on task driving and data circulation, accurate association of test data is realized through bar code binding, and unified analysis is performed in combination with multi-source information such as USB read-write performance, network transmission state and display resolution. The system realizes multi-task concurrent acquisition through a resource-aware scheduling strategy, and the analysis module identifies performance abnormity and outputs a structured report by combining preprocessing, feature extraction and time sequence prediction. According to the method, the test automation level and the anomaly recognition accuracy are improved.

The invention relates to the technical field of intelligent manufacturing and industrial digital twinning, in particular to a latex paint packaging full-process digital twinning method and system. The method comprises the following steps: collecting multi-source data in real time through a sensor network, and preprocessing to form a standard data stream; a coupled digital twinborn model fusing material characteristics and process parameters is constructed, and real-time prediction is realized by combining physical laws and data driving residual correction and online parameter self-adaption; constructing a multi-scale abnormal index by comparing model output with actual data, identifying an abnormal mode by adopting PCA, calculating local and overall health indexes, and predicting a trend; and generating a preliminary control strategy according to the abnormal mode and the health trend, forming closed-loop adaptive control in combination with health state weighted optimization and look-ahead adjustment, and issuing execution and feeding back an update strategy. According to the invention, the quality control qualification rate and the energy-saving efficiency of the whole process of emulsion paint packaging are improved.

The invention relates to the technical field of carbon fiber mold optimization, and discloses a carbon fiber box mold optimization method and system based on multiple targets. A multi-target definition library of the system stores a design constraint set composed of a geometric accuracy threshold value, a material strength threshold value and thermal deformation tolerance; the real-time sensing network collects data of layering tension distribution, temperature gradient and resin flow front edge; the topological dependency analyzer constructs a dependency relationship graph of the design parameters and the real-time sensing data; the material characteristic library stores carbon fiber layering parameters and resin curing kinetic parameters; the optimization calculation scheduler dynamically allocates multi-target optimization calculation task nodes; an iterative optimization engine generates a layering path sequence and curing temperature curve combination scheme; and the result verifier compares the combination scheme with the design constraint, and outputs a scheme deviation index. According to the system, the design standardization, the production monitoring comprehensiveness and the optimization verification accuracy of the carbon fiber box mold can be improved, and the product quality and the production stability are guaranteed.

The invention discloses a dynamic optimization method and system for a boron diffusion process of a photovoltaic cell and electronic equipment. Comprising the following steps: collecting process parameters and sheet resistance data, and associating the process parameters and the sheet resistance data to form a process parameter set; optimizing a process parameter set, removing noise data and incomplete data, and extracting an available feature data set; dividing the feature data set into a training set and a verification set, and training to obtain a nonlinear regression model for predicting resistance data under different feature data; carrying out validity verification on the nonlinear regression model, and constructing an optimization function to reduce a difference value between a predicted value and an actual value; and deploying a nonlinear regression model in the diffusion process, adjusting the process parameters of the diffusion process in real time according to the predicted value, and optimizing the obtained sheet resistance data. The problem that the boron diffusion process is difficult to accurately adjust by a traditional process control method can be solved.

The invention discloses an optimization method and system for a 42CrMo steel heat treatment process based on digital twinning, and the method comprises the steps: obtaining historical data of 42CrMo steel in each stage of the heat treatment process, and constructing a corresponding digital twinning model based on each cooling rate, deploying all the digital twin models at specified positions in a preset virtual space; acquiring a real-time data packet of the 42CrMo steel in the heat treatment process, and dividing the real-time data packet into a plurality of sub-data packets according to different cooling rates; determining a target time sequence identifier corresponding to each sub-data packet according to the cooling rate, and transmitting the target time sequence identifier to a corresponding target digital twin model; the target digital twin model runs the sub-data packets and generates corresponding optimization information; and detecting the current visual field range of the user, and guiding the user to adjust the visual field until the target digital twinborn model can be seen. By means of the digital twinborn model, accurate control over the cooling rate can be achieved, and therefore the abrasion resistance and hardness of the 42CrMo steel are optimized.

The invention provides a glass production line cold end stress detection system and method. Belongs to the technical field of glass production detection. The method comprises the following steps: carrying out stress-related spectral data acquisition on a cold end area of the glass production line through a multispectral stress sensing array, and preprocessing the acquired spectral data to obtain a preprocessed spectral data set; spectral feature extraction is carried out on the preprocessed spectral data set, and a spectral feature vector set is constructed; based on the spectral feature vector set, utilizing a space-time correlation analysis algorithm to extract space-time features of glass cold end stress to obtain a stress space-time feature matrix; spectral data are collected through the multispectral stress sensing array, the influence of traditional contact type detection on glass products and a production line is avoided, and the detection precision and the real-time performance are improved.

The invention discloses a fruit juice beverage production monitoring and early warning method and device, and relates to the technical field of food production, and the method comprises the steps: carrying out the correlation division of production nodes on a fruit juice beverage production line, and obtaining a heat and oxidation abnormal correlation node set; configuring a temperature sensor array at each thermal association node to monitor real-time temperature-time data and generate a first early warning signal; and monitoring dissolved oxygen data, a material turbulence state and pipeline pressure at each oxidation associated node, establishing an oxidation intake map sequence, predicting the total oxidation intake and distribution uniformity, and analyzing the influence of oxidation on quality indexes to generate a second early warning signal. The technical problems that in the fruit juice beverage production process, heat imbalance can damage raw material nutrition and affect the sterilization effect, and oxidation can cause color and luster deterioration, taste decline and shelf life shortening are solved, and accurate monitoring, abnormity analysis and early warning of quality related key factors in the fruit juice beverage production process are achieved; and the stable product quality is effectively guaranteed.

The invention discloses an MES-based aluminum alloy casting production management method and system, and relates to the technical field of production management, and the method comprises the steps: firstly, synchronously collecting time sequence data streams of core process parameters such as pressure, speed, position and the like, and carrying out the multi-channel data alignment; and a deep learning auto-encoder model is introduced to carry out nonlinear dimension reduction and feature extraction on high-dimensional data, and reconstruction errors are utilized to realize real-time evaluation and anomaly detection of the quality of the casting process. And for the abnormal state exceeding the preset threshold value, key influence factors causing quality fluctuation are further traced through an intelligent diagnosis mechanism, autonomous decision is made based on a diagnosis result, an optimal parameter adjustment strategy is formed, and finally a closed-loop control instruction is generated to drive the production equipment to perform adaptive adjustment. In this way, the problem of unstable quality caused by parameter fluctuation in the casting process can be effectively solved, and the stability and the intelligent level of the aluminum alloy casting process are remarkably improved.

The invention discloses a coating quality prediction and optimization method and system based on multi-modal data fusion, and the method comprises the steps: S1, fusing coating parameters, environment data, equipment states and historical quality data, and generating a global feature matrix; s2, inputting the matrix into a GBDT model containing physical prior constraints, and outputting a quality risk probability and a key index; s3, target parameters are generated through a genetic algorithm, Q-Learningg and Pareto screening; s4, performing closed-loop feedback on updated data; s5, cross-line adaptation is achieved through transfer learning and domain adaptation; and S6, iteratively training the model in batches. The system comprises a data fusion module, a quality prediction module, a parameter optimization module, a closed-loop control module, an overline adaptation module and a self-evolution module. The method aims at solving the problems that traditional coating overline parameters are difficult to reuse, small sample modeling cost is high, prediction is separated from process logic, and parameter adjustment lags, so that overline adaptability and prediction stability are improved, small sample cost and energy consumption are reduced, and the method is suitable for high-end coating scenes such as automobiles and aviation.

The invention discloses a distribution transformer load loss prediction method and system based on pilot-scale test monitoring data. The method comprises the following steps: collecting pilot-scale test data of a distribution transformer load loss related variable; preprocessing the collected pilot plant test data, and performing correlation analysis on the preprocessed variable data to obtain an independent variable in a distribution transformer finished product load loss prediction function; performing multiple regression analysis on the selected independent variables to obtain a regression model, correcting the regression model to obtain a distribution transformer finished product load loss prediction function, and verifying the load loss prediction function; and performing load loss prediction on the distribution transformer finished product based on the verified load loss prediction function. According to the method, the predicted value of the load loss can be quickly obtained by analyzing pilot test data, the time and the cost of a traditional experiment are reduced, and the calculated load loss prediction precision can be effectively improved by analyzing variables and verifying functions.

The invention provides a correlation analysis method for technological parameters in a wafer processing process and related equipment. The correlation analysis method comprises the following steps: determining target parameters needing correlation analysis in the wafer processing process; according to the target parameter, acquiring all other process parameters belonging to the same wafer batch as the target parameter and corresponding parameter values in the data warehouse, and packaging into a data set; inputting the data set into a machine learning algorithm model, and calculating the correlation between the target parameter and other process parameters through a machine learning algorithm; and determining key process parameters influencing the target parameters according to a correlation calculation result. The correlation between the target parameter and other process parameters is automatically calculated and identified through a machine learning algorithm. In this way, the key process parameters influencing the target parameters can be effectively screened out, objective data support and decision basis are provided for the process defect problem caused by the common influence of multiple factors, and the difficulty of fault analysis and problem positioning is reduced.

The invention discloses an intelligent monitoring method and device for the operation process of a reaction kettle based on visual analysis. The method comprises the following steps: acquiring a video sequence in a reaction kettle in a current period collected by a 5G explosion-proof camera deployed in the reaction kettle according to a preset period, so as to obtain a video sequence in the kettle to be analyzed; inputting the video sequence in the kettle to be analyzed into the chemical product visual analysis model for fuzzy video processing, and extracting product visual information of the current period to obtain the product visual information; determining whether the time distribution condition of the process operation nodes in the current period is consistent with the time distribution condition in preset process operation node information or not to obtain an analysis result; and if not, corresponding operation process prompt information is created and sent to the client. By implementing the method, the accurate control of the production process and the product quality stability can be effectively improved, and the method is particularly suitable for the production requirements of high-end chemical products.

The invention relates to the technical field of glass bottle detection, and discloses a glass bottle bottom defect detection method which comprises the following steps: building a detection platform, and adopting a multi-view imaging module of an annular LED and oblique light supplement combined light source, a vertical camera and 3-4 oblique cameras; after the to-be-detected glass bottle is positioned, synchronously collecting a bottle bottom full-view image set; graying, adaptive median filtering, CLAHE histogram equalization and Otsu binarization preprocessing are carried out on the image; extracting geometric and textural features of the image, inputting the geometric and textural features into an improved YOLOv5 model for identification and classification, and judging defects by combining with multi-view result fusion; and sorting the glass bottles according to a detection result and generating a traceable report. The identification rate of tiny defects is larger than or equal to 99%, the detection time of a single bottle is smaller than or equal to 0.5 second, and the method is compatible with glass bottles with the diameter of 30-100 mm, is suitable for large-scale quality control in the fields of food and beverage, medicine packaging and the like, and has extremely high practical value.

The invention discloses a tea making monitoring method and system based on a knowledge graph, and the method comprises the steps: integrating variety characteristics, processing technology parameters, equipment operation states and environmental factors into a structured data set in a unified format through a data fusion technology, and constructing a processing technology knowledge graph through a graph representation learning model; the method comprises the following steps: representing correlation between process links and parameters by nodes and edges, calculating a parameter deviation vector in real time, triggering cross-link influence propagation simulation, identifying a potential influence path, extracting an equipment related node sequence, analyzing an abnormal fluctuation mode, generating an abnormal index set, tracing the contribution degree of a preorder link parameter to abnormity, and generating a parameter adjustment vector; and finally, the optimized sequence output of the machining process parameters is realized. According to the invention, through fusion of the knowledge graph and the abnormity traceability, the problem of dynamic optimization of the processing parameters is solved, and the precise control capability of tea processing and the efficiency of equipment abnormity processing are significantly improved.

The invention discloses a manufacturing process optimization method and system based on multi-agent autonomous collaboration, and the method is applied to a central coordinator, and comprises the steps: transmitting a received production demand to a task planning agent for the task planning agent to carry out the analysis and decomposition, and forming task information; receiving task information, wherein the task information comprises sub-tasks after the task planning intelligent agent analyzes and decomposes the production demand; the sub-tasks are sent to a plurality of intelligent agents, so that the intelligent agents perform bidding according to self capability evaluation functions and cost estimation to generate bidding information; and bidding information is received, the bidding information comprises a self-ability evaluation function and cost estimation of each agent for each sub-task, the sub-tasks are allocated to the multiple agents according to a total execution cost minimization principle, and a production plan is generated and executed. According to the method provided by the invention, planning and execution of the production task can be autonomously completed in a complex manufacturing scene, and the production efficiency and the product quality are improved.

The invention belongs to the technical field of instrument board production, and discloses an instrument board tubular beam assembly integrated welding quality control and detection system which comprises the following steps: S1, design stage optimization and simulation prediction; s2, the welding process is precisely controlled; s3, material and equipment collaborative management; the design stage optimization and simulation prediction comprises structure topology optimization, weld joint layout and a welding deformation prediction model, and the welding process precise control comprises a low-heat input welding process and a dynamic parameter adjustment system. Through structural optimization and intelligent process regulation and control, welding deformation and residual stress are remarkably restrained, and high-precision forming of light-weight materials is ensured. A dynamic reverse compensation mechanism and a low-heat input welding technology cooperate to effectively avoid air holes, cracks and other defects, and the strength and fatigue life of the connector are improved. A multi-dimensional material pretreatment and equipment calibration system breaks through the reliability bottleneck of dissimilar material interface bonding, and provides guarantee for stable production under complex working conditions.

The invention provides a laser additive manufacturing defect monitoring method, system, medium, equipment and device, and belongs to the technical field of laser additive manufacturing defect recognition. A laser additive manufacturing defect monitoring method comprises the steps that X-ray image information and sound signal information in the melting process of a laser powder bed are obtained; preprocessing the X-ray image information and the sound signal information to obtain preprocessed X-ray image information and sound signal information; marking the preprocessed X-ray image information and sound signal information to construct a data set; inputting the preprocessed X-ray image information and sound signal information into a multi-modal teacher model for training so as to obtain defect identification and positioning capability based on multi-modal information; inputting the preprocessed sound signal information into a single-mode student model to obtain a trained single-mode student model; and migrating the defect identification and positioning capability based on the multi-modal information to the trained single-modal student model through a knowledge distillation strategy.

The invention discloses a port AR intelligent point inspection method, system and device, and the method comprises the steps: collecting multi-source inspection data, predicting the operation fault risk of port equipment, and generating an inspection task; the priority of the inspection task is determined, the inspection function and the inspection capability of the inspection personnel are combined, matching of the inspection personnel and the inspection task is completed, and an inspection work order is generated; generating an inspection point track and an inspection point item execution list according to each inspection work order, and displaying the inspection point track and the inspection point item execution list by using an AR inspection device; monitoring the execution process of the inspection task in real time by using the AR inspection device, and generating a cooperative task when a predefined cooperative condition is triggered; and calculating the priority of each cooperative task, completing redistribution of the cooperative tasks and adding the cooperative tasks to the inspection work order of the corresponding inspection personnel in combination with inspection personnel resources and expert resources, obtaining an inspection result of each inspection point, uploading the inspection result to a port background server, and analyzing and generating an inspection report. According to the invention, standardization and intellectualization of port equipment inspection work can be realized.

The invention provides a traditional Chinese medicine decoction piece quality evaluation method based on big data, and relates to the technical field of data processing, and the method comprises the steps: extracting texture direction consistency data from texture dense region detection points of a central region, and extracting structural symmetry data from wrinkle region detection points; comparing the color gradient change data with a preset standard color gradient threshold to generate a first difference index, comparing the texture direction consistency data with a historical qualified batch texture direction distribution range to generate a second difference index, and comparing the structural symmetry data with a pharmacopoeia form standard to generate a third difference index; and calculating a quality evaluation value based on the first difference index, the second difference index and the third difference index in combination with the initial quality deviation risk value, and obtaining a quality grade judgment result of the traditional Chinese medicine decoction pieces according to an interval matching relationship between the quality evaluation value and a preset risk threshold. According to the invention, the evaluation accuracy can be improved.

According to the intelligent recognition and state monitoring method and system for the central axis of the complex wire harness structure, the axis feature database is established in advance, the axis in the complex wire harness is scanned and compared through the machine vision technology, identity recognition is achieved, working data collected in real time are combined, the state change trend of the axis is judged through the time sequence analysis algorithm, and the state of the complex wire harness structure is monitored. When the trend is abnormal, an embedded sensor is used for obtaining temperature vibration data, the position and the type of an abnormal axis are determined through a data fusion technology, historical production data are further combined, clustering analysis is adopted for judging the relevance between abnormity and a production link, and the signal quality is optimized based on real-time monitoring data. According to the invention, the block chain technology is fused to carry out anti-counterfeiting verification, and the long-term operation stability is analyzed through the prediction model. Finally, the deep learning technology is utilized to carry out iterative training on identification and monitoring precision, intelligent management of the axis in a high-density connection scene is realized, and the reliability and production efficiency of a complex wire harness structure are improved.