3530 results about "Pressure data" patented technology

The invention relates to the technical field of fire-fighting monitoring, in particular to a fire-fighting water system fault detection and early warning method and system based on the fire-fighting Internet of Things, and the method comprises the steps: 1, periodically collecting pressure data through pressure sensors disposed at a fire-fighting water pump outlet, a pipe network main pipe branch point and the most unfavorable tail end; 2, according to the current valve opening degree, the pump state and historical normal working condition data, theoretical pressure expected values and dynamic allowable deviation zones of all nodes are generated; 3, when actually measured pressure deviates from a theoretical pressure expected value and exceeds a dynamic allowable deviation band, marking abnormal nodes, reversely constructing a fault propagation tree along the topological model, and allocating weight factors for associated nodes according to fault types; and 4, triggering graded early warning based on the number of the abnormal nodes, the fault propagation path and the weight factor accumulated value. And the system can automatically take measures when a fault occurs through an equipment linkage function, so that the efficient operation of the fire fighting water system is guaranteed, and the efficiency and safety of fire emergency response are improved.

The invention discloses an Internet of Things intelligent gas meter leakage detection and early warning system and method, and relates to the technical field of gas leakage detection, and the method comprises the following steps: S1, collecting data through multiple sensors; s2, identifying an equipment operation state based on the data set and outputting a state confidence coefficient; s3, a stable monitoring window period is judged, a corresponding strategy is selected to compensate pressure data, and reliability is marked; s4, dynamically generating a detection threshold in combination with the historical mode, the real-time parameters and the data reliability; s5, dynamically adjusting a risk assessment weight according to the confidence coefficient and the reliability, calculating a risk score and determining an early warning level; s6, safety operation is executed according to grades; and S7, updating the model by using process data to realize self-optimization. According to the invention, by deploying a multi-sensor array and adopting a multi-modal signal fusion algorithm, the system can accurately identify the running state of the gas appliance, provides reliable preposition information for subsequent analysis, and overcomes the defect that data of a traditional single sensor is easily interfered.

The invention relates to the technical field of parameter optimization, in particular to a plastic mold forming parameter optimization method and system based on big data, and the method comprises the following steps: collecting pressure data, calculating an acceleration sequence, recognizing a peak window, matching a pressure-temperature ratio, and building a combined cluster to obtain an optimization result set. According to the method, a speed increasing sequence is constructed based on the die cavity pressure change rate, a high-dynamic section is screened through a peak value ratio, automatic identification of an effective forming section is achieved in combination with continuous ratio stability judgment, forming pressure, die temperature and a cooling time sequence are collected to construct a parameter combination sequence, trend consistency is compared, and available combinations are screened; the parameter acquisition granularity, the process identification precision and the parameter group suitability are obviously improved, the problems of subjective interference and error accumulation in the parameter configuration process are effectively avoided, the objective quantitative analysis capability of the forming process state is enhanced, and the accuracy of the forming process state is improved. And the plastic mold forming process stability and the parameter configuration efficiency are improved.

The invention relates to the technical field of underwater navigation, and discloses a real-time attitude estimation method of an underwater inertial navigation system. The method comprises the steps that the underwater vehicle collects multi-axis angular velocity and linear acceleration data through an inertial measurement unit and uploads the data to a central processing module, and the central processing module conducts kinematics modeling on the data to generate attitude change initial feature vectors; receiving water flow velocity data of a Doppler velocimeter and pressure data of a depth sensor, and performing flow field interference compensation optimization on the attitude change initial feature vector based on the data to generate an anti-interference attitude feature vector; fusing the anti-interference attitude feature vector and a historical attitude estimation result to generate space-time fusion attitude state representation, and then resolving a real-time Euler angle through a nonlinear filter; and matching a corresponding attitude stability control instruction from the navigation strategy library according to the real-time Euler angle, and issuing the attitude stability control instruction to a propeller execution unit. The method can effectively cope with underwater complex environment interference and guarantee stable operation of the underwater vehicle.

The invention relates to the technical field of non-electrical variable control or regulation systems, and discloses a temperature rising blanket temperature closed-loop control method and system based on human body position recognition feedback, and the method comprises the steps: calculating a form compactness coefficient representing a heat dissipation boundary based on pressure data, and calling a mapping function to convert the form compactness coefficient into an equivalent thermal impedance estimated value; introducing the estimated value and dynamically constraining the forward channel gain of the temperature closed-loop controller according to a negative correlation strategy; according to the method, a real-time mapping mechanism of the contact topological characteristics and the control gain is constructed, so that the problem of model mismatch caused by body position change of the controlled object is solved; when the high-impedance contact state is detected, energy input is automatically reduced, the local heat accumulation effect is restrained, and the convergence and safety of the thermodynamic control process under the variable boundary condition are ensured.

The invention relates to the technical field of equipment monitoring, in particular to a method and a system for collecting and monitoring production data of automotive trim injection molding equipment. Comprising the following steps: firstly, acquiring real-time operation state data from injection molding equipment through a communication node, generating an equipment state data set, and constructing a parameter coupling model through a time sequence analysis method; when the temperature parameter fluctuation exceeds a preset threshold value, fusing the pressure data to generate optimal configuration, transmitting the optimal configuration to related equipment through an interactive network, and determining a linkage parameter set; thirdly, the linkage parameter set is processed through a classification method, a deviation index is obtained, a synchronization instruction is generated according to deviation, it is ensured that beats between the devices are consistent, and a unified production cycle is formed; and finally, through the prediction model, extracting production cycle data, predicting parameter fluctuation, adjusting equipment parameters, and determining final process configuration. The problems of beat deviation and parameter fluctuation between injection molding equipment in production are solved, the production process is optimized, and the production efficiency and the product quality are improved.

The invention discloses a battery cell simulation type state sensing and feedback method and system, and the method comprises the steps: providing a simulation battery cell detection device which flows on a production line along with an actual battery cell; the device is provided with a shell matched with an actual battery cell in boundary dimension, a film pressure sensor covering the surface of the shell, and a built-in collector, in the circulation process, the sensor collects pressure data applied by the production line equipment in real time; the built-in collector stores data and sends the data to the upper computer processing system in a wireless mode; and the upper computer processing system receives and analyzes the pressure data, judges abnormity according to a preset pressure threshold value and generates a report. By simulating the real stress condition of the battery cell, comprehensive, real-time and automatic monitoring of the pressure state in the production process is realized, the abnormal reason can be accurately positioned, the problems of lack of monitoring means and difficulty in problem tracing in the prior art are solved, the product quality and the production efficiency are effectively improved, and the potential quality hazard is reduced.

The invention provides an intelligent detection method and system for the tensile property of a plastic cable tie. Gradient temperature rise control is executed through the carbon fiber heating tube array, microscopic deformation fluctuation characteristics are captured through the double-grating displacement sensor, and temperature field distribution is collected through the infrared thermal imager. Establishing dynamic correlation between deformation and a temperature field to generate a three-dimensional space-time atlas, identifying a migration path of a thermal aggregation area, recording a standard path, and calculating a thermal anomaly coefficient in combination with pressure data; analyzing temperature conduction hysteresis to generate a heat conduction parameter, and dynamically correcting a threshold value by cross-modal correlation with an abnormal coefficient; a structure degradation index is generated based on the migration path, mapping between the structure degradation index and a temperature field is established to generate a thermal damage compensation coefficient, an abnormal coefficient is calibrated accordingly, and grading early warning is triggered in combination with the path deviation degree. According to the technical scheme provided by the invention, multi-scale damage evaluation from thermodynamic associated data acquisition to molecular chain fracture inversion is realized, and the high-temperature tensile failure critical point is accurately pre-judged through a dynamic calibration early warning mechanism.

The invention relates to the technical field of lithium ion battery safety, and discloses a lithium ion battery thermal runaway multi-cascade emergency response prevention system. The system comprises a thermal signal monitoring module, a thermal diffusion prediction module, an emergency suppression module, a pressure balance module and a multi-mode linkage module. The thermal signal monitoring module obtains multi-dimensional temperature data, identifies abnormity and generates a thermal runaway early warning signal; the thermal diffusion prediction module combines the early warning signal and the battery module structure parameters to calculate the thermal propagation path and rate and generate a prediction result; the emergency suppression module matches a suppression medium according to a prediction result, and calculates a dose putting strategy to form a suppression scheme; the pressure balance module obtains pressure data, analyzes the change rate and distribution, adjusts a pressure release valve strategy in combination with a suppression scheme, and generates a pressure regulation and control instruction; and the multi-mode linkage module integrates data and a scheme, and generates a system-level emergency response instruction based on a pressure regulation and control instruction to realize thermal runaway multi-level joint prevention.

The invention discloses an intelligent pressure maintaining control method for a thin-wall product of an injection molding machine based on mold pressing feedback. The intelligent pressure maintaining control method comprises the following steps: (1) constructing a hardware layer; (2) configuring a software layer; (3) a mold cavity pressure acquisition module acquires original pressure data in the injection mold cavity in real time through a pressure sensor; (4) the data preprocessing module carries out filtering processing on pressure signals generated by the pressure data, and (5) the dynamic pressure maintaining control module dynamically adjusts pressure parameters and pressure maintaining time by receiving the real-time pressure data, product wall thickness parameters, mold structure parameters and injection molding process parameters, generates a pressure maintaining control instruction and sends the pressure maintaining control instruction to an injection molding machine control system; driving the injection molding machine to execute pressure maintaining action; (6) the dynamic pressure maintaining control module associates and stores the pressure change curve and the parameter adjustment record in the pressure maintaining process in a feedback learning module, and optimization calculation is carried out; and (7) the man-machine interaction interface displays the pressure change curve, the pressure maintaining control parameters and the forming state of the injection mold cavity in real time.

The invention discloses a time-phased intelligent pressure regulation management method and system for secondary water supply, and belongs to the technical field of water supply management, and the method specifically comprises the steps: dividing a day into a water consumption peak period and a water consumption low period based on historical water consumption flow data, and determining the starting and ending time nodes of each time period; through hydraulic calculation and actual water supply pressure monitoring, a pressure control point at the tail end of the water supply system is locked, and water supply pressure data of the point are collected in real time; in the water consumption peak period, a peak pressurization control strategy is executed; in the water consumption low-peak period, analysis is conducted according to historical water supply pressure data of a tail end pressure control point, and a low-peak period pressure safety threshold value is set; a pressure instruction is sent to a control system according to the threshold value, and water supply equipment is controlled to output pressure according to the threshold value; the water supply pressure of a tail end pressure control point is monitored in real time, and when the pressure is lower than a low-peak-period pressure safety threshold value, pre-alarm is triggered, and pressure changes are continuously recorded; and if the pre-alarm state is not finished continuously, switching to a peak-period peak pressurization control strategy.

The invention discloses a pressure injury risk early warning method and equipment, and relates to the technical field of medical monitoring instruments. The pressure sensor array layout can be self-adaptive to the body shape of a patient, a key osseous process part is covered, no monitoring dead angle is ensured, a temperature compensation mechanism is built in, environmental interference is reduced, the measurement precision is improved, an abnormal pressure threshold value can be dynamically set based on individual medical record data, data verification is performed before early warning is triggered, false alarm is avoided, and the array structure is light, thin and flexible. The mask is comfortable to fit skin, supports long-term wearing and is easy to disinfect and maintain; environment temperature and humidity parameters and pressure data are subjected to fusion analysis, the influence of a high-temperature and high-humidity environment on skin microcirculation can be recognized, the risk of pressure sores can be evaluated in an auxiliary mode, a calibration algorithm is built in a sensor, environment fluctuation interference is reduced, and data reliability is ensured; and the early warning unit comprises sound-light alarm equipment which supports a multi-stage alarm mode and is used for triggering an early warning signal and pushing information when the risk index exceeds a threshold value, so that the early warning timeliness is ensured and the false alarm risk is reduced.

The invention discloses a neural network-driven battery acoustic pressure monitoring and early warning method and system, and aims to realize omnibearing analysis and fault early warning of battery pressure signals by constructing a multi-stage deep learning framework. According to the method, a neural network coding technology is adopted, and causal features of pressure data are extracted through reverse time evolution and chaotic attractor projection; identifying a pressure periodic rhythm by using a time convolutional network, establishing a rhythm reference model and generating an abnormal index; a mutual information resonance mechanism is introduced, and the information correlation degree between data is determined through phase slip analysis; a structured feature map is generated through variational echo reconstruction, and the evolutionary process of the features is captured through a diffusion model; predicting a phase change critical point based on a reformed group theory, converting early warning parameters into an oscillator network, and analyzing synchronization characteristics; and finally, graded early warning is realized through singular value decomposition and activation intensity calculation, so that early abnormal symptoms of the battery can be accurately identified, and reliable technical support is provided for safety management of the battery.

The invention relates to the technical field of annealing furnace control, in particular to an annealing furnace pressure control method and system.The method comprises the steps that pressure data in all furnace sections of a continuous bright annealing furnace, temperature data of furnace walls at all the furnace sections and flow data at outlets of pressure regulating valves used at all the furnace sections are collected in real time; presetting an adjusting period of a proportion parameter in a PID controller used by a pressure adjusting valve used by each furnace section to obtain each adjusting moment of the proportion parameter; obtaining each synchronous change moment of each furnace section in a time period between each adjustment moment and an adjacent previous adjustment moment, and obtaining a pressure characteristic value of each furnace section at each adjustment moment; obtaining a trend similar characteristic value of each furnace section at each adjustment moment; and obtaining the value of the proportion parameter in the PID controller in the time interval between each adjustment moment and the next adjustment moment adjacent to the adjustment moment so as to control the furnace pressure in each furnace section. The invention aims to improve the control precision of the furnace pressure.

The invention discloses a fire hydrant pressure adaptive regulation and control system based on Internet of Things data, and relates to the technical field of urban water supply pipe network fire hydrant pressure control, and the system comprises a multi-source data collection module which is used for collecting fire hydrant pressure time sequence data in real time from a plurality of Internet of Things nodes deployed in an urban water supply pipe network, and historical pressure data, pipeline topological structure data and user water consumption mode data of the water supply network are obtained from a historical database. Real-time and historical data are integrated through the multi-source data acquisition module, a comprehensive basis is provided for pressure regulation and control in combination with a pipe network topology and a user water consumption mode, and the integrity of data support is improved; the pipe network state prediction module is used for extracting multi-dimensional features and predicting pressure changes by means of an LSTM model, the pressure abnormal risk can be recognized in advance, and hysteresis of traditional regulation and control is avoided.

The embodiment of the invention provides a breathing machine inspiration detection method and device and a computer readable storage medium, and the method comprises the steps: collecting first pressure data and first flow data in a current breathing period, and calculating a target flow baseline according to the first pressure data, the first flow data and a preset fitting formula; calculating a virtual traffic baseline according to the target traffic baseline and the first traffic data; if it is judged that the inspiration triggering state is not reached or the virtual inspiration flow change rate in the inspiration triggering state is smaller than a preset flow change threshold value, second pressure data and second flow data of the next respiratory cycle are continuously collected, and the virtual inspiration flow change rate is calculated; and judging that inspiration is detected until the detected virtual inspiration flow change rate is greater than or equal to the preset flow change threshold value. According to the invention, the phenomena of inspiration detection delay, group delay and the like can be avoided, the difference between detected inspiration and actual inspiration is reduced, and the sensitivity of inspiration detection is improved.

The invention discloses an IoT fire-fighting spraying pipe network intelligent micro-leakage alarm method and system based on time period pressure drop and pressure fluctuation analysis. According to the method, a multi-node IoT pressure sensor is deployed to collect pipe network pressure data in real time, and a pressure drop-fluctuation joint detection model is constructed by adopting frequency domain analysis combining dynamic time period division and pressure drop rate calculation with wavelet transform. Multi-modal feature fusion is realized by using a Transform-LSTM-GNN hybrid model, graded alarm is triggered through a dynamic threshold value, and a leakage point is positioned based on a pipe network topology and a pressure gradient. The system comprises a data acquisition module, a feature analysis module, an AI model training module, an alarm decision module, a maintenance scheme generation module and the like, and a'monitoring-analysis-alarm-maintenance-optimization 'closed loop is formed. Time period dynamic division is realized through a DTW-BIRCH algorithm, and the model is optimized through a multi-task loss function joint; topological feature fusion is enhanced through a graph attention network; and the detection precision is continuously improved through an incremental learning mechanism, and early-stage accurate identification of tiny leakage can be realized.

The invention relates to the technical field of parcel sorting, in particular to a parcel detecting and sorting method and system based on machine vision. According to the method, historical parcel motion trajectory data and real-time video information are fused, a category and weight differentiation trajectory model is constructed, sudden offset is captured in combination with computer vision, the trajectory is accurately predicted, and the attitude offset is obtained. The initial fixed parameter of the actuator is dynamically adjusted to be a first action parameter based on the offset, a compensation coefficient is generated through trajectory tracking error verification, and a second action parameter is obtained; meanwhile, friction coefficient changes are calculated through the real-time track and pressure data, a dynamic response coefficient is determined, and finally a multi-sorting-port cooperation instruction is generated to achieve path dynamic distribution. According to the process, through historical and real-time data fusion, dynamic parameter adjustment, closed-loop feedback and multi-system cooperation, the problems of traditional sorting static presetting, parameter blind adjustment, resource allocation imbalance and the like are solved, and the sorting precision, adaptability and efficiency are improved.

The invention provides an intelligent monitoring system and method for mine drilling, and belongs to the field of mine drilling monitoring, and the system comprises a multi-mode sensor group which is arranged on drilling equipment and is used for collecting a plurality of drilling parameters in real time, the multiple drilling parameters comprise any multiple of drilling depth data, attitude measurement data of drilling equipment, pressure data of a drilling hole wall, crack image data of the drilling hole wall, vibration data in the drilling process and radial offset of the drilling hole wall; the data transmission module is connected with the multi-mode sensor group and is used for transmitting the drilling parameters to the processing module; the processing module is used for generating drilling state monitoring results according to the drilling parameters, and the drilling state monitoring results comprise any multiple of the drilling depth, the drilling direction, the drilling wall stability and the drilling shape. According to the invention, through synchronous acquisition and fusion analysis of multi-source heterogeneous sensing data, comprehensiveness and reliability of drilling state monitoring are improved.

The invention discloses a water supply system scheduling optimization method and device, electronic equipment and a storage medium, and relates to the field of intelligent scheduling of water supply systems. The method comprises the steps of collecting and preprocessing multi-dimensional data to obtain a data set; the pressure data is analyzed from the space-time dimension, and the unfavorable points and the pressure requirements thereof are accurately identified; the method comprises the following steps: constructing a water volume prediction model by adopting a time sequence model, and constructing a total water head difference prediction model by adopting a Light GBM gradient boosting tree in combination with a MultiOutputRegressor multi-output regression framework; constructing a minimum total water production cost objective function based on a prediction result, and outputting an optimal scheduling scheme by combining water volume and pressure constraint iterative optimization; and establishing a model updating mechanism to ensure dynamic adaptation of the strategy. According to the method, the problems of insufficient pressure guarantee, extensive cost control and weak model practicability and generalization ability are solved, the inherent contradiction that a traditional mechanism model is high in complexity and a pure data driving pressure prediction model is poor in generalization and lacks physical significance is overcome, and safe, stable and efficient intelligent technical support is provided for a water supply system.

The invention discloses an oil storage tank oil-water interface measurement system and method and a storage medium, and relates to the technical field of oil-water interface measurement, and the system comprises a data acquisition module, a data processing module, a decision support module and a data security module. According to the oil-water interface measuring system and method for the oil storage tank and the storage medium, a three-dimensional sensing network (distributed sensor network) is constructed by adopting a pressure sensor array and phased array ultrasonic scanning technology and combining dielectric property detection of a microwave dielectric constant sensor; the pressure, the dielectric constant, the liquid level and the vibration data in the tank body are obtained, basic data are provided for subsequent processing, the comprehensiveness of data obtaining can be improved through the sensing network, the pressure data are dynamically calibrated through Takagi-Sugeno fuzzy logic, the influence of temperature drift is effectively eliminated, and the accuracy of data obtaining is improved. The ultrasonic signals are subjected to feature enhancement through the generative adversarial network, and the emulsion layer boundary is accurately recognized.

The invention relates to a micro-pressure oxygen cabin self-adaptive pressure control method and system based on iterative optimization, belongs to the technical field of pressure control, and aims at solving the problems that existing micro-pressure oxygen cabin pressure control depends on manual repeated debugging, the efficiency is low, and self-adaptive optimization cannot be achieved. The method comprises the steps that in the pressure stabilizing stage of the micro-pressure oxygen cabin, the pressure in the cabin is monitored in real time, and when the pressure exceeds the upper limit or the lower limit of a preset pressure interval, a pressure stabilizing wave crest section or wave trough section opening degree adjusting algorithm is executed so as to adjust the opening degree of an electric exhaust valve. And after the single oxygen therapy process is finished, executing an iterative optimization step: automatically analyzing the pressure data of the current pressure stabilization stage by the system, and iteratively updating the control parameters for the next oxygen therapy process according to the pressure data. According to the method, self-learning and evolution of control parameters are realized through a closed-loop mechanism of real-time adjustment and redisk optimization, the parameters can be quickly converged to the optimal state within multiple times of operation, the debugging period is shortened, and the stability of pressure control is improved.

The invention discloses an adaptive multi-angle elbow airtightness detection method, which comprises the following steps: arranging a flexible pressure sensor array and a micro inertial measurement unit on the inner wall of an elbow, collecting multi-point sealing pressure and attitude signals in real time, fusing geometric parameters and a spatial mapping relation, and completing denoising, normalization and multi-curvature region mapping of pressure data; a distributed pressure-attitude database is combined, a function model associated with sealing pressure and pipeline geometric parameters is established, inflation, exhaust and deformation of each node are dynamically adjusted through an intelligent closed-loop regulation and control algorithm, collaborative optimization of multi-node pressure and a fitting state is achieved, and the sealing performance of the pipeline is improved. According to the method, the accuracy and the self-adaptive adjusting capacity of sealing pressure distribution under a complex bent pipe structure are effectively improved, the tiny leakage real-time positioning and self-learning optimization capacity is achieved, and the method is suitable for sealing quality guarantee of various types of curved surface pipelines.

The invention belongs to the technical field of intelligent monitoring of safety belts, and relates to an intelligent monitoring method and system for the dangerous state of a high-altitude operation safety belt and electronic equipment. After alignment, inputting a safety belt intelligent dangerous state monitoring model to carry out safety belt intelligent dangerous state monitoring; the safety belt intelligent dangerous state monitoring model comprises a sensing field adaptive convolution module, a vibration spectrum attention enhancement module, a cross-modal gating fusion layer, a time domain convolution gating circulation unit, a multi-scale time sequence pooling layer and a full connection classifier. According to the invention, the problems of insufficient data alignment precision, limited feature extraction capability, low efficiency of multi-modal fusion, incomplete sequence dependence capture and the like in the existing aerial work safety belt monitoring technology can be solved.

The invention discloses a layered micro-channel liquid cooling method and system based on a magnetic suction interface, and relates to the technical field of micro-channel liquid cooling structures. The layered micro-channel liquid cooling method and system based on the magnetic suction interface comprises the following steps: S1, acquiring and preprocessing temperature characteristic data and fluid flow velocity pressure data, and constructing a standardized thermal load trend data set; s2, the cooling liquid supply state of each layer is evaluated, and the liquid supply rhythm of the corresponding layer is dynamically adjusted; s3, analyzing the cooling demand intensity of each layer, and driving the working frequency of the branch pump of the corresponding layer to change; and S4, evaluating the coordination state of each layer in the process from liquid supply to heat dissipation to liquid return, and dynamically adjusting the liquid cooling control strategy. The problems that in a multi-layer server structure, an existing liquid cooling distribution path cannot achieve dynamic flow adjustment according to the thermal load difference of each layer of equipment, so that cooling resource distribution is uneven, and energy consumption waste is serious are solved.

The invention discloses a combined sealing device frictional wear and external leakage test bed and a test method thereof, and aims to solve the problem that the existing device cannot accurately know the sealing wear. The test bench comprises a rack, a test piece, a stay wire sensor, an oil pipe and a hydraulic system, wherein an oil tank is arranged in the rack. The test piece comprises a test sealing assembly and a plunger, and the stay wire sensor is connected with a sealing combination structure. During testing, the hydraulic system drives the plunger and the sealing assembly to move relatively, the pull wire sensor measures displacement, friction force is calculated by combining pressure data to reflect abrasion, and the oil groove collects leaked oil. According to the invention, comprehensive testing of friction, wear and leakage is realized, sealing performance and wear rules under multiple working conditions can be analyzed, the structure is convenient to observe and operate, and optimization of the sealing structure is facilitated.

The invention relates to the technical field of methanol combustion optimization, in particular to a methanol fuel combustion multi-target collaborative optimization method and a self-adaptive energy consumption regulation and control system, and solves the problem of balance between model precision and real-time optimization efficiency. The method comprises the following steps: constructing a special digital twinborn model, integrating a parameterized chemical reaction dynamic sub-model, a calibration interface dynamically coupled with real-time in-cylinder pressure data and a flow field simulation module, and continuously and dynamically updating; parallel simulation control parameter combination is carried out by using the dynamic model, and multi-target performance including at least two items of heat efficiency, NOx emission and combustion stability is predicted; outputting an optimization parameter set through Pareto frontier analysis of a multi-objective optimization algorithm; and finally, combining the parameter set with the real-time state of the model to select a control parameter to adjust the operation of the equipment, thereby realizing multi-target collaborative optimization and self-adaptive energy consumption regulation and control. According to the method, the system working condition adaptability, the multi-target optimization fineness and the energy consumption regulation and control intelligent level are improved.

The invention relates to the technical field of intelligent control, in particular to a coke oven gas collecting tube pressure dynamic adjusting method and system based on intelligent control, and the method comprises the steps: obtaining pressure data from a gas collecting tube through a sensor, and storing the pressure data in a time sequence format to obtain a pressure deviation initial sequence; according to the pressure deviation initial sequence, calculating a data difference value and carrying out smoothing processing to obtain a change rate sequence; analyzing the change trend of the change rate sequence, and calculating a secondary difference value to obtain a change acceleration sequence; according to the change rate sequence and the change acceleration sequence, predicting a pressure deviation development trajectory by adopting a filtering method to obtain a predicted deviation trajectory; and if the predicted deviation trajectory exceeds a preset threshold range, obtaining a deviation influence evaluation value through historical data comparative analysis. The problem that the pressure control precision of the coke oven gas collecting pipe is poor in the high-temperature and high-pressure environment is solved, and the pressure adjusting precision of the gas collecting pipe in the high-temperature and high-pressure environment is improved.

The invention relates to the technical field of intelligent manufacturing, in particular to an insect protein extraction automatic production line and quality tracing method based on machine vision, which comprises the following steps of: acquiring an image and pressure data advancing time sequence, comparing a path and pressure fluctuation, identifying an abnormal section image frame, extracting a boundary and gray scale difference, and marking a structure defect. And matching action time, sorting station abnormity, combining a frame sequence and a chamber number, searching a traceability table record, updating an abnormal state field, and forming a quality traceability result set. According to the method, an image particle path and a pressure intensity signal are propelled in parallel in a time dimension, the synchronous recognition capability of image change and a physical state is enhanced, a structural anomaly judgment basis is constructed by particle boundary pixels and gray scale difference, and image time and a push rod action form uninterrupted binding through natural superposition; receiving frames and cavity serial numbers are classified into a unified serial number logic according to a time sequence, abnormal identifiers and channel information are embedded into batch fields in parallel, images are communicated, and abnormal tracing and positioning are carried out.

The invention relates to the technical field of safety monitoring of a nuclear reactor, and particularly discloses a multi-region pressure overrun leakage monitoring method for a reactor pressure vessel. The method comprises the steps of multi-region pressure data acquisition, real-time pressure field model construction, dynamic pressure field baseline modeling, pressure overrun anomaly detection, leakage source accurate positioning and evaluation based on spatial gradient analysis, topological structure mapping and multi-sensor data fusion, and graded early warning and response. By the adoption of the technical scheme, real-time and fine monitoring of pressure distribution in the reactor pressure vessel can be achieved, the rapid and accurate judgment and positioning capacity of leakage events is improved, the operation safety and reliability of nuclear power facilities are enhanced, and the reliability of the nuclear power facilities is improved. The defects of independent monitoring, local accurate positioning and complex working condition adaptability of multi-region pressure over-limit leakage in the prior art are overcome.