568 results about "Parameter control" patented technology

The invention relates to the technical field of fastener process production, in particular to a process parameter control method and system applied to fastener production. The method comprises the following steps: acquiring three-dimensional cutting force of a tool-workpiece contact area in a fastener thread machining process in real time, and generating cutting force dynamic fluctuation characteristic data; the real-time abnormal situation of the current machining process is recognized according to the cutting force dynamic fluctuation characteristic data, a tool wear mode is judged, thread ring machining deviation prediction is conducted, and a predicted thread geometric deviation value is generated; and adjusting multiple machining control parameter values according to the predicted thread geometric deviation value, and carrying out real-time machining feedback monitoring so as to realize fastener production self-adaptive control. According to the method, intelligent recognition of the tool wear mode and accurate prediction of the machining deviation are achieved through cutting force sensing, and it is ensured that the thread machining precision of the fastener is stable and controllable through multi-parameter cooperative self-adaptive regulation and control.

The invention relates to the technical field of plastic processing, and discloses an environment-friendly plastic product extrusion molding intelligent parameter control system and method, and the system comprises a data collection and fusion module, a causal association construction module, a digital twinning and virtual-real interaction verification module, an intelligent control module, a self-adaptive reconstruction module, and a man-machine interaction and visualization module. The method comprises the steps of collecting data in real time, and performing deep fusion to generate features; dynamically constructing a knowledge graph by fusing features, and synchronously updating causal topology; constructing a digital twinborn model, and performing virtual operation prediction; generating an instruction to regulate and control the microstructure; recognizing and classifying disturbances, and reconstructing a causal topology to guide strategy adjustment; the system state is displayed in real time, and operation intervention is supported. On-line microstructure data are acquired and deeply fused in real time through the data acquisition and fusion module, and a deep correlation model is constructed in combination with the microstructure guide reverse deduction unit, so that the problems of large batch difference and unstable physical properties of environment-friendly plastic materials are effectively solved.

The invention relates to the technical field of printed circuit board manufacturing, in particular to a PCB lamination parameter control method and system, and the method comprises the steps: receiving a global pressure adjustment instruction; acquiring real-time temperature data of the to-be-laminated sheet, and constructing a surface temperature distribution diagram of the to-be-laminated sheet; the surface temperature distribution diagram is analyzed, and operation areas, with different thermal response attributes, of the to-be-laminated sheet are divided; according to the thermal response attribute of each operation region and the global pressure adjustment instruction, obtaining a region area weight and a response deviation coefficient, carrying out differential decomposition on the global pressure adjustment instruction, and calculating differential pressure adjustment amounts of the plurality of operation regions; and mapping the differential pressure adjustment amount to a multi-partition hydraulic execution mechanism in the laminating equipment, and controlling a target pressure value output by each operation area. The method has the advantages that refined and differentiated pressure adjustment of operation areas with different thermal response attributes in the plate can be achieved, and local defects caused by uneven material response are effectively avoided.

The invention relates to the technical field of cloth bag processing, and discloses a non-woven fabric bag processing parameter regulation and control method and system based on deep learning, and the method comprises the steps: obtaining the real-time rotation speed and tension data of equipment, carrying out the signal filtering and state calculation, obtaining a cloth traction state, carrying out the fuzzy logic processing, and determining the adjustment amount; obtaining a target rotation speed adjustment amount; according to the target rotation speed adjustment amount, threshold judgment and parameter dynamic updating are carried out, a new tension fluctuation index is obtained, fitness evaluation is carried out, and the product specification fitness is obtained; according to the product specification fitness, a dynamic adjustment decision is made, a parameter regulation and control scheme is obtained, risk quantification is carried out, and a production interruption risk level is obtained; and according to the production interruption risk level, risk threshold verification and optimization algorithm correction are carried out, a defective percentage inhibition value is obtained, tension stable signal analysis and global fusion output are carried out, and final processing equipment parameter configuration is obtained. The method can improve the qualified rate of products.

The invention discloses a control method of a hybrid energy storage system and the hybrid energy storage system, and relates to the technical field of hybrid energy storage systems.The control method comprises the steps that corresponding simulation control parameters of the hybrid energy storage system under different operation conditions are obtained, and the simulation control parameters are parameters used for simulating the characteristics of a battery cluster; and controlling the operation state of each energy storage converter according to the simulation control parameters. By adopting the method, the flywheel can be controlled to simulate the characteristics of the battery to work, the cost of the hybrid energy storage system can be effectively reduced, and the complexity of the control process of the hybrid energy storage system is reduced.

The invention belongs to the field of production quality traceability, and particularly relates to an abnormity traceability and parameter regulation and control system for a premix production line, which comprises a monitoring module, a reverse mapping module and a forward adjustment module, a monitoring module can acquire whole-course fingerprint information of an abnormal production process in real time by constructing a whole-course tracking chain containing a process-control association relationship; the reverse mapping module performs reverse whole-course reasoning by using a hidden Markov algorithm, performs longitudinal local reasoning in combination with an index mapping relation, accurately locates an abnormal source, and generates a reverse abnormal information fingerprint chain; and the forward adjustment module performs forward processing control parameter adaptive adjustment from the abnormal starting node according to the fingerprint chain and a preset correction strategy until all indexes return to normal. According to the invention, closed-loop intelligent control from abnormity monitoring and source tracing to production self-adjustment is realized, and the quality consistency and tracing efficiency of premix production are remarkably improved.

The invention provides a heavy duty gas turbine control system which comprises a signal conditioning module and a plurality of parameter control modules, and a rotating speed control module, a load control module, a temperature limit control module and an acceleration control module in the parameter control modules are all used for achieving closed-loop control according to the deviation between a set value and a corresponding observation value; the starting control module is used for acquiring a starting fuel quantity instruction through a designed control strategy; the IGV and VGVs control module obtains an IGV and VGVs angle instruction according to the turbine inlet temperature set value, the turbine outlet temperature set value and the deviation maximum value of the corresponding observation value; the fuel quantity control module obtains a total fuel quantity instruction based on a full working condition range fuel quantity control strategy; the fuel gas distribution control module is used for obtaining an opening degree instruction of each fuel valve according to the total fuel quantity instruction; the adaptability, the reliability and the usability of the control system in application scenes such as autonomous design and technical improvement are improved.

The invention relates to the technical field of aluminum alloy preparation methods, in particular to a control method for preparing an aluminum alloy rod, the aluminum alloy rod, electronic equipment and a storage medium. The method comprises the following steps: firstly, acquiring process parameters of a continuous casting machine and quality parameters of an aluminum alloy bar at an outlet, then comparing the quality parameters with corresponding target parameters, when the quality parameters do not accord with the target parameters, inputting the quality parameters and the process parameters into a regulation and control large model, and outputting parameter regulation and control quantity; and finally, controlling the continuous casting machine according to the parameter regulation quantity. The defect condition and the process parameters are obtained in real time, the operation of the large model is regulated and controlled according to the real-time condition, the continuous casting machine is dynamically controlled, and the defects generated by controlling casting of the aluminum alloy bar through static preset parameters are reduced.

The invention provides a large-scale fluid extraction and separation intelligent control method based on a mixing tank structure, and relates to the technical field of intelligent control, and the method comprises the steps: collecting parameter information in a mixing tank through double-cycle sampling, dividing flow field partitions based on three-dimensional numerical characteristics, collecting concentration gradient information of a target object, and building a digital twinborn model; executing a partition self-adaptive control strategy to dynamically adjust stirring parameters, controlling extraction agent supplement, adjusting the opening degree of a partition plate, and monitoring and updating the control strategy in real time. According to the method, precise zone control is realized, the extraction efficiency is improved, the energy consumption is reduced, and the method adapts to complex working condition changes.

The invention discloses a reinforcement learning-based casting cleaning process parameter adaptive control method, which comprises the following steps of: constructing a reinforcement learning model for updating casting cleaning process parameters; the method comprises the steps that in the process that a casting cleaning system cleans a casting according to a set control period, key process signals in each control period are sequentially collected, and observation vectors are generated; inputting the obtained observation vector into a reinforcement learning model, and processing the observation vector by the reinforcement learning model to obtain a process parameter adjusting quantity, and the casting cleaning process parameters of the current control period are updated according to the process parameter adjusting quantity to obtain updated casting cleaning process parameters, and the updated casting cleaning process parameters are adopted to control a casting cleaning system of the next control period. According to the casting cleaning process parameter self-adaptive control method based on reinforcement learning, continuous and self-adaptive adjustment of the process parameters can be achieved, and the problem that fixed parameter control is insufficient in adaptability under complex working conditions is solved.

The invention relates to a production environment parameter control method and system based on industrial Internet of Things. The method comprises the following steps: collecting a first real-time production environment parameter and a subsequent planned production task, carrying out production environment parameter prediction, obtaining a prediction time sequence production environment parameter set, and determining a critical time point; acquiring response time characteristics of the environment controller, and determining a control time point for the critical time point; and at the control time point, collecting a second real-time production environment parameter of the target workshop, generating an environment control parameter of the environment controller, and controlling the environment controller to execute production environment control based on the environment control parameter. Intelligent prediction of production environment parameters, critical point identification and optimal opportunity intervention control are realized through an Internet of Things sensing network and a production plan, so that the stability of the production environment is ensured, and meanwhile, the energy consumption is reduced.

The invention relates to a refrigerator and a control method thereof. The refrigerator comprises a refrigerator body, a refrigerating system, a temperature sensor and a controller, and is configured to obtain operating environment data, collected at different time intervals, of the refrigerator under the condition that the refrigerator meets the start-up refrigerating condition; time sequence features in the operation environment data are extracted, and time sequence feature data are obtained; extracting spatial features of the time sequence feature data under corresponding control dimensions of different parts of the refrigerator to obtain spatial feature data under the corresponding control dimensions, and performing control parameter prediction on the spatial feature data under each control dimension to obtain refrigerator control parameters under the corresponding control dimensions; and all parts in the refrigerator are controlled to operate according to the refrigerator control parameters under the corresponding control dimensions. The method can effectively improve the operation stability and refrigeration effect of the refrigerator.

The invention provides a goods shelf storage location calibration method and system for an unmanned forklift and related equipment. The method comprises the steps that a scheduling instruction is responded, a target storage location driving path is obtained, and the optimal pose parameter of the unmanned forklift is determined based on a goods shelf model containing the goods shelf standard size and the topological relation; the unmanned forklift is controlled to run to a target garage location, forklift movement data and field environment data are collected through a fork arm laser radar, a top laser radar, a visual sensor and an auxiliary measuring device, and the current actual pose of the unmanned forklift is calculated through an extended Kalman filtering algorithm; based on the current actual pose and the optimal pose parameter, calculating to obtain a pose deviation set; and when the deviation index of the pose deviation set exceeds the standard, automatically updating the corresponding storage location parameters in the storage map according to the pose deviation set. According to the method, the multi-source data fusion framework is constructed, and the extended Kalman filtering algorithm is utilized to realize automatic positioning and calibration, so that the problem that time and labor are consumed due to manual calibration traditionally is solved.

The invention belongs to the technical field of furniture plate cutting, and discloses an integrated furniture plate cutting system based on laser cutting. The system comprises a part dynamic optimal arrangement module, an intelligent cutting decision module, a dynamic parameter control module, a real-time cutting execution module and a production process collaborative optimization module, and a real-time optimization arrangement report and a cutting path optimization report are obtained. Performing parameter dynamic adjustment on the cutting process based on the digital twinborn model of the plate and the cutting path optimization report to obtain a real-time control signal report, driving the cutting equipment based on the real-time control signal report, monitoring the cutting state to obtain a cutting state report, and analyzing the utilization condition of the plate to obtain a processing optimization report. Generally speaking, the method has the remarkable advantages of being high in plate real state sensing capacity, good in plate machining self-adaption effect and large in production process global optimization effect.

The invention discloses a self-adaptive production parameter optimization system and method. The method comprises the steps that physical data and environmental data of equipment operation are collected in real time through a plurality of sensors installed on a production line; the method comprises the following steps: preprocessing data acquired in real time, and removing noise in the data; constructing an adaptive optimization model based on a deep neural network, predicting and analyzing the preprocessed data through the model, and outputting optimal parameters; and the operation parameters of the production equipment are automatically adjusted according to the optimal parameters output by the self-adaptive optimization module. Parameters can be adjusted in real time to adapt to different production environments, the use efficiency of equipment is improved, the consistency of product quality is improved through accurate parameter control, bottlenecks in the production process are reduced through optimal parameter setting, the operation efficiency of a production line is improved, the effect in the aspect of reducing resource waste is remarkable, and the production cost is reduced.

The invention provides a drift control method, an unmanned vehicle and a storage medium, and relates to the technical field of unmanned driving. The method comprises the steps of predicting an expected balance parameter and a feedforward control parameter of a vehicle at a target position based on road information of the target position and state information of the vehicle; a feedback control parameter is generated by controlling the current balance parameter of the vehicle to be the expected balance parameter; and based on the feedforward control parameters and the feedback control parameters, the vehicle is controlled to achieve drifting driving at the target position. According to the invention, the vehicle can be controlled to drift at the curve, and the turning efficiency of the truck is greatly improved. Meanwhile, based on the control strategy of the feedforward control parameters and the feedback control parameters, the drifting posture of the vehicle can be accurately maintained in the drifting process, and the risk of instability in the drifting process is avoided.

The invention belongs to the technical field of intelligent dust removal, and discloses a coal feed-forward dust removal control system and method. The method comprises the following steps: collecting data and carrying out integrity analysis and data cleaning to obtain coal quality feed-forward data X, working condition data Y and equipment data; obtaining a strong correlation subset M and a key electric field identifier F through dynamic Pearson correlation analysis; performing coupling analysis on the X to obtain a coupling index T; analyzing according to T and Y to obtain a theoretical ash deposition amount; carrying out dimensionality reduction on the strong correlation subset, obtaining a strong correlation feature subset in combination with a theoretical ash deposition amount, carrying out normalization processing, and inputting the strong correlation feature subset and F into an LSTM model together to obtain a control parameter; on the basis of an MOPSO algorithm, online rolling optimization is carried out, and optimization control parameters are obtained; controlling the system to perform cooperative execution according to the optimized control parameters; according to the method, the problem of adjustment lag of a traditional dust removal system depending on real-time discharge feedback is effectively solved, and the technical bottleneck that energy consumption and discharge are difficult to take into account under single-target optimization is broken through.

The invention discloses a new energy and energy storage combined operation control method, relates to the technical field of energy storage centers, and solves the problem that a traditional energy storage control method mostly depends on fixed rules or single parameter adjustment and is difficult to cope with complex scenes such as new energy output sudden change and energy storage state dynamic change. According to the method, the optimal execution frequency is screened from multiple groups of frequency schemes by adjusting the execution frequency range and combining periodic characteristic evaluation of convolution summation, the operation parameters can be dynamically optimized according to the real-time fluctuation state of the system, the fluctuation characteristic is reduced to the minimum, and the adaptability of the energy storage system under the complex working condition is remarkably improved; according to the optimization logic based on data driving, limitation of fixed parameter control is avoided, the system is always in an efficient operation state, a relative law segment is locked through law analysis of a current waveform in a tracing period, a new energy intervention value is dynamically limited based on current characteristics, and the duty ratio is adjusted, so that a new energy source is traced. And accurate matching of the new energy output and the energy storage demand is realized.

The invention provides a factory equipment control method and device, equipment and a storage medium, and relates to the technical field of industrial manufacturing. In some embodiments of the invention, the method comprises the following steps: obtaining an equipment operation state parameter, a process operation parameter, a global operation parameter and an external environment parameter; inputting the equipment operation state parameters, the process operation parameters, the global operation parameters and the external environment parameters into a scheduling model, and solving through multi-objective optimization by taking the minimum comprehensive cost as an objective function to obtain high-collaboration operation regulation and control parameters; controlling factory equipment to execute regulation and control operation according to the operation regulation and control parameters; according to the invention, cross-level parameter linkage is realized, global optimum is taken as a regulation and control policy, the control efficiency of factory equipment control is improved, and the intelligent degree is improved.

The invention specifically relates to the technical field of distributed control, and discloses a high-strength screw production line cooperation system based on distributed control, which comprises a distributed acquisition module, a quality analysis and evaluation module, a production parameter control module, a dynamic task scheduling module, an edge local decision module and a global optimization feedback module, constructing a high-spiral distributed sensing data set through a distributed acquisition module; the high spiral production quality grade is evaluated through the quality analysis and evaluation module; a production equipment adjusting instruction is generated through a production parameter control module; generating a task allocation instruction through a dynamic task scheduling module; a terminal control instruction is generated through an edge local decision module, and production equipment execution data is obtained; a global optimization strategy is formulated through a global optimization feedback module; through the cooperative effect of the distributed acquisition module and the edge local decision module, the data transmission amount is reduced by constructing the cloud edge cooperative architecture, and the system architecture flexibility and the data processing efficiency are improved.

The invention discloses a control method and system for intelligent matching of the fan frequency and the roller way speed of a high-speed wire air cooling roller way, and relates to the technical field of rolled steel cooling. The method comprises the following steps: designing eight subsystems: a finished product coil head and middle-rear section fan frequency optimization system, a roller way crawling frequency optimization system, an air cooling roller way global acceleration and gradient connection speed system, a fan-roller way speed interval matching correction system, an interval temperature drop quantitative identification system and an interval optimal frequency-speed system; segmented frequency slope control, accurate recognition of the crawling state, rolling line linkage speed increasing, temperature drop difference value correction and double-variable optimal control are achieved. The problems of scattered coil blowing, uneven cooling, high energy consumption and unstable quality caused by existing fixed parameter control are solved, it can be guaranteed that finished product coils are cooled evenly, the scattered coil distance is reasonable, the performance stability is improved, meanwhile, power consumption is saved, the production cost of ton steel is reduced, and the device is suitable for the double-high-wire finished product scattered coil cooling and conveying procedure of a steel rolling mill.

The invention relates to the technical field of intelligent control, and discloses an intelligent hammer crusher control method and system based on artificial intelligence. The method comprises the steps that material characteristics are analyzed through near-infrared scanning, and vibration and acoustic signals are collected to obtain crushing state data; analyzing the energy conversion efficiency and the particle size distribution condition according to the data, and evaluating the crushing efficiency and the product quality; calculating the ratio of the optimal hammerhead linear speed to the feeding rate based on the evaluation result; and the optimization parameters are converted into equipment control signals to realize self-adaptive adjustment. The technical problems that traditional vibrating screen fixed parameter control cannot adapt to multi-coal-type changes, the multi-target constraint problems that screening efficiency and energy consumption are difficult to balance in the parameter optimization process, and a precise matching mechanism is lacked for control parameters and material characteristics are solved.

The invention discloses a method and a device for detecting the bonding strength of a coating, and belongs to the technical field of coating detection. The method for detecting the bonding strength of the coating comprises the following steps: respectively bombarding test areas of a plurality of samples to be detected by adopting first laser according to different control parameters; the method comprises the following steps: detecting the falling degree of a coating in a test area of each to-be-tested sample from a substrate, and judging whether the falling degree reaches a set degree or not; taking the control parameter of the first laser corresponding to the to-be-detected sample with the falling degree reaching the set degree as a critical control parameter; and controlling the first laser to bombard a coating area of a to-be-tested product with the same model as the to-be-tested sample according to the critical control parameter, and evaluating the coating bonding strength of the to-be-tested product according to the surface appearance of the to-be-tested product. The coating bonding strength can be measured without contact with the coating, matrix damage is avoided, detection precision and detection efficiency can be improved, manual intervention is reduced, cost is reduced, and environmental pollution is avoided.

The invention provides a production line production efficiency optimization method and device, and relates to the technical field of intelligent production scheduling, and the optimization method comprises the steps: obtaining equipment monitoring data; performing abnormity monitoring on the equipment monitoring data; when the abnormal production of the production line is monitored, according to the abnormal production of the production line, analyzing to obtain a trigger process corresponding to the abnormal production of the production line of the processed product; based on the triggering process, obtaining a compensation repair process and a compensation repair parameter corresponding to the processed product; and controlling the production line to continuously perform compensation repair on the processed product through the compensation repair process and the compensation repair parameters so as to optimize the defective rate of production of the production line. According to the optimization method and device provided by the invention, the increase of the total quantity of products produced by the production line in order to reach the preset yield due to the increase of the defective product quantity can be effectively reduced, so that the production efficiency of the production line with the preset yield is improved.

According to the machining method and system for the stainless steel rectangular groove plate, the material thickness and elastic modulus parameters of a 316L stainless steel thin plate are obtained, the stress distribution state in the machining process is monitored in real time through a sensor, and if the stress value exceeds a preset threshold value, the cutting force and machining speed parameters are adjusted; a support vector machine algorithm is adopted to analyze the nonlinear relation between the material thickness and the elastic deformation, and an optimized mechanical parameter control model is obtained; according to stress control data output by the mechanical parameter control model, the machining temperature and vibration frequency information of a contact area of a tool and a workpiece is obtained, if the vibration frequency exceeds a stable machining range, the rotating speed and the feeding amount of a spindle are adjusted in real time, and the influence of thermal deformation on the groove depth precision is corrected through a temperature compensation algorithm; and determining a dynamic processing parameter combination adapted to the characteristics of the thin plate. Intelligent control over the thin plate precision machining process is achieved, and the machining precision and efficiency are effectively improved.

The invention relates to a building air conditioner control strategy generation method and device, equipment and a medium. The method comprises the steps that state data, operation states and operation scenes of different areas in a building are obtained; calculating an influence factor score of the corresponding region; adjusting a weight coefficient of the influence factor score; calculating a comprehensive score of the current area according to the influence factor score and the corresponding weight coefficient; comparing the comprehensive scores of different areas with a preset grading threshold value, and determining the importance level of the corresponding area; and generating different control strategies according to the importance levels of the different areas and issuing the control strategies to the corresponding areas. According to the method, comprehensive scoring of different regions is realized by combining the score of the influence factor with dynamic weight adjustment, so that region importance is accurately quantified, and a basis is provided for generating a differentiated control strategy. Compared with a traditional timing control or single parameter control mode, the method can more accurately reflect the actual demand of each area, and unreasonable energy waste is avoided.

The invention discloses a conveying control method and system for a belt conveyor, and relates to the technical field of conveying control, and the method comprises the steps: carrying out the multi-parameter control collaborative decision according to the material conveying scene data of the belt conveyor, and obtaining an initial conveying control scheme; executing simulated transmission and acquiring a data set, and performing start-stop impact risk detection, response abnormal risk mining and fault risk detection based on the data set to respectively obtain three types of risk detection results; and finally, carrying out collaborative optimization on the initial conveying control scheme according to the risk detection results to obtain an optimized conveying control scheme. Therefore, the technical effects of diversified control strategies, perfect risk early warning mechanism and improved optimization depth are achieved.

The invention relates to the technical field of artware processing, in particular to an intelligent regulation and control system and method for carbon carving cinnabar painting, and the system comprises a pretreatment module, a spraying module, a detection module and a control module. The pretreatment module is used for sequentially carrying out sand blasting, cleaning and drying treatment on the target drawing surface of the carbon plate; the spraying module completes spraying according to preset parameters; the detection module obtains a spraying surface image and a bonding strength parameter; the control module judges whether the spraying of the carbon plate meets the standard or not according to the brightness characteristic value of the pre-spraying area, and additionally judges on the basis of the curing duration difference value; and if not, determining the reason that the spraying of the carbon plate does not meet the standard according to the mean value of the bonding strength. According to the intelligent regulation and control system, the problem that color difference exists when cinnabar is sprayed on the surface of a carbon carving product due to orientation of a carbon base material is solved, and the painting effect of spraying is improved.

The application provides a virtual power plant regulation method and device based on non-linear operation and risk, the method comprising: obtaining historical operation data, at least including power plant regulation parameters, power plant cost parameters and risk intervention parameters; constructing a power plant operation model according to the parameters, simulating the change trend of the net profit of the virtual power plant with the power plant regulation parameters and the risk intervention parameters; constructing a first constraint group and a second constraint group, the first constraint group being used for non-linearly limiting the correlation between the power plant regulation parameters and the net profit of the power plant operation model, and the second constraint group being used for non-linearly limiting the correlation between the risk intervention parameters and the net profit of the power plant operation model; solving the power plant operation model based on the two constraint groups with the maximum net profit as a target function, obtaining operation parameters, and controlling the virtual power plant operation. The method solves the problem that there is no virtual power plant regulation method that comprehensively considers the volatility and uncertainty of the virtual power plant in the prior art.