683 results about "Production control" patented technology

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 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 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 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 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 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 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 relates to the technical field of power system network and information security, and discloses a power system transverse isolation and secure data transmission method and system.The method comprises the steps that a production control security domain and a management information security domain are determined, and cross-domain service flow information is configured; when transmission between a production control security domain and a management information security domain is needed, cross-domain service flow information to which service data belongs is collected and packaged into a security self-description data unit; on the transverse isolation path, acquiring header information to generate a session token; when the secure self-description data unit is forwarded, feature data are obtained and compared with the associated session token, and forwarding is carried out when limiting conditions are met; and maintaining a sliding time window, carrying out statistics on the forwarded security self-description data unit, and identifying an abnormal behavior. According to the invention, semantic-level access control and dynamic protection of cross-domain services between a production control security domain and a management information security domain are realized.

The invention discloses a production control management system for ultrathin stones, which mainly realizes intelligent management and control of the whole production process of the ultrathin stones and solves the problems of unstable quality and low efficiency in traditional production. The system comprises eight core units, namely a process parameter configuration unit, a grinding control unit, a fusion layer formation control unit, a thickness monitoring unit, a standing curing monitoring unit, a quality detection unit, a data recording and tracing unit and a central control unit, and the units are electrically connected and coordinated to construct a whole-process closed-loop management and control system. The system adopts technologies such as an intelligent algorithm, deep learning, machine learning, image recognition and big data analysis. A process parameter configuration unit generates an adaptive scheme in combination with raw material fluctuation; the grinding and fusion layer control unit corrects equipment parameters in real time and detects abnormity; the thickness monitoring and standing curing unit accurately regulates and controls the thickness and the curing environment; the quality detection unit is used for realizing defect classification and performance pre-judgment; and the data recording and tracing unit is associated with the whole-process data through the unique identifier.

The invention discloses a production control method and system based on multi-source data fusion and an intelligent algorithm, and relates to the technical field of automatic control. Multi-dimensional production state information is collected through a sensor, multi-source heterogeneous data is integrated into a data set in a unified format through a data fusion technology, and the multi-dimensional production state information is obtained; a long short-term memory network is used for trend analysis and prediction, a multi-objective optimization algorithm is used for calculating an optimal control parameter, multi-loop cooperative adjustment is realized, an adjustment instruction is issued through a distributed control protocol, a priority ranking algorithm is introduced to ensure system stability, and a game theory negotiation algorithm is used for realizing dynamic resource allocation among production units. The production scheduling plan is optimized through the genetic algorithm, continuous monitoring and optimization can be achieved, control parameters can be adjusted in a self-adaptive mode, the production efficiency and the resource utilization rate are effectively improved, and intelligent automatic control under the complex production environment is achieved.

The invention discloses an intelligent monitoring method and system for on-line production of a magic tape based on big data analysis, and the method comprises the following steps: carrying out the collection and generation of an on-line production time sequence data set of the magic tape, and carrying out the production reference calibration, and obtaining an on-line production standard database of the magic tape; obtaining production quality sensitive factor data, and carrying out recessive quality time sequence fluctuation analysis and production quality fluctuation tracing to generate a production quality fluctuation tracing chain of the magic tape; performing quality conduction evaluation and on-line production quality prediction of the hook and loop fastener on each link on the hook and loop fastener production line based on the hook and loop fastener production quality fluctuation traceability chain so as to generate on-line production quality trend prediction data of the hook and loop fastener; and performing intelligent regulation and control parameter matching based on the on-line production quality trend prediction data of the magic tape, generating a production dynamic adjustment strategy, and pushing the production dynamic adjustment strategy to a production control cloud platform to execute closed-loop monitoring of the on-line production quality of the magic tape. According to the invention, intelligent quality control of the whole production process of the magic tape can be realized.

The invention discloses an oil and gas field integrated dynamic simulation construction method and system based on artificial intelligence, and the method comprises the steps: S1, carrying out the space-time alignment and standardized cleaning of a multi-source heterogeneous data signal, and generating a fusion data signal with unified space-time; s2, a physical information embedded deep learning model architecture is constructed, the architecture receives the fusion data signal, and a mixed training target signal fusing data driving and mechanism driving is generated; s3, training the deep learning model by using a fusion data signal of a historical time sequence, and finally obtaining a convergent intelligent agent model; and S4, feeding the current oil and gas reservoir state parameters and the production control parameters into the intelligent agent model as input signals, and directly outputting a dynamic prediction signal. The oil and gas field integrated dynamic simulation construction method and system based on artificial intelligence can solve the problem that traditional oil and gas field numerical simulation calculation is low in efficiency.

The invention provides an electronic component intelligent production control method and system, electronic equipment and a storage medium, and relates to the technical field of electronic component intelligent manufacturing, and the method comprises the steps: synchronously collecting second-level power consumption data, temperature field distribution information and environment temperature and humidity data of a chip mounter, reflow soldering equipment and optical detection equipment; and fusing to generate multi-source time sequence stream data. And generating thermal imaging data based on temperature field distribution, identifying an abnormal power consumption region, extracting cross-equipment cycle correlation characteristics through expansion convolution operation in combination with multi-source time sequence flow data, and predicting future energy efficiency state change points. And decomposing the global energy efficiency target into chip mounter start-stop time sequence constraint, reflow soldering power parameter constraint and optical detection sampling frequency constraint by using a time sequence target cascading algorithm to realize multi-device coordinated regulation and control. According to the invention, the energy efficiency management level and the product yield stability of the electronic component production line are improved.

The invention relates to the technical field of machine vision detection, in particular to an intelligent production control method of a PCB (Printed Circuit Board). The method comprises the following steps of: shooting a sampling inspection board, extracting characters and fixed through hole characteristics, aligning the characters and the fixed through hole characteristics with a standard character board, mapping a silk-screen layer circuit to the standard board, and calculating the cross density of a character frame and a circuit; dividing the board into an upper area, a middle area and a lower area, screening characters with low cross density in each area as measurement identifiers, and calculating an offset vector and a vector mean value of each area; and then, according to the region weights and the vector mean values, evaluating an overall offset estimated value, if the overall offset estimated value exceeds a threshold value, generating an affine transformation matrix according to the vector mean values of the regions, obtaining continuously changing compensation parameters through smooth interpolation, and according to the affine transformation matrix, performing pre-deformation processing on the original silk-screen graph to generate a corrected silk-screen file to drive printing. According to the invention, automatic detection and intelligent compensation of character local offset are realized, and the printing alignment precision and the production quality are improved.

The invention discloses a cable production process quality monitoring system and method based on multi-source data fusion, and relates to the technical field of production process monitoring. According to the system, firstly, multi-dimensional heterogeneous data of cable surface defects and production working conditions are collected by means of a sensor network, the current production environment of the cable is dynamically adapted through timestamp alignment fusion and production process parameter self-adaptive calibration, and then the current production environment of the cable is dynamically adapted on the basis of defect feature data after self-adaptive calibration. According to the method, the coupling relevance between the cable defects and the process parameters is analyzed, a result is quantified, the linear and nonlinear relevance degree between the process parameters and the defects is determined, finally, a dynamic regulation and control instruction is generated based on the coupling relevance, adaptability verification is carried out, and accurate closed-loop control over the cable production process is achieved. Therefore, accurate fusion and dynamic optimization of multi-source heterogeneous data in the cable production process are realized, the missing detection rate and the false detection rate of small defects are effectively reduced, and the defect detection accuracy and the cooperative adaptability of production control links are improved.

Embodiments of the present disclosure provide an automatic production line for gluing and laminating, a method, a system and medium for gluing and laminating. The automatic production line includes at least one production line unit and a central control processor. The at least one production line unit includes a gluing unit, a laminating unit, a baking unit, a pressing unit, or an image acquisition unit. The central control processor is configured to: determine, based on an image data set, gluing quality data, wherein the image data set includes an original image data set and a gluing image data set; determine, based on the gluing quality data, at least one control parameter; generate, based on the at least one control parameter, a control instruction, and send the control instruction to the at least one production line unit to perform a production control.

The invention discloses a high-entropy alloy electrode production management method and system based on quality tracing, and belongs to the technical field of production management, and the method specifically comprises the steps: endowing an identifier for an electrode production batch, and collecting a technological parameter curve; dividing application scenes and storing data in a classified manner by associating batch identifiers with terminal product models; obtaining the statistical failure life of the terminal product and extracting related process curves to form an analysis sample set; quantitative fluctuation characteristics of the process curve are extracted, and correlation analysis is carried out on the quantitative fluctuation characteristics and corresponding batches of life; defining a process characteristic value interval in positive correlation with the long life in a specific scene and generating a corresponding production control parameter set; and matching an application scene according to the order product model, and calling a corresponding parameter set to execute directional production. According to the method, closed-loop tracing and correlation analysis between process data and terminal service performance are constructed, so that the performance suitability and the production control accuracy of the high-entropy alloy electrode in subdivision application are improved.

The invention relates to the technical field of PTA production control, and discloses a PTA mixed feeding control method and system. The system comprises a mechanism modeling module, a data driving prediction module, a dynamic fusion module, a rolling optimization module and an online learning module. The mechanism modeling module constructs a sub-model based on a PTA mixed reaction kinetic equation, and outputs a theoretical feeding constraint value; the data driving prediction module receives temperature, pressure and raw material flow data collected by the real-time sensor and outputs a real-time feeding prediction value. The dynamic fusion module performs weighted fusion on the two values to generate a fused feeding prediction value; the rolling optimization module generates a feeding parameter adjustment instruction through a rolling optimization algorithm. And when the deviation between the adjusted sensor data and the fused predicted value exceeds a preset threshold value, the online learning module performs online fine adjustment on the parameters of the data driving model. The system combines a reaction mechanism and real-time working condition optimization feeding control, reduces manual intervention, and adapts to a complex production environment.

The invention discloses racket burr detection processing equipment and method based on machine vision, and belongs to the technical field of intelligent production control. The equipment comprises a conveying system, a clamping mechanism, an image acquisition module, a control system and a burr removing device. The conveying system conveys the rackets to a station and the rackets are fixed by the clamping mechanism; the image acquisition module acquires a racket image, and the control system identifies a burr position and plans a path according to the image; and the multi-degree-of-freedom mechanical arm carries a rotary cutting tool, and burrs are removed according to the planned path. The problems that in the prior art, manual deburring is unstable in quality and low in efficiency, and products are prone to being damaged are solved. Through the combination of machine vision and the force-controlled mechanical arm, full-automatic closed-loop operation from recognition, positioning to processing is achieved, the detection precision and the product quality are effectively improved, and the device is suitable for automatic production of complex curved-surface products such as badminton rackets.

The invention discloses a conductive slurry production control method and system based on multi-parameter cooperative control, and relates to the field of conductive slurry production, and the method comprises the steps: obtaining rheological characteristic data; according to the rheological characteristic data, a zero crossing point time difference between the excitation signal and the response signal is extracted, and a rheological phase angle is calculated; whether the rheological phase angle is located in a preset optimal dissipation interval or not is judged; if yes, the intrinsic relaxation time of the slurry microstructure is determined, and compensation frequency is generated according to the intrinsic relaxation time; the compensation frequency is used as a perturbation signal to be superposed to a driving voltage waveform of a driving motor, so that the stirring blades generate high-frequency vibration; and acquiring the deviation direction of the rheological phase angle, and dynamically adjusting the speed ratio of the revolution speed of the revolution shaft to the rotation speed of the rotation shaft under the condition of keeping the total power unchanged, so as to lock the rheological phase angle in the optimal dissipation interval. According to the invention, the yield and reliability of the electronic product can be effectively guaranteed.

The invention relates to the technical field of lithium battery production control, and discloses a production control method for realizing efficient output of lithium battery monomers. The method comprises the following steps: obtaining production batch characteristic data of a lithium battery monomer, the production batch characteristic data comprising an electrode material ratio characteristic and an electrolyte composition characteristic, and extracting a corresponding process parameter configuration set based on the production batch characteristic data; on the basis of the process parameter configuration set, key control nodes in the lithium battery production process are identified, and the key control nodes comprise a material ratio adjusting node and a process parameter switching node; parameter deviation data in the production process are calculated according to the key control nodes, and the parameter deviation data comprise accumulated deviation in the same-batch production process and parameter jump values during cross-batch production; based on the parameter deviation data, a reusable process control parameter set is extracted, and the reusable process control parameter set comprises universal process parameters and special process parameters.

The invention relates to the technical field of PE lamination production control, and discloses a distributed control system for a high-speed PE lamination production line. A base material state monitoring module of the system collects surface tension parameters and thickness distribution data of a base material in real time, and a dynamic base material state matrix is generated in combination with the operation speed of the base material; the temperature field coordination module is used for matching a preset melt temperature gradient curve according to thickness distribution data in the dynamic base material state matrix and generating a partition temperature regulation and control instruction set; the extrusion pressure balance module analyzes the pressure fluctuation historical record of each extrusion section based on the partition temperature regulation and control instruction set, calculates the pressure compensation amount of adjacent sections and generates a pressure balance parameter table; the film layer fusion control module receives the compensation amount data, adjusts the rotating speed difference of the double-screw extruder and the opening degree parameter of a die head in combination with the real-time melt casting speed, and outputs a film layer fusion quality index; and the distributed execution module decomposes the film fusion quality index into independent control instructions and synchronously issues the independent control instructions to each execution unit.

The invention discloses a remote production control method based on an AI data center, and the method comprises the steps: importing a customer order from an ERP platform by the AI data center, determining a production task, and transmitting a production instruction to a laser cutting machine, a slitting machine and a welding machine according to the production task; the correction machine adjusts correction parameters within a correction range determined by a historical correction database according to the welded material detection data, the module value of the cutting adjustment vector, the module value of the splitting adjustment vector and the module value of the welding adjustment vector; and after each correction is completed, the AI data center updates the customer order and feeds back the customer order to the ERP platform and the remote client. According to the invention, through deep fusion of AI decision, equipment control and remote cooperation, efficient, accurate and adaptive intelligent manufacturing remote control is realized.

The invention provides an automobile part injection molding parameter control method and device, equipment and a medium. The method comprises the steps that feature extraction is conducted on nozzle pressure waveform data and a screw position sequence which are synchronously collected to generate a process feature set, V / P switching point calibration is conducted on the process feature set to generate optimized V / P switching point parameters, and injection molding speed optimization is conducted on the process feature set to generate optimized injection molding speed parameters. According to the optimized V / P switching point parameter and the optimized injection molding speed parameter, an injection molding simulation test is carried out to set an optimized dwell pressure parameter, and according to the optimized dwell pressure parameter, the optimized injection molding speed parameter and the optimized V / P switching point parameter, fluid-solid coupling simulation is carried out to generate an optimized mold clamping force parameter. And generating a production control adjustment instruction according to the nozzle pressure waveform data in the production process. By calibrating the V / P switching and injection speed and optimizing the pressure maintaining and mold clamping force, self-adaptive closed-loop optimization of process parameters is achieved, and the stability of the product size and appearance is guaranteed.

The invention is suitable for the technical field of automatic control, and provides an MPC heating furnace oxygen-enriched combustion control system parameter optimization method based on reinforcement learning, which comprises the following steps: step 1, production line data is acquired, and the acquisition of the production line data is mainly to acquire real data of an actual cement production control production line, including MV, OV and DV data; and step 2, obtaining a state space model, the obtaining of the state space model comprising establishing a model of converting a first-order transfer function into a state space, the MPC parameter adjustment is performed by using the prominent learning ability and adaptive ability of a reinforcement learning (RL) method, the dynamic adjustment according to the change of the system can be realized, and the control accuracy of the system is improved. And training the current oxygen-enriched combustion control algorithm according to the state information under the existing decision-making condition so as to output the expected furnace temperature, and evaluating the effectiveness of the control algorithm through the prepared evaluation standard.

The invention discloses a full-process control method and system for galvanization processing, and relates to the technical field of Internet of Things production control. The invention discloses a full-flow control system for galvanization processing. The full-flow control system comprises a data acquisition module, a micro-flow field observation module, a risk calculation module, a parameter decision module, a comprehensive optimization module and a data positioning module. According to the method, a physical information neural network containing electrochemical constraint and thermal-fluid conservation constraint is introduced into a CFD digital twinborn model, and assimilation is carried out on micro-flow field observation data, so that micro-flow field state and dynamic evolution prediction synchronous with a production line are obtained; on the premise of ensuring mass balance and energy balance, the physical consistency and precision of flow field reconstruction and prediction are improved, and credible flow field state representation is provided for defect risk assessment and control decision based on the micro-flow field.

The invention relates to the technical field of textile printing and dyeing quality detection and industrial automation control, in particular to a textile color difference detection method based on image detection, which comprises the following steps: acquiring a fabric design file and yarn spectral data of a to-be-detected textile, and constructing a noisy-point-free ideal reference image; generating a theoretical damaged simulation image containing preset defect features; constructing a real observation and theoretical deduction residual matrix; comparing the structural similarity value with a preset judgment threshold: when the structural similarity value is greater than the preset judgment threshold, judging that the textile has real chromatic aberration, and generating corresponding production regulation and control instruction data; otherwise, judging that the difference is derived from environmental noise, generating a defect-free confirmation signal and recording a detection log; according to the method, the problem of reference deviation caused by dependence on physical sample cloth in the traditional technology is solved, the influence of weaving flaws, stains or warp and weft density fluctuation possibly existing in the sample cloth on the detection result is eliminated, and the purity and objectivity of the detection reference are ensured.