1762 results about "Production control" patented technology

The invention provides a battery film production automation control method and system based on visual inspection, and relates to the technical field of production control, and the method comprises the steps: collecting a film image, carrying out the fusion, generating a fused film image, carrying out the noise reduction, constructing a feature point matching relation, removing wrong point pairs, carrying out the geometric correction, carrying out the sub-pixel-level alignment, and obtaining a registration image; edge enhancement is carried out based on an improved Frangian filtering algorithm, an edge region is repaired and segmented to obtain a film edge contour, three-dimensional reconstruction is carried out, three-dimensional point cloud coordinates are solved, a film three-dimensional contour model is reconstructed, a film defect detection model is constructed, and a film defect distribution diagram and defect confidence are obtained through forward propagation; performing association analysis with a production process, constructing a feature mapping model, generating a defect knowledge base and matching a process parameter optimization strategy, generating a dynamic instruction and sending the dynamic instruction to a station control unit, monitoring operation data, performing closed-loop feedback control, and obtaining a current optimal control scheme.

The invention provides a new energy automobile control circuit board production process optimization control method and system, and relates to the technical field of production control, and the method comprises the steps: collecting production process parameters in real time, inputting the parameters into a process digital twin model, and obtaining process deviation data; and inputting the deviation data into a process parameter optimization model based on a deep reinforcement learning algorithm to generate an adjustment instruction, performing real-time parameter adjustment on the production line, and feeding back to a digital twinborn model to form closed-loop control. According to the invention, accurate monitoring and intelligent optimization of the production process can be realized, the production quality and the production efficiency of the circuit board are improved, and the rate of defective products is reduced.

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 relates to the technical field of cable production automation control, and provides a crosslinked cable production control method based on an improved PID (Proportion Integration Differentiation) control algorithm, which comprises the step of constructing an optimal reference model by establishing a cooperative control framework of temperature, pressure and traction speed and taking the temperature of a vulcanization pipe as a main control variable and the pressure and the traction speed of an extruder as slave control variables. A main controller tracks a temperature set value in real time, PID parameters are dynamically set according to technological parameters such as the thickness deviation of an insulating layer and the crosslinking degree of a material, and precise control is achieved through a three-ring cooperative coupling improved PID controller. A temperature control ring enables the steady-state error of the actual temperature and the set value to be converged to zero. The pressure and speed control loop converges the fluctuation amplitude to a process allowable range. The temperature stability, the pressure uniformity and the traction synchronism of crosslinked cable production can be improved, the process fluctuation and the defect rate are reduced, and the product quality and the production efficiency are improved.

The invention relates to the field of wastewater treatment, and provides an industrial wastewater membrane process intelligent optimization method and system based on multi-dimensional data modeling. The method comprises the following steps: acquiring wastewater quality parameters of a target scene and a production line equipment state in real time through a multi-dimensional sensor network, and constructing a digital twinborn model of a wastewater treatment system in the target scene; the membrane surface pollution type of each node in the wastewater treatment system is identified through a digital twin model, and the inner membrane pollution evolution condition in the future preset duration is predicted; a multi-strategy evolutionary reinforcement learning algorithm is adopted, the wastewater quality parameters, the production line equipment state and the membrane pollution prediction result are combined, multi-target dynamic optimization is conducted on a wastewater treatment system through a multi-agent collaborative decision-making algorithm, an optimization strategy is generated based on the multi-target dynamic optimization result, and membrane process operation parameters and production control parameters are dynamically adjusted. According to the method, real-time intelligent adjustment of membrane process operation can be realized, the stability of membrane process operation is guaranteed, and the treatment efficiency of membrane process operation is improved.

The invention provides a production process intelligent monitoring method and system based on an intelligent mine, and the method comprises the steps: collecting a target monitoring data set in the real-time production process of mine equipment, covering equipment vibration time sequence signals, environment temperature and humidity distribution data and an equipment energy consumption fluctuation curve, and carrying out the dynamic standardization processing, the method comprises the following steps: obtaining a standardized monitoring data set matched with an equipment type and a production process stage, calling a pre-trained multi-dimensional feature extraction model to carry out joint feature mapping, generating an equipment operation state, environment association and data abnormal fluctuation features, and carrying out dynamic fusion analysis on the features based on a preset abnormal mode identification model, and finally, according to the risk level, an alarm instruction is triggered, an optimization strategy is fed back to a production control terminal to adjust equipment operation parameters, and intelligent monitoring and optimization of the intelligent mine production process are realized.

The invention provides a management method and system for coal preparation quality intelligent control, and the method comprises the steps: firstly obtaining raw material quality, equipment operation and environment state index sets of a target production line, then carrying out the dynamic association processing of the index sets, and generating a technological process feature set; then calling a preset optimization decision model to carry out parameter prediction on the technological process feature set to obtain a process regulation parameter set, generating a quality optimization instruction set based on the process regulation parameter set, transmitting the quality optimization instruction set to a production control unit to execute dynamic regulation operation, and finally, according to a quality feedback index set after regulation execution, carrying out dynamic regulation operation. And the parameter weight distribution of the optimization decision model is updated, intelligent control and continuous optimization of the coal dressing quality are realized, and the stability of coal dressing production and the product quality are improved.

The invention relates to the technical field of production process control, in particular to a PVC developing functional granule production control method and system, and the method comprises the steps: calling a spectrometer to capture spectral data emitted from PVC granules in real time based on the starting of a production line, analyzing the intensity and wavelength distribution in the spectral data in real time, and generating spectral data characteristic values; and comparing and analyzing the deviation of historical data and real-time data through the spectral data characteristic value, and generating a deviation dynamic prediction analysis result. According to the invention, by monitoring and analyzing the spectral data of the PVC granules in real time, the response speed and precision of a production line are improved, and the spectral data are compared in real time, so that production deviations such as light source intensity and reaction temperature can be quickly found and adjusted, material waste is reduced, the output quality is improved, and continuous data analysis helps to adjust chemical agent feeding and heating power; product consistency is guaranteed, automatic fault diagnosis is achieved, production parameters are adjusted in time, the service life of equipment is effectively prolonged, shutdown is reduced, and cost benefits are increased.

The invention provides a graphene antibacterial polyester yarn production control platform based on intelligent algorithm optimization, and relates to the technical field of automation control, and the graphene antibacterial polyester yarn production control platform comprises the steps that a quality control analysis module obtains a production operation parameter set of graphene antibacterial polyester yarns for quality control analysis, and sets product performance indexes; the optimization model building module is used for building an objective function and root and building a multi-objective optimization model; the process optimization module performs process optimization by using a multi-objective optimization model to obtain production optimization parameters; the operation change analysis module performs operation control monitoring based on the production optimization parameters and draws a production parameter change trend chart; the product quality prediction module performs product quality prediction through reinforcement learning; the control strategy making module makes a production control strategy based on the product quality prediction data set. The technical problems of product quality fluctuation and cost control difficulty caused by lack of real-time accurate adjustment means for production parameters in the prior art are solved, and the production cost is reduced while the product quality is improved.

The invention provides a cold-rolled strip steel plate shape closed-loop control method and system based on dynamic feedback, and belongs to the technical field of cold-rolled strip steel production.The method comprises the steps that a dynamic incidence matrix and a plate shape deviation signal are generated based on cold-rolled strip steel transverse measuring point data, processing the dynamic incidence matrix to obtain a space correlation defect feature vector of the cold-rolled strip steel, and processing the strip shape deviation signal to obtain a multi-dimensional defect feature vector; determining the strip shape defect type of the cold-rolled strip steel based on the multi-dimensional defect feature vector and the space correlation defect feature vector; and strip steel production control parameters are adjusted based on the strip shape defect type. The plate shape quality stability of the cold-rolled strip steel can be improved.

The invention relates to the technical field of image processing, and discloses a graphite composite insulation board production process control method and system and a storage medium. The control method comprises the following steps: carrying out characteristic analysis and proportion optimization on raw materials to obtain pretreated raw materials; and monitoring the reaction state of the pretreated raw material, regulating process parameters, and generating a modified raw material. And performing formula optimization and performance prediction based on the material parameters of the modified raw material to obtain an optimized formula material. And forming process parameters of the optimized formula material are analyzed, a forming process is regulated and controlled, and a forming material is generated. And the curing process is optimized through the state parameters of the forming material, and a cured product is formed. And finally, evaluating the quality according to the detection data of the cured product, and generating a production control scheme. By establishing a multi-dimensional data acquisition and processing system, intelligent control over the whole production process is achieved, and therefore the stability and consistency of product quality are improved.

The invention relates to the technical field of visual management and control, in particular to an essential oil production digital visual management and control system and method based on data analysis. The method comprises the following steps: obtaining essential oil raw material data and carrying out component content identification, removing abnormal values to generate standard raw material components, obtaining historical process data, matching the standard raw material components according to the data, obtaining a reference process, carrying out production simulation to generate essential oil parameters, and carrying out multi-dimensional quality evaluation on simulation process parameters. The method comprises the following steps: optimizing a matching reference process, forming a quality compensation essential oil process, carrying out scene simulation on the process, evaluating process risks, implementing safety compensation treatment, obtaining a safety compensation essential oil process, carrying out production control adjustment on standard raw material components based on the process, carrying out visual rendering, and constructing a digital visual view of essential oil production. According to the invention, a more efficient and safer digital visual management and control method for essential oil production is realized.

The invention relates to the technical field of grease production control, in particular to a grease product production control method and system based on intelligent digitization. The method comprises the following steps: monitoring temperature, viscosity and turbidity data in a grease crystallization process in real time, and generating grease crystallization state snapshot data; performing crystallization state prediction according to the grease crystallization state snapshot data, and performing reaction kettle cooling rate adjustment to obtain a target control cooling rate; performing real-time stirring rotating speed adjustment fuzzy reasoning on the grease crystallization state snapshot data to generate a corrected grease stirring speed; and performing control instruction conversion on the target control cooling rate and the corrected grease stirring speed, executing a production control instruction, and generating a grease crystallization process control report. By accurately controlling the cooling rate and the stirring speed, intelligent control over the grease crystallization process is achieved, and the quality and stability of grease products are remarkably improved.

The invention discloses a straw feed fermentation treatment control system and method, particularly relates to the technical field of production control, and is used for solving the problem of insufficient identification and regulation of an imbalance phenomenon in a straw fermentation pile caused by non-uniform gas diffusion in the existing fermentation process. The method comprises the following steps: acquiring environmental parameters of a fermentation heap, performing three-dimensional gas concentration field reconstruction processing, identifying a layered air mass region in combination with a fuzzy clustering algorithm, measuring heap pressure distribution and deformation characteristics, analyzing a heap body structure abnormal region, and marking a risk analysis region; through a three-dimensional tensor model and a fractal analysis model, the time dependency and the gas diffusion equilibrium degree of the gas exchange and metabolism process are evaluated respectively, and finally fermentation pile structure parameters and ventilation device operation parameters are dynamically adjusted based on a closed-loop control system; through accurate region identification and a dynamic regulation and control mechanism, the uniformity of the fermentation process and the quality consistency of the straw feed are remarkably improved.

The invention belongs to the technical field of optical fiber sensing analysis, and particularly relates to an intelligent self-adaptive environment multi-parameter passive optical fiber sensing analysis system which comprises an optical fiber sensor array module, an intelligent signal processing module, a self-adaptive adjustment control module, a comprehensive monitoring alarm module and a remote control center. By fusing an optical fiber sensing technology and an intelligent algorithm, real-time monitoring and accurate analysis of environmental changes are realized, and the system has the advantages of high transmission rate and electromagnetic interference resistance, has wide application prospects in the fields of environmental monitoring, industrial production control and the like, and is suitable for popularization and application. The environment management and control difficulty in the detection period is analyzed through the environment management and control analysis module, and the inspection performance of the optical fiber sensor array module in the detection period is analyzed through the inspection influence analysis module when the environment control high-difficulty signal is generated, so that whether the influence caused by the inspection performance factor is eliminated or not is judged; management personnel can reasonably carry out follow-up management scheme planning, and the intelligent degree is high.

The invention provides a production control method and system based on a digital factory, and the method comprises the steps: collecting the real-time operation data of production equipment in multiple regions of the factory, and carrying out the standardized fusion processing, and obtaining a standardized data flow; according to the standardized data flow, in combination with a preset digital factory knowledge graph, analyzing an association relationship among the production elements to obtain key influence factors; based on the key influence factors in combination with a preset multi-dimensional cost analysis model, optimally allocating production tasks to obtain a production control instruction; and issuing the production control instruction to the production equipment of the corresponding area, monitoring the production parameter change of each area, and dynamically adjusting the production control instruction according to the production parameter change. According to the method, through standardized fusion and knowledge graph analysis of multi-region data, accurate identification of production element association relationships and extraction of key influence factors are realized, the resource utilization efficiency is improved based on optimal allocation of a multi-dimensional cost model, and a dynamic monitoring and adjusting mechanism enhances the response capability to changes.

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention provides a washing composition production regulation and control method and system based on multi-parameter intelligent analysis, and relates to the technical field of production control, and the method comprises the steps: collecting process parameters to form a data flow, inputting the data into a cascade self-coding deep learning network to construct a dynamic association map, grouping parameters based on the map, and calculating collaborative change characteristics, a layered progressive intelligent controller is constructed to generate an optimization regulation and control strategy, adaptive parameter adjustment and closed-loop optimization control are realized, the problem that a traditional regulation and control method is difficult to cope with multi-parameter collaborative change is solved, and the production process stability and the product quality consistency are improved.

The invention relates to the technical field of peanut oil production control, in particular to an intelligent optimization system for a peanut oil production control process, which comprises a process parameter modeling module for collecting peanut oil production parameters, performing linear combination on the peanut oil production parameters, analyzing the correlation of the peanut oil production parameters to quality indexes, and calculating the quality indexes of the peanut oil production parameters; and obtaining a process parameter mapping matrix. According to the method, through deep correlation analysis of multiple parameters in the production process, the mapping relation between the process parameters and the oil quality indexes is established, and the precision and reliability of quality control are improved. In the process control, the specific influence of each production link on the quality index is accurately reflected through standardized and linear combinatorial analysis on the parameters such as squeezing temperature, pressure, time, stirring speed and the like. By predicting the quality index under the current production condition, the response speed to the quality change is improved, so that the quality index deviation can be quickly analyzed and evaluated in real-time data monitoring.

The invention discloses an automatic control method and system for water-based gravure ink production, and relates to the technical field of production control. An automatic control system for water-based gravure ink production comprises a formula acquisition module, a raw material configuration module, a dynamic adjustment module, a performance detection module, a finished product detection module and a record optimization module. The production batches in the production schedule are automatically obtained, the corresponding ink formula is matched, it is guaranteed that the raw material demand quantity of each production batch is accurately calculated, human errors are reduced through automatic configuration and accurate metering of the raw materials, and high efficiency and consistency of production are guaranteed; the temperature and the viscosity are monitored in real time in the mixing process, and the stirring time and the stirring power are dynamically adjusted according to the standard curve, so that the mixing process is optimized, the stability and the consistency of product performance are ensured, and meanwhile, the production period is shortened.

The invention discloses an intelligent operation and maintenance control platform and system for a secondary power system, belonging to field of power system operation and maintenance technology. The platform includes a interval layer, a transmission layer, a station control layer, a system layer, a platform layer, an application layer, and a disaster recovery backup centre. By collecting data from a security system and external information system, and sharing data with other application platforms of a secondary system, problems of omission, repetition, and isolation in current data collection are solved. Balancing the safety of production control area and the needs of advanced application development in information management area, ultimately achieving comprehensive data collection and deep mining, making the production and operation business of the power secondary system more smooth, meeting the professional needs of various professional operators, and achieving the improvement and efficiency of the secondary system.

The invention discloses an electric toothbrush head plastic mold forming control device and method, and relates to the technical field of mold forming control. The device comprises the steps that the production requirement of an electric toothbrush head is obtained, plastic mold design is conducted, and brush head plastic mold design parameters are obtained; according to the brush head plastic mold design parameters, a plastic mold production line is activated, and a target brush head plastic mold is obtained; constructing a brush head molding process control space to obtain a brush head injection molding process solution set; carrying out traversal search optimization by taking the production demand of the brush head of the electric toothbrush as a constraint parameter, and outputting a forming process control parameter; and performing a brush head pre-forming test to obtain brush head forming test parameters, and performing brush head production optimization control. The technical problem that in the prior art, the product quality and the production efficiency are low in the whole brush head production process is solved, and the technical effects that precise control and optimization from the production requirement to the whole brush head production process are achieved, and the product quality and the production efficiency are improved are achieved.

The invention provides a stamping production control method based on big data monitoring, and relates to the technical field of stamping control, and the method comprises the steps: obtaining a stamping production control instruction through a stamping production management module; setting a stamping production loss factor; performing loss prediction on the stamping production control scheme according to the stamping production loss factor to obtain a stamping loss prediction result; the prediction result is input into a stamping loss verification module, and a stamping loss verification result is generated; when the verification result is not passed, a stamping loss optimization module is activated, the production control scheme is optimized in combination with product requirements, and a stamping production control optimization result is obtained; and based on the punching production monitoring regulation and control module, punching control is performed according to the control optimization result, the technical problems of insufficient data utilization, slow response speed and insufficient optimization capability in punching production in the prior art are solved, and the purposes of improving the punching production efficiency and the product quality through big data monitoring and optimization control and improving the product quality are achieved. And the production cost is reduced.

The invention provides a coal-bed gas well production dynamic analysis method and system based on AI time sequence prediction, and the method comprises the steps: firstly collecting a production dynamic data sequence of a coal-bed gas well, a corresponding well position geological condition data sequence and a surrounding environment data sequence, and constructing a ternary time sequence dynamic association network comprising node dynamic time sequence segments and a connection line real-time association relationship; and then calling a pre-trained AI time sequence coupling deviation evolution mining model to process the ternary time sequence dynamic association network to obtain a ternary time sequence coupling deviation result. And a deviation conduction closed-loop path is determined according to a ternary time sequence coupling deviation result in combination with forward conduction positioning and reverse tracing verification, and a deviation conduction analysis report is generated. And finally, generating a dynamic regulation strategy based on the deviation conduction analysis report in combination with a production dynamic prediction threshold value, outputting the dynamic regulation strategy to a production control terminal, and meanwhile, feeding back regulation data to update a network node association relationship, thereby realizing accurate analysis and regulation of the production dynamic state of the coal-bed gas well.

The invention discloses a multi-group film winding tension cooperative control system, which belongs to the technical field of plastic film production control, and comprises a plurality of groups of parallel winding devices, each winding device is provided with a motor, a winding wheel group, a belt, a speed sensor, a laser range finder, a data acquisition module, a tension calculation module, a speed adjustment module and a control execution module, the tension calculation module is used for acquiring corrected winding tension characteristics according to the film winding tension information, and the speed adjustment module is used for adjusting the winding speed according to the tension information and the historical tension information and outputting the corrected winding speed; through comprehensive consideration of multiple factors, the tension calculation accuracy is improved, cooperative control over the tension of multiple sets of films is achieved, and the purposes of dynamically adjusting the winding speed and then improving the system stability are achieved.

The invention provides a continuous annealing strip steel performance multi-index prediction method based on multi-objective evolution deep learning, and relates to the technical field of data processing in the cold rolling production control process of iron and steel enterprises. A multi-objective evolutionary algorithm is applied to hyper-parameter selection of a deep learning model, so that the precision of the online detection method is further improved; meanwhile, a training set is obtained in a random selection mode, the optimal hyper-parameter combination of the deep learning model can be rapidly obtained through the search strategy of the training set in the hyper-parameter space, and finally the generalization ability of the online detection model is improved. According to the method provided by the invention, the defects of a traditional deep neural network model based on artificial experience are overcome, simultaneous prediction of multiple performance indexes is realized, autonomous optimization construction of a deep learning model and multi-index prediction of strip steel performance are realized, and the cold rolling production can be helped to improve the product quality control level.

The invention discloses a mixed emulsion explosive one-key production control method and system, and relates to the field of automatic and intelligent control. By means of automatic and intelligent control means, the problems of low efficiency, high cost, quality fluctuation, process uncertainty and the like in traditional mixed emulsion explosive production are solved, accurate feeding of raw materials is achieved through a quality sensor array and a PID control algorithm, and the production efficiency is improved. The stability of production parameters is ensured through multi-sensor data fusion and an advanced control algorithm, so that the consistency of product quality is improved; meanwhile, a feedback control loop and a continuous learning mechanism are established, closed-loop control in the production process is achieved, technological parameters are optimized, the production stability and reliability are ensured, the production efficiency and the product quality are remarkably improved, and the labor cost is reduced.

The invention provides a controllable SCADA (supervisory control and data acquisition) industrial production control platform based on an industrial internet architecture. The controllable SCADA industrial production control platform comprises a field layer, an edge layer, a communication layer, a cloud / fog computing layer and a data management layer, wherein the field layer is composed of a sensor array, an execution mechanism, a PLC controller and an RTU terminal; the sensor array comprises a temperature sensor, a pressure sensor, a flow sensor and a position sensor, and adopts 4-20mA current loop signal output; the execution mechanism comprises an electric valve, a servo motor and a variable frequency driver, and communicates with the PLC through a Modbus RTU protocol; the PLC controller is provided with a dual-core processor and is provided with 16 digital quantity input / output channels and 8 analog quantity input channels; the RTU terminal is internally provided with a LoRa wireless communication module; the edge layer comprises an edge computing node and a protocol conversion gateway; the edge computing node is provided with an FPGA coprocessor and supports an edge reasoning framework; and multi-protocol conversion is realized through the protocol conversion gateway.

The present invention provides a production control method and system for an intelligent clothing industry based on data analysis, which relates to the field of intelligent manufacturing technology, and includes: setting an intelligent data acquisition platform, collecting heterogeneous data for common feature extraction, generating a comprehensive data set, and constructing a digital twin model for clothing production; performing data extraction based on the comprehensive data set, generating triples and constructing a clothing production knowledge graph, constructing a clothing production data analysis model, extracting key features and determining implicit association rules, generating a cross-modal knowledge graph, identifying clothing production control rules, and solving clothing production control parameters; constructing a decision evaluation model to parse clothing production control parameters into production control decisions, perform evaluation, dynamically correct based on the evaluation results, obtain optimized production control decisions, send them to equipment terminals, adaptively optimize clothing production, collect real-time production data, update the clothing production digital twin model and the clothing production knowledge graph, and periodically optimize until the production task is completed.