1358 results about "Production control" patented technology

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

The invention discloses a health food production line control method and system based on deep management, and relates to the technical field of food production control. A raw material feature database is established, a first analysis result is output as a raw material deviation evaluation reference, data of a current batch of raw materials are collected, differences are compared, and a raw material deviation result is output; the method comprises the following steps: collecting process data, analyzing whether the process data is abnormal or not by combining with raw material deviation, generating an adjustment suggestion, executing formula and parameter adjustment, triggering a self-repairing mechanism when the process data is abnormal, identifying an intermediate product state, evaluating a quality risk level and triggering early warning. Dynamic optimization of a formula and process parameters is achieved, meanwhile, an image and spectrum fusion early warning technology is introduced, the quality control ability of intermediate products is improved, finally, optimization parameter configuration is generated for the next batch through whole-process data chain construction and causal reasoning analysis, and self-adaptive control and quality steady-state improvement in a production closed loop are achieved.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The invention discloses a workshop task allocation method and system based on multiple dimensions, and relates to the technical field of production control management. Comprising the steps of obtaining order data and then performing preprocessing; based on the preprocessed order data, an advanced machine learning algorithm is adopted to construct a decision tree model, and the processing complexity of each order is identified; determining a task allocation scheme by using an optimization algorithm according to the identified order processing complexity and the current state of the production line; monitoring the actual operation condition of each production line in real time, starting a dynamic adjustment mechanism when the work load of a certain production line is unbalanced, and re-evaluating and adjusting task distribution; through intelligent task allocation and resource optimization, the production efficiency and customer satisfaction are improved, and meanwhile, dynamic adjustment and intelligent management of the production process are realized.

The invention relates to the technical field related to intelligent production control, in particular to a coal mine production equipment control method and system based on remote body interaction, and the method comprises the steps: coal mine production equipment uploads parameters, analyzes the state, configures a twin architecture, and carries out local decision abnormity early warning. A remote end is communicated with an interaction device, biological characteristics are reflected as a pose instruction, time delay compensation and track deviation prediction are embedded, a multi-mode feedback unit triggers smooth control according to the pose instruction, and tactile feedback is enhanced in combination with a vibration spectrum. The technical problems that the network condition of the underground coal mine environment is poor, obvious communication delay exists in remote control, operation instruction lagging exists, and the response speed and operation safety of equipment are affected are solved, dynamic time delay is predicted through the LSTM network, track deviation is compensated, the real-time performance and stability of remote control are improved, accurate execution of instructions is ensured, and the service life of the equipment is prolonged. And the remote operation efficiency and reliability of the coal mine equipment are improved through high-precision pose control and real-time compensation.

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 invention relates to the technical field of production line monitoring control, in particular to a monitoring control method and system for an automatic production line. According to the technical scheme, the pulse laser is transmitted to the monitoring optical cable of the battery production line, and the feedback scattering signal is received; performing time-frequency analysis, space-time array analysis and frequency-domain space array analysis on the scattered signals to obtain multi-dimensional vibration information of each operation device of the battery production line; the multi-dimensional vibration information comprises the vibration main frequency of the operation equipment and the vibration source position and the energy spectrum density of the bearing area to which the operation equipment belongs, so that the actual operation state of each operation equipment during execution of the current production task can be determined based on the multi-dimensional vibration information of each operation equipment and the progress information of the corresponding production task; and performing interlocking operation control on the production takt of the battery production line according to the actual operation state of each operation device. According to the technical scheme, the stability of production control of a battery production line and the consistency of battery product quality are improved.

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 provides a printing machine equipment safety production control method and system, and relates to the technical field of printing machines, and the method comprises the steps: collecting the operation data of a printing machine group through a multi-mode sensor, extracting dynamic characteristics through a time sequence attention mechanism, and constructing a space-time incidence matrix to represent the coupling relation between the printing machines. And constructing a hierarchical deep learning model, integrating feature fusion, time sequence prediction and risk assessment, analyzing the health state of the equipment, calculating an overall safety risk index and identifying fault equipment. And generating an optimal scheduling scheme including an equipment start-stop sequence and a load distribution strategy based on a scheduling decision model of deep reinforcement learning in combination with a safety threshold, resource distribution and production efficiency constraints, and realizing safe and efficient scheduling of the printing machine group. Potential risks can be effectively identified, safety accidents are avoided, and the production efficiency is improved.

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.