2812 results about "Process control" patented technology

The invention relates to the technical field of process control, and particularly discloses an unmanned aerial vehicle real-time control system and method based on 5G network and perception fusion. The system comprises an airborne sensing and communication module, an edge collaborative decision center and a global resource management and task planning platform, and dynamic collaboration of sensing data fusion, real-time decision and resource scheduling is realized through a multi-layer closed-loop control framework, so that the trajectory tracking precision and the system response agility of an unmanned aerial vehicle group are improved, and the system performance is improved. And the utilization efficiency of limited wireless resources is optimized.

The invention discloses a beneficiation process intelligent optimization control method and system based on a knowledge graph, and relates to the technical field of beneficiation process control, and the method comprises the steps: collecting static attribute data and dynamic operation data, constructing a causal knowledge graph, and obtaining a causal relationship between nodes in the causal knowledge graph and an optimization target constraint; based on a causal relationship between nodes in the causal knowledge graph and optimization target constraints, screening data from a historical database to form a high-value historical data set; according to the high-value historical data set, a reward function model is constructed, a strategy function is trained, and a reinforcement learning control strategy is obtained; running the reinforcement learning control strategy, generating a control instruction, and issuing the control instruction to the target equipment to obtain dynamic optimization control; and performing real-time monitoring on the dynamic optimization control, and performing anti-fact reasoning based on a causal knowledge graph when abnormal deviation of the running state is detected to obtain an anti-fact reasoning result.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.

The invention relates to a production abnormality automatic identification and recovery process control method, which comprises the following steps of S1, realizing second-level synchronization of multi-source heterogeneous data, constructing a real-time data flow pipeline and generating a total-factor production situation data flow through a distributed message queue by adopting a Modbus / TCP protocol analysis algorithm based on industrial Internet of Things edge calculation; through the cooperative effect of industrial protocol analysis and distributed message queues, second-level synchronization of multi-source heterogeneous data is realized, a real-time data flow pipeline is constructed, data acquisition delay is effectively eliminated, the timeliness of anomaly detection is ensured, a dynamic weight distribution mechanism of a rule engine and a long and short-term memory network prediction model is adopted, and the real-time performance of the system is improved. By combining sliding window threshold detection, the accuracy and coverage of anomaly recognition are improved, false alarm and missing alarm caused by a single detection mechanism are reduced, and multi-dimensional root cause tracing is performed by combining a fault mode knowledge base through combined application of time sequence correlation analysis and a causal diagram inference engine.

The invention discloses a non-point source pollution emergency bypass decision control method, particularly relates to the technical field of automation and process control, and aims to solve the problems that existing scheduling cannot align multi-source measurement and evaluate credibility in minutes under a unified time base, and is lack of risk measurement integrated with a tide level phase and a moisture regain path and same-layer gating. The bypass rhythm is easy to misjudge, and the oscillation is difficult to audit. A minute-level risk density is constructed through multi-source alignment and credibility grading under a unified time reference, injected tide level phases and moisture regaining paths, bypass rhythms are constrained by same-layer gating and tide windows, and beam search, boundary arbitration and fixed window closed-loop correction are matched, so that the risk density is improved. Therefore, under the superposition of rainstorm and tide, decision making and execution are stably limited in a non-backflow red line, bearing and equipment limit, misjudgment and oscillation are remarkably reduced, the problems of unavailability and difficult auditing caused by multi-source asynchronization and moisture regain coupling are solved, intelligent decision support is provided for non-point source pollution treatment, and it is ensured that the treatment technology is accurate and efficient.

The invention relates to the technical field of project intelligent management and control, and discloses a multi-agent power grid project intelligent monitoring, management and control and evaluation system and method, and the system comprises a mixed data resource library, a multi-agent system, an index calculation module and a process control module. The mixed data resource library stores structured business data and unstructured documents; the multi-agent system comprises an information extraction agent, an index design agent, a code generation and treatment agent and the like, works cooperatively, and converts a high-level natural language management and control rule into an executable code; the index calculation module is responsible for executing codes according to a scheduling strategy and calculating quantitative indexes; and the process control module automatically identifies risks and executes management and control operations such as process locking according to the index result and a preset threshold value. According to the invention, the complex business logic can be automated and coded, the accuracy and timeliness of risk identification are improved, and refined and prospective intelligent management and control of the power grid project are realized.

The invention relates to the technical field of industrial automation process control, and discloses a data source adaptation and control binding method, device, equipment and medium, comprising: establishing a variable model to generate a variable directory, constructing a data source adapter to generate a data source mapping table and a read-write interface set, generating a control tree in a visual control designer, establishing a binding relationship between control attributes and variables, loading a variable binding engine, analyzing a binding path, generating an execution plan, registering change monitoring, accessing a read-write interface set of a target data source adapter based on the execution plan to complete variable read-write, and generating an execution record, and responding to a variable change event or a control input event to trigger control value write-back and interface refreshing, and finally collecting a variable directory, a binding relationship, a control tree and an execution record to generate a package configuration file. Through a unified variable model and control binding mechanism, cross-data-source analysis and bidirectional linkage are realized, and the interaction efficiency and the system maintainability are improved.

The invention relates to cable manufacturing process optimization, and discloses a cable conductor process parameter optimization method and device. The method comprises a multi-source data acquisition step, a parameter decision step and a process control step. According to the multi-source data acquisition, a multi-source data signal is formed through data fusion; in the parameter decision-making stage, performance prediction, defect risk signals, optimal process parameters and safety process thresholds are output through sub-steps of prediction, optimization and regulation; and in the process control stage, dynamic parameter adjustment, equipment cooperative control and risk comprehensive early warning are realized through sub-steps of adaptive execution, equipment group control and interactive early warning. According to the method, through closed-loop control of multi-source data driving and intelligent decision making, precise optimization of cable conductor process parameters is achieved, the adaptability and stability of the production process are enhanced, the defect occurrence rate is effectively reduced, and the product quality consistency is improved.

The invention relates to the technical field of industrial process control, and discloses an intelligent optimization control method of an alkaline water hydrogen production system, which comprises the following steps: a controller synchronously acquires real-time voltage, current and temperature data of an electrolytic bath; theoretical reversible voltage is calculated according to a Nernst equation, ohm voltage drop is eliminated, and a bubble additional impedance value representing the bubble coverage degree is analyzed; performing differential processing on the change rate of the bubble additional impedance value to generate a flow feed-forward control instruction, combining a feedback control instruction based on temperature deviation to synthesize a target rotating speed instruction, and driving an alkali liquor circulating pump to adjust the flow speed; according to the method, the bubble impedance observation model is constructed, so that zero-delay sensing and decoupling control over the state of bubbles in the electrolytic cell are achieved, hysteresis of single temperature feedback is eliminated, and the problems of actuator saturation and model aging drifting are solved.

The invention relates to the technical field of industrial process control, and discloses a denitration intelligent monitoring method and system based on data analysis and a storage medium. The method comprises the following steps: performing working condition identification by using an improved K-means algorithm to obtain stable operation, fire change preparation, fire change proceeding and fire change recovery modes; matching a segmented control strategy according to an identification result, and carrying out weighted fusion on feedforward and predictive control in a fire exchange preparation stage to generate a differentiated liquid ammonia regulation instruction; the disturbance feed-forward compensation amount is calculated in combination with the combustion state of the kiln, the adjusting instruction is restrained, and a liquid ammonia control instruction is obtained; the execution effect is monitored in real time through standardized deviation detection, manual intervention is switched in case of abnormity, and control parameters are fed back and updated in case of normal. The problems that NOx control lags behind and liquid ammonia consumption is not economical under the fire exchange working condition are solved, and the control precision and the self-adaptive capacity of the denitration system are improved.

The invention discloses a spectrum aliasing decoupling and concentration inversion method under the cross influence of multi-source environmental factors, belongs to the field of industrial process control and environment monitoring, and constructs an environment-spectrum collaborative fusion decoupling model for concentration prediction. The method specifically comprises the following steps: respectively collecting absorption spectrum signals of specified mixed gas at different temperatures, pressures and known concentrations, meanwhile, collecting environmental parameter data, constructing a multi-source data set, and carrying out denoising, dimension reduction and preprocessing on the multi-source data set; constructing a self-supervised feature extraction network for adaptive modulation of environmental parameters to realize deep fusion of spectrum and environmental information; the feature expression capability and generalization performance of the self-supervised feature extraction network are improved by using a self-supervised learning mechanism; and constructing a BPBO-GRNN self-adaptive concentration inversion optimization model for realizing inversion of mixed gas concentration and self-adaptive optimization of model parameters. According to the invention, high-precision concentration inversion and stable detection of the aliasing gas can be realized.

The invention discloses a welding robot self-adaptive wire feeding control method, a platform and a medium, relates to the technical field of welding automation process control, and is suitable for welding process management under complex working conditions. According to the method, an arc voltage sequence and a molten pool characteristic parameter sequence in the welding process are collected, a welding state characteristic value is obtained through calculation, a reference database containing welding process parameters, welding working condition parameters and the welding state characteristic value is constructed based on historical experimental data, and corresponding preset reference indexes are generated through a mapping relation function; and in real-time welding, the welding state characteristic value of the current wire feeding control period is obtained and compared with a preset reference index, the deviation value is calculated, the welding state is judged according to the grading threshold value interval, the wire feeding speed is dynamically adjusted, and self-adaptive matching of the wire feeding speed and the welding state is achieved. According to the method, instability caused by traditional adjustment depending on experience can be overcome, dynamic control over the welding process is achieved, and the stability of a welding pool and the welding seam forming quality are improved.

The invention relates to an abnormity alarm method and system for a feed production control system, and the method comprises the following steps: collecting multi-field data related in a feed production process in real time, the multi-field data at least comprising production equipment operation parameters, material characteristic parameters, environmental parameters and process control parameters; preprocessing the collected multi-field data, and fusing to generate a multi-field description feature vector representing the current production state; inputting the multi-field description feature vector into a pre-trained fault prediction model, wherein the fault prediction model respectively calculates the confidence of the fault type prediction result and the confidence of the fault point prediction result; wherein the confidence coefficient is in positive correlation with the distance from the feature vector to a known fault mode clustering center or the distance from the feature vector to a decision boundary; and when the fault prediction result indicates that an abnormal risk exists and the corresponding confidence exceeds a preset threshold, triggering a corresponding alarm signal.

The invention discloses a mold injection molding full-process automation system based on layered cooperative control, which comprises a central control layer, a module scheduling layer and an equipment execution layer, and clock synchronization among the layers is realized through an industrial Ethernet. The central control layer comprises a dynamic scheduling server and a digital twinborn verification module, the module scheduling layer comprises a mold intelligent scheduling module, an injection molding process control module and a product quality closed loop module, and the equipment execution layer comprises a manipulator cooperation unit and a sprue material processing unit. By constructing a three-layer cooperative control architecture of a central control layer, a module scheduling layer and an equipment execution layer and combining a modular hardware carrier and a cross-layer data closed loop, common industrial problems such as cooperative level splitting, mold control marginalization and abnormal response delay are systematically solved; and finally, the comprehensive benefits that the die changing efficiency is remarkably improved, the product reject ratio is greatly reduced, and the energy consumption is obviously reduced are achieved.

The invention discloses a thermal spraying coating thickness online optimization control method based on digital twinning, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: synchronously collecting multi-modal online observation data and process control observation data in a thermal spraying process, carrying out the time-space alignment, and generating a multi-modal observation set; performing adaptive optimization of multi-modal uncertainty gating on the candidate control set according to the credible thickness field confidence distribution map to generate a control instruction sequence; and the control instruction sequence acts on the thermal spraying process, response feedback data are continuously collected in the execution period, a new multi-mode observation set is generated according to the response feedback data, and real-time closed-loop optimization control is kept. According to the method, the thickness field with high credibility and the credible thickness field confidence distribution diagram can be generated through forward generation and uncertainty quantification of physical guidance, and depth coupling of multi-modal data and physical rules and accurate mapping of uncertainty are achieved.

The invention discloses a hot-pressing control method and system for a flexible circuit board, and particularly relates to the technical field of flexible electronic manufacturing process control, and the method comprises the following steps: calculating a heat conduction equilibrium coefficient through collecting the temperature and heat flux density data of each region in the flexible circuit board, and achieving the real-time evaluation of a heat diffusion characteristic; in combination with an infrared scanning mode, acquiring reflectivity data, analyzing dynamic change of the reflectivity data along with time, and identifying an abnormal region in which coating residues possibly exist; further comparing the position relationship between the thermal diffusion anomaly and the infrared reflection anomaly, screening a space overlapping region as a high-risk point, and executing a preset regulation and control strategy; according to the method, the heat conduction equilibrium coefficient is calculated by collecting heat flow and temperature data, the coating residue abnormal area is recognized by combining infrared reflectivity time sequence characteristics, the high-risk area is screened based on the space overlap ratio for dynamic regulation and control, accurate recognition, differential control and closed-loop optimization of hot pressing defects are achieved, and the hot pressing quality and consistency are improved.

The invention relates to the technical field of feed quality control, in particular to an intelligent feed processing quality control method and system. According to the method, the operation parameters and the environment temperature and humidity of the production equipment are acquired in real time, and the environment and equipment state parameter set is established, so that the influence factors of the production conditions are quantified, and an accurate basis is provided for parameter adjustment of the subsequent processing flow. In the production process, the adaptive processing flow threshold value is calculated based on the operation stability of equipment and the change trend of raw material components, and unstable product quality caused by raw material fluctuation or equipment state change is avoided. And whether key indexes in the production process are in an adaptive range or not is dynamically judged by combining the change conditions of the steam absorption rate and the granulation pressure, so that the processing flow control is finer. For trend deviation possibly occurring in the production process, the stability of the production process is analyzed by analyzing the equipment load level, the energy consumption change synchronism and the abnormal trend of processing flow parameters.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

The invention relates to the technical field of production process control, in particular to an MES system management and control method and system based on precision mold production and manufacturing, and the method comprises the following steps: obtaining a measurement point cloud and a theoretical model, calculating rigid body transformation matrix registration to generate a registration point cloud, searching a nearest projection point, measuring geometric deviation, and constructing a three-dimensional deviation vector; and calculating the actual residual material thickness, screening and generating margin insufficient risk points, analyzing a numerical control code to extract a coordinate instruction, retrieving a nearest risk point, calculating a compensation distance, and adjusting coordinates to generate a self-adaptive finish machining program. According to the method, the three-dimensional deviation vector is constructed to represent the entity state, the risk area is screened in combination with the machining allowance, the compensation distance is calculated according to the actual residual thickness, and the instruction is dynamically adjusted to generate the self-adaptive program, so that the path is automatically corrected according to the shape of the workpiece, and the overcut risk caused by insufficient allowance is effectively avoided; and accurate matching between the machining process and the workpiece state is ensured.

The invention discloses an intelligent traditional Chinese medicine decoction control method based on the Internet of Things and a storage medium, and relates to the technical field of traditional Chinese medicine preparation equipment and process control. A target state vector is generated through a prediction model deployed on a terminal side, and a benchmark reference is provided for off-line control; the fine adjustment of decoction parameters is realized by controlling an increment mapping function, and the stability of the control process is ensured by a rate limitation and amplitude saturation mechanism; due to introduction of the safety boundary condition, abnormal working conditions such as pressure overrun and too low liquid level are effectively prevented; after the network is recovered, an incremental training mechanism of differential privacy protection is adopted, so that off-line data is fully utilized, and the risk of privacy disclosure is avoided; a dynamic weight distribution strategy in cloud federation aggregation ensures the leading effect of high-quality data on model optimization, and a multi-stage verification mechanism ensures the reliability of model updating.

The invention provides an industrial furnace temperature adaptive optimization control method and system based on a multivariable chaotic time sequence, and belongs to the technical field of industrial process control, and the method comprises the steps: collecting operation parameters to form a multivariable time sequence, and carrying out the processing verification of chaotic characteristics, and obtaining an analyzable sequence; extracting features through phase-space reconstruction and quantifying variable coupling strength to obtain multivariable chaotic features; a prediction model and an early warning model are constructed based on the above, and a temperature change trend and early warning information are obtained; and finally, setting and optimizing control parameters, generating strategy execution and combining feedback to form closed-loop control. According to the method, the multivariable chaotic characteristics of the industrial furnace are analyzed, the prediction and early warning model is constructed, and self-adaptive optimization control is carried out, so that accurate regulation and control of the temperature are realized, the product quality stability is improved, and the energy consumption and the production cost are reduced.

The invention discloses a cooperative control system and method for multi-process processing of a PCB (Printed Circuit Board). Multi-source process data of each process is collected, a process state baseline is formed through mutual verification of redundant sensors, process drift characteristics are examined and identified based on process capability, a process reference library is constructed in a layered manner by adopting a dynamic threshold value, and a generated process deviation parameter is detected; performing correlation analysis on adjacent processes to determine a transfer coupling degree, positioning a defect transfer link through bidirectional verification combining forward tracking and reverse backtracking, and cascading and accumulating process deviation parameters along the link to form a process incidence matrix; carrying out backtracking on defect diffusion to determine a sweep range, generating process state parameters according to the sweep range, and extracting trend turning characteristics to construct a process characteristic spectrum; defect amplification factors are extracted through sensitivity decoupling, process critical nodes are identified, a regulation and control rule base is constructed, process characteristic chromatograms are integrated, and process control instructions are output, so that defect source positioning and propagation path tracking are realized.

The invention relates to the technical field of process control, and discloses a multiple cyclic utilization process control method of regenerated polyethylene for tailing seepage prevention, which comprises the following steps: synchronously acquiring melt pressure, screw driving motor current and a control input sequence, calculating a cross-correlation function of the current and the pressure, and identifying physical transmission lag time; monitoring an uncommanded residual component of the current in real time, deducing an impending pressure fluctuation amplitude based on a cross-correlation function, and generating an inertia feedforward compensation amount; according to the method, a dual-time scale model is utilized to update parameters online and construct a disturbance observer to generate a feedback compensation control quantity, feedforward and feedback compensation are superposed to a basic output quantity to generate a final driving instruction, a heterogeneous signal time sequence correlation mechanism is utilized, reverse adjustment is carried out before pressure fluctuation is transmitted to a machine head through a screw, and the pressure fluctuation is reduced. And a control blind area caused by physical transmission lag is avoided.

The invention discloses a solid waste-based zinc chloride preparation process control method and system, and relates to the technical field of zinc chloride preparation, and the method comprises the following steps: obtaining a unified time base line, establishing a bubble phase reference field, collecting a sound-induced echo sequence, a micro-pressure waveform sequence and a light scattering sequence of a reaction liquid under the bubble phase reference field, and calculating the solid waste-based zinc chloride preparation process. Fusing to generate a bubble density space-time matrix; and performing reversible time-frequency rearrangement based on the bubble density space-time matrix, decomposing to obtain different frequency band distributions, extracting a narrow-band resident bubble group, calculating critical volume distribution and collapse precursor indexes, and constructing a dynamic risk fingerprint database. According to the method, a unified time base line and a bubble phase reference field are constructed, the bubble state is perceived in real time, collapse forebodes are recognized, a dynamic risk fingerprint database is established, directional intervention and reverse energy channel collapse nucleus extinguishing are achieved in combination with an energy aggregation node, finally closed-loop regulation is constructed through calibration write-back, the safety and stability of zinc chloride preparation are improved, and the method is suitable for large-scale popularization and application. And efficient utilization and clean production of solid waste are promoted.

The invention relates to the technical field of process control, and discloses a liquid medicine circulation filling control method and system for inhibiting flow pulsation, and the method comprises the steps: obtaining an original flow signal and liquid medicine viscosity data of liquid medicine in a liquid medicine filling pipeline, and calculating a reference deviation value of the liquid medicine viscosity data, performing gain effect analysis on the liquid medicine viscosity data based on the calculated reference deviation value to obtain a dynamic gain coefficient of the liquid medicine viscosity data, and acting the dynamic gain coefficient on the obtained original flow signal of the liquid medicine in the liquid medicine filling pipeline to obtain a compensation control signal; a pressure fluctuation signal in the compensation control signal is decomposed into a high-frequency mechanical vibration component and a low-frequency flow pulsation component, the two decomposed components are subjected to frequency-band-divided collaborative filtering, a frequency division filtering signal is obtained, and the opening degree of a liquid medicine filling valve is adjusted according to the frequency division filtering signal; according to the invention, the filling precision of the liquid medicine capable of inhibiting the flow pulsation can be improved.

The invention discloses a valve state monitoring and safety interlocking method based on machine vision, and belongs to the technical field of industrial process control and safety. The method comprises the following steps: 1, calibrating a camera and establishing a model; step 2, image acquisition and preprocessing; step 3, key image feature extraction and state decision; fourthly, opening degree calculation and state judgment are carried out; 5, safety interlocking is triggered; and 6, feeding back data. The valve state monitoring and safety interlocking method is achieved through the valve state monitoring and safety interlocking system. According to the invention, the millimeter-level or even submillimeter-level displacement and angle real-time accurate measurement of the opening degree of the valve can be realized, the state monitoring of the valve is realized, and the stability and reliability in the production process are improved; state judgment is carried out by comparing the real-time opening value with the instruction opening from the control system, a safety interlocking signal is generated and transmitted to the safety interlocking system, and finally physical safety action is triggered, so that a set of complete safety protection system is realized, and accidents are avoided.

The invention discloses a sewage plant multi-process-section collaborative intelligent decision fusion system, which comprises a sensing and rule fusion layer used for collecting inlet and outlet water quality parameters, process control parameters and operation state parameters of multiple process sections in real time, preprocessing data and fusing an expert rule base; the mechanism and data driving joint modeling layer is used for establishing a mechanism model and a data driving model under the constraint condition of the expert rule base and predicting control quantities respectively; and the collaborative optimization and fusion decision-making layer is used for executing cross-process-section multi-target collaborative optimization and fusion decision-making based on an output result of the mechanism and data driving joint modeling layer under the constraint condition of an expert rule base, calculating a dynamic fusion weight, generating a final control quantity, and issuing the final control quantity to execution equipment. And in combination with real-time feedback self-adaptive adjustment, closed-loop optimization is realized. According to the system, the sewage treatment stability, decision precision and resource utilization efficiency are improved, and the environmental risk is reduced.

The invention discloses a discrete coating manufacturing method and system which are suitable for small-batch and multi-variety production scenes. The system comprises an MES system, an AGV intelligent logistics device, an RFID identification module, various processing devices and a quality inspection terminal. The method comprises the following steps: an MES system issues a production task and process parameters, a material distribution worker endows each box of materials with a unique identifier and binds a process template, an AGV conveys the materials to a matching device according to a path template and carries out processing, and the device automatically reports a processing state when the materials flow in or out to realize single-box-level tracing; if the equipment bottleneck exists, the materials are fed into a cache stereoscopic warehouse and wait for redistribution; and after machining is completed, the AGV conveys the materials to a quality inspection station, quality evaluation is completed in a machine vision or manual quality inspection mode, and a detection result is uploaded to an MES system for filing. The system has the capabilities of flexible scheduling, precise process control and whole-course tracing, and the coating production efficiency and the quality control level can be remarkably improved.

The invention relates to the technical field of liquor brewing process optimization, and discloses a Maotai-flavor liquor process optimization method based on big data analysis. The method comprises the following steps: establishing a dynamic knowledge graph covering the whole process of raw material treatment, starter propagation, stacking fermentation, fermentation in a cellar, distillation, aging and blending; receiving real-time production data streams including temperature, humidity, microbial quantity and raw material components; matching the data stream with the knowledge graph to identify an abnormal process node; generating a process correction path containing a parameter adjustment amount and an execution time sequence for each abnormal node; and finally, an overall optimization scheme is formed through integration. According to the method, systematization and computability of process knowledge are realized through the dynamic knowledge graph, and the precise and collaborative regulation and control instruction is generated based on data driving, so that the dependence on traditional experience is overcome, and the accuracy of process control and the stability of product quality are improved.

The invention relates to the technical field of electrode preparation, and discloses a dry-method electrode preparation process control method and system and a storage medium. The method comprises the steps that in the rolling forming process of a dry-method electrode material, pressure roller gap data and rotation angle data of a driving motor are monitored and collected in real time, and a support vector machine algorithm is used for recognizing the transmission ratio change trend of a pressure roller system; calculating a pitch precision offset caused by wear through Kalman filtering in combination with historical wear data; when the offset exceeds the limit, an adjustment coefficient is generated based on a simulation model, and the control precision is improved through feed-forward compensation and feedback optimization; when the adjustment precision meets the process requirement, a self-adaptive parameter set is determined through dynamic deviation residual analysis, and an actuator is driven to adjust the gap between the compression rollers; and monitoring the thickness uniformity index of the electrode plate produced after adjustment in real time, and finally forming quality closed-loop control by taking the thickness uniformity of the electrode plate as a verification standard. According to the invention, the quality precision of dry-method electrode production is improved.