3803 results about "Process control" patented technology

The invention discloses a sewage plant total nitrogen concentration real-time prediction and process regulation and control method based on an optimization integration algorithm, and belongs to the technical field of environment monitoring and treatment. According to the method, sewage plant data are monitored and collected, a sliding window and a time sequence are combined to analyze and clean the data and reconstruct features, total nitrogen concentration strong correlation variables are screened, data quality is standardized and optimized, a plurality of machine learning algorithms are adopted to construct a prediction model, and an optimal model is optimized through cross validation and performance evaluation. The robustness is improved by global parameter adjustment in combination with optimization algorithms such as a particle swarm, process schemes such as aeration intensity and carbon source adding are generated through multi-objective optimization after containerization deployment, and a whole-process intelligent management and control system is constructed by integrating virtual verification, graded early warning and a self-adaptive feedback mechanism. According to the method, the problems of detection lag, insufficient model generalization ability, regulation response delay and the like of a traditional method are solved, and the operation energy consumption and the medicament cost are remarkably reduced while it is guaranteed that the effluent quality stably reaches the standard.

The invention discloses a plug-in inductor winding process control method based on production parameter dynamic compensation, and provides a closed-loop compensation control strategy taking a physical field evolution model as a core aiming at the problems of unstable sensing quantity and non-uniform structure in the traditional process. The method comprises the following steps: firstly, constructing an ideal physical field reference model according to target inductance parameters and a structure, and describing an evolution law of characteristics such as a magnetic field, an electric field or impedance in an ideal state; in the actual winding process, a physical field signal is collected through a sensor, production parameters such as wire tension and main shaft rotating speed are obtained, and a physical field distribution snapshot of the current winding state is generated. And the system compares the extracted actual features with the reference model, identifies a dominant disturbance source causing deviation in combination with real-time production parameters, and outputs a disturbance type label carrying causal information. And according to a label and a production parameter change trend, a compensation instruction is dynamically generated and executed, fine adjustment of tension, a winding displacement track or density is realized, and consistency and precision of an inductor finished product are ensured.

The invention relates to the technical field of manufacturing process control, and discloses a multi-dimensional process data co-simulation control method and system and a storage medium. The method comprises the following steps: firstly, collecting a multi-dimensional process parameter set of a plurality of process equipment in a manufacturing production line, carrying out cross-dimensional feature extraction, and generating a comprehensive feature matrix containing a time sequence feature, a spatial distribution feature and an energy consumption feature; according to feature relevance of different dimensions in the comprehensive feature matrix, a process parameter dynamic coupling model is constructed, co-simulation is carried out on interaction between process equipment, and a process state evolution sequence is output; extracting abnormal fluctuation characteristics in the sequence, and generating a process parameter adjustment instruction set; and finally, according to the adjustment instruction set and real-time process feedback data, dynamically correcting model simulation parameters, generating an optimized process control strategy, and executing the optimized process control strategy. According to the method, collaborative analysis and dynamic control of multi-dimensional process parameters are realized, and the accuracy and adaptability of process control are improved.

The invention provides a sewage plant effluent prediction method, system and equipment based on an improved Bi-LSTM model, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring historical operation data of a sewage plant, introducing an attention mechanism, a bidirectional structure and residual connection based on a standard LSTM unit, constructing a Bi-LSTM prediction model, taking a key kinetic equation of a simplified activated sludge model ASM as a physical constraint condition, inputting the operation data subjected to data preprocessing into the Bi-LSTM prediction model, and calculating the operation data of the sewage plant according to the operation data. The prediction result is subjected to multi-objective optimization based on the genetic algorithm to obtain an optimal process parameter combination, and the optimal process parameter combination is converted into an actual process control instruction to realize dynamic parameter adjustment, so that the prediction precision is greatly improved, and the energy consumption is reduced, the stability is improved and the abnormal working condition adaptive capacity is improved through multi-objective optimization.

The invention relates to the technical field of electric power engineering management and control, and discloses an electric power engineering construction project whole process management and control system. The system comprises an engineering parameter monitoring module, a risk analysis module, a progress control module, a quality evaluation module, an early warning output module and a background supervision terminal. The engineering parameter monitoring module collects multi-dimensional real-time parameters through a sensor array and sends the multi-dimensional real-time parameters to the risk analysis module and the background supervision terminal. The risk analysis module generates a progress control instruction according to the deviation between the real-time parameter and a preset standard parameter and sends the progress control instruction to the progress control module. The progress control module executes progress adjustment operation, and the quality evaluation module monitors the adjustment process and generates a quality signal. The early warning output module receives the quality early warning signal, generates early warning information and sends the early warning information to the background supervision terminal, and the background supervision terminal gives an alarm. According to the system, collaborative operation of engineering parameter real-time monitoring, risk analysis, progress adjustment, quality evaluation and early warning is realized, and a whole-process management and control closed loop is formed.

The invention relates to the technical field of automobile part manufacturing, and discloses an automobile injection molding part production process control system and method, and the system comprises the following modules: a data collection module which is used for collecting technological parameters, molds, raw materials and equipment operation original data, attaching timestamps, and storing the data in a database; the process parameter prediction module is used for reading original data to construct a time sequence data set, inputting the time sequence data set into a TFT model to obtain a pre-training model, and predicting a short-term process parameter fluctuation range in combination with current production working condition parameters; and the quality risk index acquisition module is used for inputting the process parameter data and the mold data into a quality risk index calculation formula to obtain a quality risk index. Through the system, data-driven comprehensive production optimization is realized, the process control accuracy and adaptability are improved, the quality control scientificity and reliability are enhanced, the intelligent level of the production process is improved, the production efficiency is effectively improved, the defective rate is reduced, and the product quality is stabilized. The problem that process control lacks system intelligence is solved.

The invention discloses a charging and moisture regaining equipment temperature prediction method based on a time sequence fusion network model, and relates to the field of production process control, and the method comprises the steps: collecting time sequence data in a charging and moisture regaining equipment production environment, and carrying out the preprocessing; dividing the data set into a training set, a verification set and a test set, and injecting Gaussian noise into the training set; a prediction model for predicting the outlet temperature is constructed, and the prediction model is a time sequence fusion network model and comprises a residual TCN time sequence convolutional network, an SK-Net multi-scale attention network and a BiLSTM bidirectional circulation network; pre-training the prediction model by using the training set; utilizing the trained prediction model to predict the outlet temperature of the feeding and moisture regaining equipment; and evaluating a prediction result, and if an evaluation index is greater than a threshold value, starting an incremental training process to re-train the prediction model. According to the invention, through a multi-module combined deep learning model, the prediction accuracy of the outlet temperature of the charging and moisture regaining equipment can be improved in a complex and changeable industrial environment.

The invention discloses a dynamic intelligent control method and system for improving the efficiency of a rectifying tower, particularly relates to the technical field of chemical process control, and is used for solving the technical defect that an operation variable is mismatched with a physical state due to the fact that an existing distributed control system cannot recognize a hidden degradation state of equipment. The method comprises the following steps: acquiring temperature gradient distribution of each tower plate of a rectifying tower, thermal load of a reboiler and tower top condensation load parameters in real time, calculating a mass transfer efficiency offset by combining the temperature gradient distribution to generate a scaling trend index, and synchronously calculating a local entropy yield based on the thermal load and the condensation load to generate a thermodynamic irreversibility index. When the double indexes exceed a preset threshold value, it is judged that a recessive degradation state is entered, then a backflow ratio correction coefficient is reversely derived through a dynamic correlation model, and the correction coefficient is input into a distributed control system to dynamically adjust backflow ratio operation parameters; the product purity is maintained, the tower plate scaling process is obviously delayed, and long-term efficient operation of the rectifying tower is guaranteed.

The invention discloses an intelligent thickness control system for calendering a high-brightness edge sealing sheet, and relates to the technical field of intelligent manufacturing and industrial process control. The method sequentially comprises six steps of multi-modal online acquisition, multi-source fusion drift compensation, digital twinning real-time synchronization, model prediction closed-loop control, high-speed execution fault self-diagnosis and reinforcement learning adaptive optimization. Thickness data are synchronously obtained through laser, terahertz, ultrasound and environment quantity, trusted thickness flow is output through a drift model and a confidence matrix, future thickness is predicted through digital twinning rolling to give uncertainty, first-step control quantity is generated through quadratic programming, millisecond-level execution is conducted through a distributed clock, and self-healing is conducted under shadow driving hot standby and cloud diagnosis. And reinforcement learning agent online iteration improves the energy-saving and stable performance, and full-life-cycle closed-loop control with accurate thickness, low energy consumption and long service life of equipment under complex working conditions is realized.

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 relates to the technical field of industrial production intelligent process control, in particular to an AI-based electrolytic manganese process production quality intelligent prediction method and system, and the system comprises a drift calibration module, a structure matching module, a process parameter inversion module, a path correction module and a deviation verification module. According to the method, dynamic compensation of deviation between input parameter drift and output parameters is achieved through the response relation between input and output of all links, the actual influence degree of fluctuation of all the parameters on the target control parameters and product quality is effectively recognized according to correlation matching of the periodic change of the production process and the fluctuation frequency of the target control parameters, and the accuracy of the product quality is improved. Potential interference of key process parameters on stability of each process is locked in time, diffusion starting time and space of impurities are clearly traced through path judgment of residual liquid concentration trend and raw material adding position, and migration path parameters are further adjusted based on ion retention behaviors of boundary detection points.

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 relates to the technical field of industrial automation and process control, in particular to a digital visual control system and method for automation equipment. The method comprises the following steps: carrying out image sequence acquisition and preprocessing on the material conveying automation equipment to obtain an enhanced flow image set; performing flow texture and flow velocity distribution analysis on the enhanced flow image set to obtain a texture feature data set and a velocity field distribution diagram; performing flow state feature extraction on the texture feature data set and the velocity field distribution map to obtain a flow state feature vector map; performing three-dimensional point cloud construction on the material to obtain a stacking curved surface model; and carrying out repose angle measurement and partition processing on the accumulation curved surface model to obtain a repose angle distribution diagram. By means of the industrial automation and process control technology, control normal form transformation from passive response to active prevention is achieved, and the stability and accuracy of the powder material conveying and subpackaging process are remarkably improved.

The invention discloses a processing control method and system for a carbon fiber composite transmission shaft, and relates to the technical field of intelligent transmission shaft manufacturing, and the method comprises the steps: dynamically adjusting the control current of a magneto-rheological damper through a PID controller based on a process control parameter set, and recording tension fluctuation and temperature gradient data; inputting tension fluctuation and temperature gradient data into an ANSYS coupling simulation engine, establishing a three-dimensional temperature field model, calculating a resin flow compensation coefficient, generating a temperature-pressure cooperative control curve through an NSGA-III multi-objective optimization algorithm based on the resin flow compensation coefficient, and calculating the resin flow compensation coefficient and the temperature-pressure cooperative control curve according to the resin flow compensation coefficient and the temperature-pressure cooperative control curve. And integrating through a time sequence control engine to generate a solidification control instruction. According to the invention, a multi-source data fusion algorithm is combined with the industrial Internet of Things edge computing gateway, so that accurate monitoring and synchronous processing of the process control parameter set are realized, and the dynamic behavior of the carbon fiber composite material in the processing process is comprehensively reflected.

The invention discloses a power grid transient process deduction method based on digital twinning, electronic equipment and a medium, and the method comprises the steps: building a digital twinning simulation model based on a new energy single-machine simulation model and a three-dimensional field station virtual model, and outputting a virtual value by the digital twinning simulation model; collecting core parameters in a monitoring period; calculating the deviation ratio of the physical value and the virtual value of the physical station based on the core parameter; and adjusting the transient process control strategy based on an adjustment object and the deviation rate, wherein the adjustment object comprises a virtual value, a core parameter, a scene parameter and a model control mode. The digital twinborn model can be calibrated in real time through deviation ratio closed-loop correction of a physical value and a virtual value, it can be ensured that simulation of the transient process of the power grid is highly consistent with the physical reality, and the precision and reliability guarantee of a deduction result is improved.

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 relates to the technical field of semiconductor manufacturing, in particular to an accurate detection method and system for a flat-edge sheet wafer placement rotary chuck, and the method comprises the steps: when a wafer is placed on the rotary chuck, scanning the lap joint condition of a wafer edge and a chuck contact area through a laser sensor, and obtaining the real-time distribution data of an edge lap joint sheet; flat edge position information is extracted from the edge lap joint distribution data, an image matching algorithm is adopted to compare a preset orientation reference, and a specific angle value of orientation deviation is determined; according to the orientation deviation angle value and the edge lapping piece distribution data, the rotating speed and the angle of the rotating chuck are adjusted through a cooperative control algorithm, and a final positioning result of the wafer on the chuck is obtained; after a final positioning result is obtained, the in-place state of the wafer is classified and judged through a deep learning model, and whether the residual deviation or wafer lapping phenomenon exists or not is determined; and extracting abnormal data from a classification judgment result, and adjusting collaborative parameters of carrying and placing according to the abnormal data to obtain an optimized process control scheme.

The invention discloses an unmanned aerial vehicle task full-process control system and method based on a multi-agent large model, and mainly solves the problem of poor flight control and anomaly detection performance in a complex dynamic environment. Comprising the following steps: 1) performing real-time alignment and fusion on IMU, energy management, environmental meteorology and other multi-source data, and extracting key features; 2) performing intelligent semantic analysis on a user instruction by means of a large language model, and disassembling the whole task into sub-tasks such as environment perception, path planning, flight control and anomaly detection in combination with real-time data and historical task information; (3) the processing units synchronously execute tasks through a multi-agent cooperation mechanism and feed back the tasks in real time; and 4) performing time sequence modeling by adopting a deep learning technology to obtain a high-precision anomaly detection result, and filtering instantaneous fluctuation through a self-adaptive gating mechanism to form closed-loop control. The intelligent level and safety of task execution of the unmanned aerial vehicle can be effectively improved, and autonomous and efficient flight and emergency response of the unmanned aerial vehicle are achieved.

The invention discloses a full-reactor-core parallel fluid-solid coupling simulation method and system. The system comprises a global collaborative coupling simulation scheduling module, a hierarchical index forest management module, a high-performance parallel solver module and an intelligent parameter transmission module. Wherein the global collaborative coupling simulation scheduling module is used for being responsible for overall process control, time stepping, solver scheduling, convergence judgment and inter-module communication triggering; the hierarchical index forest management module is used for being responsible for distributed parallel construction and efficient dynamic updating of CLGIF and providing rapid geometric query service; the high-performance parallel solver module is used for embedding or tightly integrating numerical solvers of fluid and solid physical fields and applying a dynamic parallel multi-solver optimization module to maximize the calculation performance and expansibility of the high-performance parallel solver module; and the intelligent parameter transmission module is used for executing physical quantity exchange on a fluid-solid interface, the core of the intelligent parameter transmission module is a GME-driven region sensing RBF mapping engine, and the transmission precision and efficiency are ensured.

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 processing control, in particular to a household appliance injection molding workshop process control system based on dynamic adjustment, which comprises an injection molding dynamic control center, and the injection molding dynamic control center comprises a process supervision unit, a traceability management unit, a processing quality unit, a dynamic regulation and control unit and a control early warning unit. According to the method, the processing efficiency and the loss of the household appliance injection molding workshop process are preliminarily analyzed, so that the safety supervision efficiency of the household appliance injection molding workshop process and the reliability of the household appliance injection molding workshop process are improved, and raw materials or parts of abnormal injection molding equipment are specifically managed in an information progressive mode; according to the method, the processing cost is reduced, the processing result of the process product in the household appliance injection molding workshop is deeply supervised and analyzed to improve the processing qualification rate of the process product, and the parameter processing stability evaluation analysis is performed on the injection molding parameters, so that the floating parameters are dynamically adjusted in a targeted manner.

The invention provides an AI-driven industrial decarburization process control method and system based on feedback regulation, and relates to the technical field of industrial control, and the method comprises the steps: executing industrial decarburization process monitoring, and obtaining a process operation data stream sequence; performing process working condition time sequence iterative analysis; matching is carried out in a pre-constructed causal knowledge base; traversing the associated causal clue set to perform historical data backtracking exception investigation to obtain an exceptional associated causal clue set and an exception degree set; and performing feedback control scheme identification to obtain a target feedback control scheme, generating a feedback adjustment instruction, and issuing the feedback adjustment instruction to the control execution module to execute the target feedback control scheme. According to the method and the device, the technical problem that in the prior art, due to the fact that static model prediction or empirical rule-based judgment is mainly adopted, real-time tracing and dynamic intervention of process-level carbon emission abnormity are difficult to achieve, and the carbon emission control effect in a complex environment is not ideal is solved, and the control effect of the industrial decarburization process is improved.

The invention discloses an optimal control method for a straw pyrolysis carbonization process. The method comprises: performing multipoint temperature real-time acquisition and data processing on a carbonization zone of a carbonization furnace, obtaining temperature information and calculating a deviation from a preset ideal temperature curve (S01); dynamically adjusting an operating parameter of the combustion zone using a control algorithm based on the temperature deviation (S02); performing on-line monitoring on the flue gas components (S03); and regulating and controlling the return air flow and the secondary air supply amount of the combustion area in a linkage manner according to the flue gas monitoring result so as to optimize combustion and energy recovery (S04). Through accurate and intelligent process control, efficient, environment-friendly and high-valued utilization of the straw carbonization process is realized.

The invention discloses a bamboo industry-oriented AI intelligent production service management cloud platform and system, and the platform comprises the following steps: a data collection module which is used for collecting multi-source heterogeneous data, and constructing a process behavior sequence and a state variable sequence; the causal modeling module is used for executing a causal migration focusing algorithm to construct a causal influence map and identifying a key variable sequence; the time sequence modeling module is used for jointly inputting the process behavior sequence and the key variable sequence into a Jamba model and outputting key control parameters and process control path suggestions; the causal evaluation module is used for outputting a path causal credibility score; the optimization rerouting module is used for triggering an expert sub-network rerouting mechanism and generating an optimized process control path suggestion; and the process execution control module is used for suggesting to execute the processing operation of each process link of the bamboo according to the optimized process control path. According to the method, causal modeling and the Jamba model are combined, so that accurate control and production service management of the bamboo wood process 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 automatic control, and discloses a control method and system for an injection molding process of automotive trim. The method comprises the following steps: synchronously acquiring a pressure pulsation signal, flow velocity distribution data and flow front position information of a plastic melt through a high-frequency pressure pulsation sensor array and a melt flow velocity sensor, and performing fast Fourier transform processing on the pressure pulsation signal to obtain a characteristic frequency component; a flow impedance coefficient and a shear stress distribution parameter are calculated in combination with flow velocity data, the impedance coefficient is compared with a reference value to identify an abnormal type, a pressure compensation amount is calculated through a classification compensation algorithm, and finally a servo hydraulic execution system carries out rapid pressure adjustment. The technical problem that the flow impedance characteristic change of the plastic melt cannot be recognized in real time and the injection pressure cannot be dynamically compensated in the existing automotive trim injection molding process is solved.

The invention discloses tunnel deeply-buried ditch construction equipment and method.The method comprises the following steps that S1, a self-propelled inverted arch trestle is moved to a construction section, a total station is adopted for lofting an excavation sideline of a deeply-buried ditch, and the sideline is marked; s2, driving tunneling equipment into an operation area, adjusting the center line of the tunneling equipment to be aligned with the designed center line of the ditch, meanwhile, lifting a slag removal conveying belt of a bridge approach machine to a specified height, enabling a transport vehicle to be in place, and enabling the equipment to run in a no-load manner; s3, a tunneling system of the tunneling equipment is started, and the tunneling equipment is driven by the control system to conduct tunneling operation according to a preset track; s4, muck in the muck collecting system is conveyed to a transport vehicle through a conveying belt to be transported outwards; and S5, after the ditch is excavated, a base is formed at the bottom, and meanwhile a temporary protection wall is installed. According to the device, the randomness of manual operation is avoided through mechanical operation, process control is more convenient, meanwhile, procedure connection gaps are reduced through continuous construction, and the problem that the exposure time of the inverted arch is too long is effectively solved.