1073 results about "Production control" patented technology

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 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 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 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 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 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 relates to the technical field of food industrial production, and discloses a food industrial production control method and system based on artificial intelligence, and the method comprises the steps: A, collecting the original attribute information of a to-be-processed raw material through a raw material data collection module before a food processing production line is started; b, in the food processing process, physical state parameters of the dough are obtained in real time; c, synchronously inputting the data obtained in the step A and the step B into a control system in which a multi-model fusion mechanism is constructed; d, the control system carries out dynamic recognition, difference comparison and trend evaluation on the current batch of raw materials and the process state, and process adjustment suggestions of corresponding stages are output based on feature weights learned in the training data; and E, converting the process adjustment suggestion into an instruction set, issuing the instruction set to a control executor, and integrating an advanced control method and system with intelligent prediction and adaptive control capabilities of multi-source information so as to realize the stabilization of the quality of the baked product.

The embodiment of the invention discloses a circuit board production control method and system based on data monitoring, and the method comprises the steps: collecting a real-time monitoring data set of a target circuit board production line, the real-time monitoring data set comprising a plurality of continuous time sequence process parameter monitoring data and equipment operation monitoring data; performing multi-dimensional time sequence feature extraction processing on the real-time monitoring data set, and generating a production time sequence state feature and a process fluctuation correlation feature of the target circuit board production line; calling a production decision model to perform state fusion processing on the production time sequence state characteristics and the process fluctuation correlation characteristics, and generating production control decision information of the target circuit board production line; and generating an equipment control instruction set according to the production control decision information, and transmitting the equipment control instruction set to an execution terminal of the target circuit board production line to realize production state adjustment operation. Therefore, accurate control over the circuit board production process can be achieved, and the stability and efficiency of production are improved.

The invention relates to the technical field of polyester production control, in particular to an intelligent control method and system for a PET short fiber traction-cutting production line based on a PLC, a storage medium and a program. According to the method, a dual-network architecture in which a high-speed bus and an information bus cooperate is constructed, ARX tension prediction, a Kalman observer, resonance notch, energy efficiency constraint model predictive control (e-MPC) and two-degree-of-freedom speed feed-forward compensation are fused, and high-precision tension control and resonance suppression of the traction-cut-off linkage system are achieved. The method supports redundancy switching and self-calibration of the tension sensor, has millisecond-level response and + / -1.5% steady-state fluctuation control capability, remarkably improves control stability and energy-saving level, and is suitable for intelligent upgrading of a high-performance PET short fiber continuous production line.

The invention relates to the technical field of phosphoric acid production control, solves the problem that in the prior art, accurate control over the whole process of phosphoric acid production cannot be achieved, and provides a multivariable model prediction control method and system for the whole process of phosphoric acid production. The method comprises the following steps: acquiring key process data of a plurality of process units in the whole phosphoric acid production process in real time; preprocessing the key process data to obtain real-time standardized data; establishing a soft measurement model according to a pre-collected historical standardized data set; inputting the real-time standardized data into the soft measurement model to obtain a soft measurement predicted value; constructing a multivariable dynamic model according to the historical standardized data set and the soft measurement predicted value; according to the multivariable dynamic model, in combination with a model prediction control algorithm, obtaining target set values of a plurality of preset operation variables; and according to the target set value, realizing control of the whole process of phosphoric acid production. According to the invention, accurate control of the whole process of phosphoric acid production can be realized.

The invention belongs to the field of production control, and particularly discloses a control method of a thick-wall optical product precision injection molding hot runner system, which comprises the following steps of: acquiring mold geometric data of local thickness distribution in a mold-containing cavity along a melt flowing direction, calculating theoretical cooling time constants of each region by combining material thermophysical parameters, and calculating the cooling time constant of each region; establishing a thickness-cooling rate mapping relation model; predicting the time when the melt reaches each area and the effective cooling time according to an injection time sequence; solving a temperature compensation value of a heating area corresponding to the hot runner by taking the minimum final temperature variance when the key area is ejected as a target; and finally, dynamically applying a compensation value according to a time sequence before the melt arrives, and withdrawing / adjusting after region filling is completed or a pressure maintaining stage is entered. The invention can balance the temperature field of the mold cavity, reduce the refractive index gradient and shrinkage defect, improve the optical performance and yield of the product, and is suitable for precise injection molding of the thick-wall optical product.

The invention relates to the technical field of data processing, and provides a lens hardening process adaptive optimization method and system based on data driving. The method comprises the following steps: collecting a plurality of process parameters in a lens hardening process in real time; calling a hardening process prediction model, performing lens hardness prediction based on the plurality of process parameters, and comparing the predicted lens hardness parameter with a preset hardness value to generate a predicted hardness deviation; performing adaptive optimization on the plurality of process parameters according to the predicted hardness deviation to generate optimized process parameters; and the optimized process parameters are fed back to a production control end of the lens hardening process, and regulation and control of the lens hardening process parameters are executed. The technical problems of uneven hardness and low production efficiency caused by unstable process parameters in the lens hardening process are solved, and the technical effects of ensuring the consistency of the lens hardness and improving the production efficiency and the product quality through real-time prediction and adaptive optimization of the process parameters are achieved.

The invention relates to the technical field of intelligent mine and environmental protection crossing, and discloses a mine development ecological data fusion and causal mining method based on a double-lineage knowledge graph. The method comprises the steps of constructing a double-pedigree domain ontology model comprising a production activity pedigree and an ecological response pedigree, carrying out multi-modal data space-time alignment and standardization, constructing a dynamic map reflecting the real-time state of a mine, predicting future ecological indexes by using a time sequence diagram neural network introduced with physical constraints, and carrying out real-time monitoring on the real-time state of the mine. And calculating the contribution degree of each production link to abnormity by utilizing anti-fact reasoning so as to lock a disaster-causing source, and generating a production regulation and control instruction according to a causal analysis result, and feeding back and executing the production regulation and control instruction. According to the method and the system, full-process traceability and accurate regulation and control of mine environment problems such as surface deformation and water and soil pollution are realized, so that the problems that data multi-source heterogeneous is difficult to fuse and the causal relationship is difficult to confirm in mine ecological environment monitoring are solved.

The invention belongs to the field of advanced inorganic non-metallic materials, and relates to a 3D special-shaped glass bending tempering forming method and system, which comprises the following steps: according to initial stress distribution data, partitioning a glass curved surface by adopting a k-means clustering algorithm, determining an independent temperature control parameter required by each partition, generating a multi-mode temperature field partitioning scheme, partition temperature control data is obtained; according to the stable temperature distribution data, a non-contact electromagnetic suspension technology is adopted to control the distance between the glass and the mold, electromagnetic field intensity distribution data is generated, the stress state of the glass is calculated through finite element analysis, and contact stress reduction data is obtained; and aiming at the optimized mechanical property data, integrating the temperature field, the deformation rate and the stress distribution data by adopting a data fusion algorithm, generating a process parameter database for bending and tempering forming of the 3D special-shaped glass, and determining final production control parameters.

The invention relates to the technical field of industrial production control, in particular to a polyurethane raw material preheating control method and system. The method comprises the following steps: acquiring tank body temperature, environment temperature, batch temperature rise, tank body structure and raw material liquid data, constructing a thermal resistance-thermal capacitance series-parallel model to represent system thermal inertia characteristics, then identifying an initial value drift resistance section, a main temperature rise section and a disturbance absorption section in a segmented manner, and implementing power back-stepping based on segmented data, so as to obtain the thermal inertia characteristic of the system. Dynamically generating a heating power track; and a heating track is replanned in combination with real-time change data, so that precise modeling and self-adaptive control of a temperature rise process are realized, and the temperature control precision and energy efficiency response capability of the preheating system under different raw material batches and different working conditions are effectively improved.

The invention discloses an intelligent optimization system based on big data predictive control for a granulating slag micro mill, and belongs to the technical field of mill production control, the intelligent optimization system is used for constructing a multi-level PID control structure, an outer ring PID controller generates an intermediate control quantity as a set value of an inner ring PID controller according to a deviation between an outer ring set value and an actual measurement value, and the inner ring PID controller generates an intermediate control quantity as a set value of the inner ring PID controller; the inner ring PID controller outputs a cascade control instruction according to the inner ring deviation; establishing a multivariable prediction model, predicting the change trend of the multivariable prediction model in a short period, and generating a feedforward compensation instruction before disturbance; obtaining a plurality of input variables and interference variables related to the operation state of the execution equipment, inputting the input variables and the interference variables into the trained neural network model, and outputting a neural network output instruction based on historical experience; and performing weighting processing on the cascade control instruction, the feed-forward compensation instruction and the neural network output instruction to generate a final control instruction so as to realize optimal control of the production process.

The invention relates to the technical field of production control, in particular to a control system and method for an insulating part production line, and the system is characterized in that a sensing module collects a temperature signal, a deformation monitoring signal and a viscosity state signal in real time; the main control module fuses the three paths of signals through time sequence alignment and space registration to generate a fusion process state matrix, and constructs and dynamically updates a first current shrinkage dynamic prediction model based on the fusion process state matrix in combination with material attributes and environmental parameters; predicting a spatial non-uniformity shrinkage distribution map of a subsequent forming stage by using the model; according to the atlas, a cooperative adjustment instruction set is calculated and generated through a reverse thermal coupling algorithm, and the instruction set comprises a temperature compensation instruction and an extrusion back pressure and transmission rate cooperative instruction; and the execution module regulates and controls the corresponding actuator in a linkage manner, and dynamically counteracts non-uniform contraction power caused by space temperature gradient difference.

The invention discloses an anomaly diagnosis method and system based on a chain grate-rotary kiln pellet roasting temperature prediction model and a storage. According to the method, process detection parameters such as the thickness of a material layer of a chain grate machine, the temperature of a smoke hood of a preheating section, the coal injection amount of a main burner and the temperature of a kiln head of a rotary kiln serve as input, the pellet roasting temperature is predicted in advance through a prediction model constructed based on an XGBoost algorithm, and then the contribution size and direction of all the parameters to roasting temperature abnormity are determined through SHAP value analysis. According to the method, advanced information can be provided for production regulation and control, the hysteresis quality of traditional actual measurement analysis is overcome, meanwhile, key influence factors causing temperature abnormity are rapidly positioned, and accurate regulation and control are guided. The abnormality diagnosis system developed based on the method can improve the pellet production stability, the product quality and the production efficiency, reduces the production cost and the energy consumption, and plays an important guiding role in the intelligent production of the chain grate-rotary kiln oxidized pellets in the ferrous metallurgy field.

The invention provides an air energy storage well coated sand layer alternating injection-production instability mode discrimination and evaluation method, which comprises the following steps of: 1, determining an initial state parameter of an evaluation object, and determining flow in experimental evaluation according to a similarity criterion; 2, collecting parameters of two ends of the coated sand filling layer in the experiment process, classifying alternating injection-production instability failure forms of the coated sand filling layer of the air energy storage well, and identifying instability failure modes; step 3, calculating each index according to the collected parameters; and 4, carrying out production regulation and control and coated sand filling layer evaluation according to an index calculation result. According to the air energy storage well coated sand layer alternating injection-production instability mode distinguishing and evaluating method, the stability of a coated sand filling layer in the experiment (production) process is evaluated through a simple method, the underground production state of the coated sand filling layer is simulated, instability mode distinguishing is carried out, and certain guiding significance is achieved for actual well safety production.

The invention relates to the field of industrial data detection, in particular to an industrial production data anomaly detection system, which comprises a production monitoring module used for detecting a production control difference parameter and a set drift parameter and determining a production data state of a time sequence training data set; the window analysis module is used for responding to the window evaluation condition to determine a window setting strategy of the time sequence training data set; the extraction execution module is used for performing training execution window extraction based on the window setting strategy determined by the window analysis module; the training execution module is used for training the anomaly analysis model based on the acquired training execution windows; and the anomaly evaluation module is used for carrying out reconstruction anomaly analysis on each window analysis data of the to-be-evaluated data to determine a weighted anomaly index of each window analysis data, and responding to the early warning evaluation condition to determine anomaly detection data, and the anomaly detection efficiency of the multivariate time series data is improved.

The invention relates to the technical field of numerical control machining production control, in particular to a numerical control machining dynamic scheduling method oriented to multi-machine collaboration, which comprises the following steps of: establishing a directed graph according to real-time production state data, and then obtaining an adjustment priority score of each machining device based on a preset graph neural network according to the directed graph; the plurality of processing devices are scheduled based on the directed graph and the adjustment priority score. Compared with the prior art, the dynamic state of the multi-machine collaborative processing system can be more comprehensively and accurately modeled by constructing the directed graph containing the processing equipment node features and the process relationship, an adjustment priority score calculation mechanism based on the graph neural network is introduced, the adjustment suitability of each equipment in an emergency can be intelligently evaluated, and the processing efficiency of the multi-machine collaborative processing system is improved. The quick response capability of the scheduling scheme to abnormal events is effectively improved, better scheduling performance and higher production efficiency are achieved, and the problem that in the prior art, a multi-machine-tool scheduling method is poor in flexibility is solved.

The invention relates to the technical field of easy-to-open cap production control, and discloses a beverage easy-to-open cap production line control system and method. A sensing monitoring module of the system collects a cover deformation sensing data flow and an assembly pressure sensing data flow in real time; the quality feature detection module performs geometric feature analysis on the basis of a preset cover deformation constraint condition in combination with the cover deformation sensing data stream to generate cover mass distribution feature information; the process decision-making module generates a primary process decision-making scheme based on an embedded process decision-making space and a process fitness constraint condition in combination with cover body mass distribution characteristic information and an assembly pressure sensing data flow; a trend prediction module performs production trend prediction on the two types of data to obtain predicted quality distribution characteristic information and predicted assembly pressure data; the process compensation module compensates and optimizes the primary process decision scheme based on the prediction data to generate an optimized process decision scheme; and the execution control module controls the punch forming mechanism based on the optimization scheme.

The invention discloses an intelligent cable production management and control method and system based on feature data analysis, and relates to the technical field of cable production management and control, and the method comprises the steps: firstly obtaining cable structure information to determine a type, and synchronously collecting historical production whole-process data of a corresponding type, including equipment operation, technological parameters and other data; key features are extracted from historical data, and a cable production quality index prediction model and a production equipment energy consumption prediction model are constructed; obtaining real-time production data, extracting key features, and inputting the key features into the model to obtain quality and energy consumption indexes; and finally, the production plan is dynamically adjusted based on the two types of indexes, and a set threshold value, multi-scene early warning and a plan maintaining or adjusting strategy are covered. The method has the advantages that intelligent control over the whole production process is achieved through data driving, the quality prediction accuracy can be improved, energy consumption management can be optimized, the production plan flexibility can be enhanced, and technical support is provided for efficient and accurate management of cable production.

The invention discloses a stainless steel thin-wall pipe bending forming production line control method and system. The method comprises the steps that state feature vectors are generated through data collection; establishing a hybrid network model, and optimizing hyper-parameters of the hybrid network model by using an OOA eagle optimization algorithm to obtain an improved hybrid network model; inputting the state feature vector into an improved hybrid network model, and outputting predicted values of the springback value, the wrinkling probability and the ovality which are about to occur in the current bending section; inputting the predicted value into a multi-objective optimization algorithm, and performing back calculation in real time to obtain an optimal compensation parameter set by taking minimization of springback, wrinkling risk and ovality as optimization objectives; and the compensation parameter set is transmitted to a physical execution unit for production line control including bending control, core rod control, clamping control and feeding control. And the first-pass yield of bending forming and the production control efficiency are improved.

The invention relates to the technical field of production control, and discloses a control method and system for waterproof material production, and the method comprises the steps: determining a mapping strategy, a traversal strategy or a model strategy based on the relation between all temperature change values and a standard temperature change value, when the mapping strategy is determined, determining a stirring rotating speed according to a temperature change value, and when the traversal strategy is determined, determining the stirring rotating speed; when the strategy is determined to be a traversal strategy, traversing the temperature influence chain in a historical influence set to determine the stirring speed, and when the strategy is determined to be a model strategy, performing fusion based on a sorting result to determine a temperature change influence value, determining the stirring speed based on the temperature change influence value, and determining a stirring load torque curve graph based on a collection time period; according to the relation between the minimum load torque and the load torque, the stirring rotating speed is determined to be adjusted or not adjusted, the stirring rotating speed is adjusted based on the stirring rotating speed adjusting factor, and production is completed according to the adjusted stirring rotating speed. According to the invention, the stability and reliability of waterproof material production are ensured.

The invention relates to the field of co-extrusion production control, and particularly discloses an efficient film bag feeding control system, which is applied to a multi-raw-material synchronous conveying device of a co-extrusion process, and comprises a full-link data acquisition module, a full-link data processing module and a full-link data processing module, the raw material characteristic parameters comprise a melting point T, a stacking density rho and a magnetic sensitive particle loading amount M; the instantaneous load pressure P of each raw material conveying channel, the temperature Tm of a co-extrusion die head melting area, the real-time thickness H of a co-extrusion film layer, stock bin liquid level data and motor rotating speed data are collected and integrated to obtain a full-link original data set; the single-drive linkage conveying mechanism comprises a main servo motor, a plurality of sets of electromagnetic clutches and a plurality of sets of spiral conveying assemblies, and the main servo motor drives a transmission shaft through a synchronous belt. According to the technical scheme, for the co-extrusion process, the multi-raw-material synchronous conveying precision can be matched, and the problems that parameter adjustment lags behind, adaptability is poor and a downstream process feedback closed loop is lacked are solved.

The invention provides a production control method and system based on SPC alarm linkage process locking, and relates to the technical field of quality management and control, and the method comprises the steps: deploying an SPC sensing node used for collecting technological parameters at a cutting edge position where a machining tool of production equipment is in contact with a workpiece, or at a contact point where a probe of online measurement equipment is in contact with the surface of the workpiece; the method comprises the following steps: acquiring process parameter data of a key production process in real time through an SPC sensing node deployed at a cutting edge position or a contact point; inputting the process parameter data as real-time data or small sample expansion data into an SPC judgment engine; and performing statistical process control analysis on the real-time data or the small sample expansion data through an SPC judgment engine so as to model data clusters which are densely distributed and have stable parameters in the data into a virtual reference curved surface representing a normal state domain. According to the invention, the accuracy of abnormity determination can be improved, and timely management and control of quality abnormity and minimization of production loss can be realized.

The invention discloses a black-gray production control agent training generation and processing method and device based on a large model, a medium and equipment. The invention belongs to the technical field of computers. The method comprises the steps of intelligent agent training: training task understanding ability, task planning ability, subtask execution ability and tool calling ability of an intelligent agent through a research and judgment report document and a settlement file document of historical case events related to black and grey production management to obtain a black and grey production management intelligent agent; collection of case event data: acquiring various data related to the current case event in a synchronous or asynchronous, real-time or non-real-time manner through a retrieval enhancement method; and intelligent agent execution: calling related tools to execute each subtask, and carrying out parameter transmission and fusion calculation to obtain an output result of the black and grey production control intelligent agent. By adopting the technical scheme, the efficiency and the accuracy of black ash production treatment can be improved, and the capability of coping with complicated and diversified black ash production problems is enhanced.

The invention discloses a high-elasticity antibacterial yarn production control method and system, and relates to the technical field of textile manufacturing. The method comprises the following steps: acquiring tension, temperature, antibacterial agent distribution and other multi-dimensional state information, and generating a comprehensive performance evaluation index based on a feature projection and gating residual fusion model; the index is compared with target performance, and key influence parameters such as fiber arrangement unevenness and antibacterial agent application distribution density are identified; calling a process response model to generate a parameter adjusting strategy, and driving an adjusting device to adjust process variables such as tension and temperature through a control instruction; and finally, based on a stochastic gradient descent algorithm and in combination with an expert knowledge base, adjusting rules are periodically updated, and performance self-adaptive control is achieved. According to the method, the intelligence and robustness of regulation and control of the elasticity and the antibacterial performance of the yarn are improved, and the method is suitable for industrial production of high-end textiles such as medical yarn and intelligent wearing fabric.