5380 results about "Pressure data" patented technology

The invention provides an intelligent temperature adjusting method and system for an injection molding mold. The method comprises the steps that real-time multi-source sensor data flow is generated by collecting temperature data and pressure data of all areas of the injection molding mold; performing feature extraction on the real-time multi-source sensor data flow to obtain temperature field distribution features; a temperature field distribution prediction model is obtained through training based on the temperature field distribution characteristics and a differential private Bayesian optimization algorithm, and then a mold temperature prediction result is output through the model; performing feature fusion on the mold temperature prediction result and the pressure data, constructing input features of a neural network controller, training based on the input features and a positive definite curvature learning method to obtain the neural network controller, and generating a power control signal of a heater and a flow control instruction of a water cooling system through the neural network controller, and based on the control signal and the instruction, the temperature field of the mold is dynamically controlled through the adjustable clearance compensation mechanism. According to the invention, the precision and response speed of temperature adjustment are improved.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention relates to the technical field of geophysical exploration, and particularly discloses a tunnel surrounding rock stability automatic monitoring and early warning method and system. The method comprises the steps that surrounding rock microseismic signals, convergence displacement and osmotic pressure data are collected through a space-time synchronization array to generate a multi-source fusion data set; based on a dynamic constitutive model separation frequency dispersion effect, extracting correlation characteristics of a microseismic event rate and a displacement rate; generating an instability energy density index; dynamically switching impact or creep damage criteria according to the deformation behavior of the surrounding rock; inputting the indexes into corresponding criteria to generate a damage state result; and if the result exceeds the threshold value, verifying the spatial focusing property of the micro-seismic event cluster, and outputting an early warning instruction when the focusing degree exceeds the critical value. Through multi-source fusion and a dynamic criterion mechanism, the accuracy of tunnel surrounding rock instability early warning is remarkably improved.

The invention discloses an intelligent control method and system for tunnel loudspeakers, and relates to the technical field of tunnel audio control, environmental parameters in a tunnel are collected by adopting a mode of deploying sampling equipment in a distributed manner, and data preprocessing is performed in a targeted manner for different environmental parameters; a sound propagation model is established, and attenuation and delay of sound in different environments are simulated. According to the intelligent control method and system for the tunnel loudspeakers, various temperature and humidity sensors are arranged in the tunnel, and the absolute humidity is calculated in combination with the air pressure data, so that the sound velocity is accurately corrected, and the phase difference of the multiple loudspeakers is reduced; an adaptive Kalman filtering algorithm is adopted to process wind speed data, and reliable input is provided for a sound propagation model; a deep reinforcement learning algorithm is used to carry out collaborative optimization on parameters such as amplitudes and directional angles of multiple loudspeakers, a Bayesian network is used to detect loudspeaker faults, and Delaunay triangulation and a distributed consistency algorithm are combined to realize rapid fault reconstruction.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention relates to the technical field of cable fault detection, in particular to a cable channel pressure change monitoring system based on a sensing network, and the system comprises a pressure signal decomposition module which collects multi-point pressure readings based on pressure signal data collected by the sensing network, and carries out the step-by-step frequency division of signals through segmented filtering. According to the method, pressure data are collected at multiple points, segmented filtering and layered decomposition technologies are combined, the levels of signal analysis are refined, pressure characteristics of different frequencies are captured, monitoring comprehensiveness is ensured, energy entropy calculation is combined with a wavelet decomposition method, deep description of signal non-stationary characteristics is achieved, and the detection sensitivity of abnormal changes is improved. Recursive analysis is adopted in time sequence prediction, dynamic error window correction is combined, accurate prediction of future pressure changes is provided, sudden change detection is based on dynamic threshold and time point change rate judgment, abnormal conditions are quickly responded, trend correction is combined with offset data and measurement data, and the prediction precision and correction capacity of the pressure change trend are effectively improved.

The invention relates to the technical field of underground pipe gallery intelligent monitoring, and discloses an underground pipe gallery data real-time processing system based on edge computing, which comprises a dynamic sensing primitive library abstracting pipeline pressure and video monitoring multi-source data into primitive units containing associated weights, the primitive recombination module dynamically adjusts the coupling relation between primitives through a function according to the rainfall environmental parameters, so that the association weight of the video texture and the pressure data is adaptively enhanced; according to the system, through a dynamic coupling mechanism driven by environment feedback, leakage gradual change characteristics which are difficult to capture by a traditional fixed threshold are effectively identified; the edge collaborative network realizes cross-node knowledge migration, and automatically triggers high-precision sampling of adjacent nodes when local abnormality occurs, so that a self-organizing diagnosis cluster is formed. According to the invention, through a composite architecture of dynamic primitive recombination and edge collaboration, the problems of response lag and high false alarm rate of a traditional monitoring system are avoided, and the technical span from passive monitoring to active prediction of the underground pipe gallery is realized.

The invention discloses a constant-temperature and constant-humidity test box temperature control system based on a multistage PID algorithm and a control method thereof. The constant-temperature and constant-humidity test box temperature control system comprises an environmental parameter detection module, an in-box parameter detection module and a multistage PID controller, wherein the environmental parameter detection module is used for collecting temperature, humidity and air pressure data outside a test box in real time; the in-box parameter detection module is used for collecting multi-dimensional environmental parameters inside the test box; when the failure of any sensor is detected, starting a fault self-diagnosis module, replacing the data of the failure sensor with a data interpolation method of a side sensor, and recording the deviation compensation amount at the same time. A multi-stage PID control architecture is adopted, and feedforward compensation, parameter adaptive optimization and fault redundancy strategies are combined, so that high-precision and anti-interference control of the temperature and humidity of the test box is realized.

The invention belongs to the technical field of collaborative robots, and particularly discloses and provides an anti-explosion mechanical arm positive pressure control system and method.The system comprises a mechanical arm motion tracking module, an environment data monitoring module, a configuration adjustment analysis module, a pressure adjustment analysis module, a database and a pressure adjustment execution terminal. The mechanical arm motion tracking module is designed to track the motion state of the mechanical arm and record motion data, a key part pressure sensor is started to monitor pressure data, and the correlation change of motion and pressure can be captured. The environment data monitoring module monitors environment parameters of a working area in real time, and environment changes are comprehensively mastered. The all-dimensional monitoring capability of the system is improved, factors influencing positive pressure control can be obtained more comprehensively, meanwhile, configuration adjustment judgment is carried out in combination with environment, movement and pressure data, adjustment values are dynamically determined, and intelligent decision making and self-adaptive adjustment of mechanical arm positive pressure control are achieved.

A porous media grouting test system and method taking temperature effects into consideration. The system comprises a porous media formation simulation model (1), a grouting system (2), a water level adjustment system, a formation temperature control system and a data analysis system, wherein the porous media formation simulation model (1) is used for simulating a porous media formation; the water level adjustment system and the formation temperature control system are used for simulating the conditions of different water saturation levels and different water temperatures and ground temperatures; and the data analysis system is used for comparing different time-series data collected by sensors such as a temperature sensor and a pressure sensor with data such as temperature data and pressure data that is obtained by means of coupled solving of a heat transfer equation and a momentum equation, so as to verify and analyze a grouting diffusion path. Free simulation of different water saturation levels is realized by means of a water level adjustment system, and simulation of different ground temperatures is realized by means of a formation temperature control system, thereby realizing the research on grouting diffusion under different water levels and different water temperatures and ground temperatures.

The invention relates to the technical field of process optimization, and discloses an optimization method and system for a graphite sagger compression molding process. The method comprises the following steps: determining and analyzing particle size distribution, specific surface area, porosity and viscosity-temperature characteristics of graphite powder and a binder to obtain raw material characteristic parameter data; modeling is conducted on the migration behavior of the binder and the deformation characteristic of the graphite powder in the pressing process, a material rheological model is obtained, meanwhile, a multi-point pressure sensor system is arranged on the surface of a pressing mold, and target pressure data are collected; constructing a pressure distribution feature vector and a material flow state prediction result; performing adaptive adjustment on the initial pressing parameters to obtain a segmented pressure curve and a local pressure compensation strategy; and performing multi-objective optimization on the graphite sagger pressing path to obtain an optimal pressing process parameter combination. The method is combined with material flow state prediction, accurate control over the pressing process is achieved, and the intelligent level of the graphite sagger pressing forming technology is improved.

The invention relates to the technical field of vacuum extraction control, and discloses a multi-stage vacuum extraction control system which comprises a central processing unit, a multi-stage vacuum pump set control module, a vacuum degree dynamic monitoring module and the like. The multi-stage vacuum pump set control module coordinates all stages of pumps to work according to a preset vacuum degree target value, and the vacuum degree dynamic monitoring module collects pressure data in real time and judges abnormity. The pressure gradient adjusting module adjusts the extraction rate of the adjacent pump to maintain the vacuum gradient stable when the pressure is abnormal, and the abnormity response module controls starting and stopping or rate correction of the pump according to the abnormity level. In addition, the system further has the functions of vacuum pump efficiency evaluation, pressure feedback compensation, leakage detection, redundancy switching, historical data backtracking, collaborative optimization and the like. The system can accurately control the vacuum degree, efficiently deal with abnormities and realize equipment state monitoring and optimization, and is widely applied to the fields with high requirements on the vacuum degree, such as semiconductor manufacturing and vacuum coating.

The invention relates to the field of computer vision and industrial safety monitoring, and discloses a pipe gallery pipeline key connection point leakage monitoring system and method based on AI vision, and the system comprises a data collection module, an intelligent analysis module, a 3D modeling module, a space-time verification module and an alarm prediction module. Real-time detection and three-dimensional accurate positioning of a leakage area are realized in combination with neural network optimization driven by physical simulation parameters; further through optical flow tracking and fluid mechanics verification, false alarm events of non-physical rules are screened out; and based on Bayesian decision and diffusion equation prediction, generating a leakage risk heat map and triggering graded alarm. The method solves the technical problems of low leakage detection precision, high false alarm rate and inaccurate positioning in a complex pipe gallery scene, has the advantages of high environmental adaptability, high anti-interference capability and prospective risk pre-judgment, and is suitable for intelligent safety monitoring of underground pipe galleries and petrochemical engineering pipelines.

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 provides a while-drilling formation pressure early warning method and device based on model prediction, equipment and a medium, and relates to the technical field of low-carbon mining, and the method comprises the steps: in a pre-drilling stage, based on seismic data, adjacent well historical data and design data, analyzing and generating prediction risk data of a target monitoring well; in the drilling process, actual formation pressure data are calculated in real time by using drilling data collected for a target monitoring well in real time, and the actual formation pressure data are compared with the predicted risk data; in the drilling stage, a pressure safety window is depicted based on the predicted risk data, the cycle equivalent density of the target monitoring well calculated in real time is compared with the safety window, and the drilling safety risk is recognized and analyzed; and after drilling is finished, summarizing the predicted risk data, the actual formation pressure data and the drilling safety risk, and updating and iterating the model algorithm through the summarized data. The method has the effect of improving the accuracy of formation pressure early warning while drilling in the petroleum drilling process.

The invention discloses a tubular pile reinforced composite foundation bridgehead differential settlement monitoring method and system, and belongs to the technical field of foundation treatment and foundation engineering. Comprising the following steps: acquiring soil pressure data, settlement data, displacement data and pore water pressure data of a pipe pile reinforced composite foundation bridgehead, respectively performing feature extraction, and adaptively fusing a plurality of feature extraction results by utilizing correlation to generate a fused feature vector; the fusion feature vector is processed through a trained settlement prediction model, and a settlement prediction result is obtained with spatial-temporal features as guidance; wherein the settlement prediction model comprises an adaptive residual connection LSTM network layer, a hierarchical space-time attention mechanism network layer, a full connection layer and a linear layer which are connected in sequence. Comprehensive monitoring of a complex foundation environment can be realized, and the precision of settlement prediction is improved; the problems that the settlement mechanism is not deeply considered in existing settlement monitoring, and the settlement prediction accuracy needs to be improved are solved.

A pore pressure prediction method for fine-grained hybrid sedimentary rock based on variable P-wave velocity includes: carrying out a P-wave velocity test of rock under different effective stresses, determining a function form of the relationship between acoustic velocity and effective stress, fitting the parameters in the function by optimization, and establishing an evaluation model of effective stress influence term by lithology. According to the measured formation pressure data and the effective stress influence term model, the acoustic velocity background value is deduced, the logging curve is preferred, and the acoustic velocity background value logging evaluation model is established. According to the logging curves of DT and DEN, combined with the lithology identification results, the acoustic velocity background value is calculated, and then the effective stress of the formation is obtained, and then the pore pressure is determined according to the overburden pressure.

The invention relates to the technical field of pressure detection, in particular to an oil well pressure anomaly detection method and system based on deep learning, and the method comprises the following steps: collecting a control signal and pressure data, calculating a slope difference and fluctuation to mark a sudden change point, extracting a multi-dimensional feature group to generate a training sample, and constructing a deep learning model to recognize pressure difference anomaly. The oil well pressure abnormal time period is predicted in real time. According to the method, accurate attribution of abnormal fluctuations in the oil well operation process is achieved by constructing the time sequence corresponding relation between control signals and pressure difference sudden change, the target pertinence of pressure change recognition is improved, a sliding structure extracts multi-dimensional dynamic characteristics, the capacity of capturing wellhead or underground pressure micro-amplitude abnormity is enhanced, and the training sample is combined with a control behavior label, so that the accuracy of the abnormal fluctuations in the oil well operation process is improved. The distinguishing capability of the model to different abnormal forms is optimized, the adaptability under complex working conditions is improved, a real-time prediction mechanism ensures that oil well pressure state recognition has continuity, rapidness and high confidence, and risk early warning and operation guarantee in the oil extraction process are effectively supported.

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 provides an intelligent pressure management system, method and terminal for geological sequestration of carbon dioxide, and the system comprises the steps: collecting the pressure data of the interior and peripheral key positions of each geological reservoir in real time through a pressure monitoring subsystem, and then carrying out the data preprocessing and feature extraction through a data processing and analysis subsystem; and predicting the reservoir pressure change trend in a specific time period in the future. If the predicted reservoir pressure change trend pressure is too high, high-risk related information is transmitted to the intelligent regulation and control subsystem, and the intelligent regulation and control subsystem regulates and controls the reservoir pressure by regulating the flow of a carbon dioxide injection well and a production well or starting a pressure release device according to a preset strategy and dynamically regulates a regulation and control mode according to pressure data. Meanwhile, the communication and monitoring subsystem transmits system operation data to a remote monitoring terminal in real time, so that real-time monitoring is facilitated. Real-time, accurate and automatic regulation and control of the pressure of the geological reservoir are achieved, the stability and safety of the geological reservoir in the carbon dioxide injection process are guaranteed, the geological storage effect of the carbon dioxide is improved, and various negative effects caused by too high pressure are avoided.

The invention relates to the technical field of fault detection of the Internet of Things, in particular to a valve path detection method and system based on the Internet of Things, and the method comprises the following steps: based on real-time data of a valve path, removing high-frequency noise and high-frequency components in temperature fluctuation through a low-pass filter, carrying out the mean value smoothing operation of pressure data, and carrying out the mean value smoothing operation; and performing data standardization conversion on the flow data to obtain valve operation state data. According to the method, through real-time collection and optimization processing of valve operation data, it is ensured that the data is accurate and stable, weighted calculation is carried out in combination with the data influence degree, the representativeness of state feature extraction and the perception ability of working condition changes are enhanced, and based on state feature trend analysis, the sensitivity of fault detection is improved; and health state evaluation is carried out in combination with the operation duration and the load change, so that health state scoring is realized, the predictability of the long-term operation state is improved, false alarms and missing alarms are reduced, and the accuracy of fault early warning and the equipment health monitoring capability are enhanced.

The present invention relates to a multi-region collaborative control method and device for a high-precision pressure sensor array. The method: based on a first collaborative sampling parameter, data is collected from multiple sensor units in a pressure sensor array to obtain a pre-processed pressure data set and a sensor unit spatial position matrix; a spatial weighted pressure covariance matrix is ​​constructed; eigenvalue decomposition and information entropy calculation are performed, and the number of target pressure points is determined according to the minimum description length criterion and the information gain ratio; a pressure feature subspace and a local perception domain are constructed; the pre-processed pressure data set is projected onto the pressure feature subspace to generate a sensor unit state data set; a multi-layer attention encoder is input for processing, and the first collaborative sampling parameter of the pressure sensor array is adjusted to obtain a second collaborative sampling parameter. The implementation of the present invention improves the system's response sensitivity to local pressure changes, thereby realizing multi-region collaborative control of pressure sensing.

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 embodiment of the invention discloses a water supply network abnormity identification method and device, electronic equipment and a storage medium, and relates to the technical field of water supply network abnormity identification, and the method comprises the steps: collecting pressure data from each monitoring point, carrying out the preprocessing, employing a Pearson's correlation coefficient to construct a dynamic pressure correlation matrix according to the pressure data, carrying out abnormity judgment and restoration on the monitoring equipment; the pressure difference standard deviation of each monitoring point in adjacent time periods is calculated to recognize the water hammer effect; constructing a residual error distribution reference through a machine learning algorithm based on historical monitoring data, calculating a real-time pressure residual error of each monitoring point, calculating a comprehensive anomaly index according to the residual error distribution reference, and carrying out anomaly judgment and early warning; and a Kriging interpolation method and a semi-variation function are adopted to generate pressure residual error space distribution according to the real-time pressure residual error and display the pressure residual error space distribution. The problems that in the prior art, data verification is not systematic, space-time relevance is lacked, the abnormal classification dimension is single, misjudgment is prone to occurring, and the response efficiency is low are solved.

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 deep learning, and discloses an earphone mold injection molding parameter optimization method, device and equipment, and the method comprises the steps: collecting temperature distribution data and mold cavity pressure data in an earphone mold cavity, and carrying out earphone shell injection molding dynamic analysis to obtain an earphone shell material flow curve; carrying out topological characteristic analysis on the earphone shell material flow curve, and constructing an earphone shell injection molding process parameter model; based on the earphone shell injection molding process parameter model, the mold cavity temperature and the mold cavity pressure serve as state variables, the heating power and the injection pressure serve as control variables, and an earphone shell injection molding optimization controller is constructed; the earphone shell injection molding optimization controller is connected with the injection molding machine control system, the heating power and the injection pressure of the earphone mold are adjusted in real time, the optimal technological parameters of the earphone mold are solved, a real-time feedback and parameter fine adjustment mechanism is adopted, the injection molding machine control system can quickly respond to technological fluctuation, and the consistency of product quality is guaranteed.

The invention discloses an intelligent double-liquid grouting system and a grouting method. The system comprises a liquid A stirring barrel, a liquid B stirring barrel, a double-liquid grouting pump, a slurry mixer, a PID (Proportion Integration Differentiation) regulating valve, a control system, a flow meter, a pressure meter, a flow sensor and a pressure sensor. The liquid A and the liquid B are respectively prepared and then sent out by a double-liquid grouting pump, enter a slurry mixer to be compounded and mixed, and are injected into a target stratum through a directional grouting pipe. The system collects and analyzes flow and pressure data through a control system, and closed-loop regulation and control of grouting flow and proportion are achieved through PID regulation logic. The PID regulating valve has the functions of over-voltage current limiting and overflow prevention at the same time. The method is suitable for reinforcement and leakage treatment under a complex stratum in geotechnical engineering, and has the advantages of being high in matching precision, high in mixing efficiency, good in safety, high in adaptability and the like.

The invention belongs to the field of aircraft wireless temperature and pressure sensors, and relates to a multi-redundancy aircraft wireless temperature and pressure sensor and a control method. The sensor comprises a multi-redundancy pressure sensitive assembly, a signal conditioning circuit, a Bluetooth transceiver module, a solid battery, a rear cover, a support and an outer cylinder. The pressure-sensitive assembly is welded to one end of the outer cylinder. The support is welded to the pressure-sensitive assembly. The Bluetooth transceiver module, the signal conditioning circuit and the solid battery are fixed on the bracket; the rear cover is in threaded connection with the outer cylinder; the pressure sensitive assembly is connected with an inner cavity of a tire through a pipeline and transmits pressure data to the signal conditioning circuit, and after the temperature measuring module measures the temperature, temperature and pressure signals are conditioned and calculated and then sent to a ground terminal through the Bluetooth receiving and transmitting module.

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.

A method for monitoring a rock burst in ultra-thin coal seams, which method belongs to the technical field of fully mechanized coal mining in ultra-thin coal seams. The method comprises: respectively recording the acquired propagation times and propagation velocities of attenuated seismic waves from artificial seismic sources and the acquired propagation time and propagation velocity of a rock burst, and comparing different seismic wave signals and real-time rock burst signals passing through a mining area; converting the different seismic wave signals and the real-time rock burst signals into digital signals, and determining via comparison whether the digital signals have waveforms during the rock burst; when the digital signals have waveforms during the rock burst, identifying the waveforms of micro-seismic events, and determining the energy intensity of the rock burst; locating rock burst sources, and obtaining an intersection area of the rock burst sources, so as to obtain a seismic source position after a delineated range is reduced; and on the basis of acquired real-time pressure data in underground rock or coal seams, obtaining the positions of the maximum pressure and maximum pressure changes of a seismic source, and the numerical values thereof by means of analyzing a real-time pressure value and a real-time strain change rate. By means of the method, potential hazardous areas can be monitored. The present invention further relates to a system for monitoring a rock burst in ultra-thin coal seams.