566 results about "Smart transducer" patented technology

The invention relates to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering operation risk monitoring management system based on multi-sensor fusion. The system comprises a data acquisition module, a data fusion module, an early warning and instruction generation module, and an instruction distribution and verification and correction module. The data acquisition module acquires structural deformation, water flow pressure and environment temperature and humidity data in real time through multiple types of intelligent sensors; the data fusion module inputs the collected data into a dynamic weighted fusion model, and generates a comprehensive monitoring index in combination with a sensor confidence coefficient weight and a historical data deviation rate; the early warning and instruction generation module triggers multi-stage early warning and generates an equipment regulation and control instruction set according to the comprehensive monitoring index and a preset safety threshold value; the instruction distribution module distributes the instruction to an execution terminal through a distributed message queue; and the verification and correction module checks terminal feedback signals by using a back propagation verification mechanism, and corrects the confidence coefficient weight of the model.

The invention discloses a cable fault accurate positioning system based on an intelligent sensor system, and relates to the technical field of power fault positioning, and the cable fault accurate positioning system comprises a cloud monitoring center which is in communication connection with the following modules: a signal acquisition and processing module which is used for deploying the intelligent sensor system along a cable, operation environment data and traveling wave signals in the cable operation process are obtained, and the signals are preprocessed and stored in a cloud data warehouse. According to the invention, the front band-pass filter and the self-adaptive trap filter are combined to filter out high-frequency harmonic waves and specific-frequency interference signals, and the differential sampling technology is adopted to counteract common-mode interference, so that the signal-to-noise ratio of the signals is remarkably improved, a system can accurately capture and process weak traveling wave signals caused by a high-resistance grounding fault in a complex environment, and the fault detection accuracy is improved. Therefore, the fault positioning precision is greatly improved, the positioning error caused by signal interference is reduced, and the determination of the fault position is more reliable and accurate.

The invention provides an intelligent fault diagnosis method and system for a photovoltaic system. The method comprises the following steps: collecting multi-source heterogeneous operation parameter data of the photovoltaic system based on an intelligent sensor network and carrying out standardized preprocessing; local feature extraction is carried out on edge nodes, a frequency moment is calculated, data summary aggregation is carried out through a frequency moment optimization algorithm, and a fusion key feature vector set is constructed by using a multi-modal data fusion model; performing anomaly detection based on the normal behavior mode baseline, and generating an abnormal state alarm signal and a preliminary anomaly type indication; constructing a weighted graph model based on a system topological structure, constructing a fault association sub-graph by using an approximate spanning tree algorithm, accurately positioning a fault unit and diagnosing a fault type; and generating and executing a self-healing operation scheme according to the fault diagnosis conclusion. Through the distributed multi-modal data fusion and approximate spanning tree fault positioning technology, rapid and accurate diagnosis and autonomous recovery of the photovoltaic system fault are realized, and the system reliability and the operation efficiency are improved.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

The invention relates to the technical field of intelligent sensors, in particular to a workshop harmful gas abnormity monitoring method and system based on multi-modal data, and the method comprises the following steps: obtaining the multi-period concentration change of a node, recognizing the reverse direction, forming a bounce fragment, combining node behaviors according to the correlation of time and space, and constructing a diffusion path; and in combination with node toxicity residence and trend change and gas and time information at the convergence position, a workshop multi-modal toxicity anomaly monitoring result is obtained. According to the method, gas fluctuation characteristics are captured by recognizing the concentration change direction and the reversal amplitude, a behavior combination is constructed by combining time and space information, the linkage recognition capability between nodes is enhanced, a path sequence is constructed by depending on the trend direction and the node sequence to present a diffusion track, and extension and mutation nodes are extracted by superposing the residence state and the change trend. The dynamic monitoring range of toxic gas in multiple scenes is expanded, and correlation analysis of behavior traceability, path tracking and abnormal gathering areas is supported.

The invention provides an electrocardiograph electromagnetic compatibility test data diagnosis method based on an intelligent sensor, and relates to the technical field of electrocardiograph electromagnetic compatibility test. The method includes generating structured test data. And calculating a theoretical interference reference threshold. And generating an initial interference judgment atlas arranged according to the interference time sequence. And constructing a dynamic compensation coefficient matrix, and injecting the dynamic compensation coefficient matrix into the initial interference determination map to obtain a dynamic interference determination map. And calculating the characteristic similarity between the real-time test data in each interference time sequence window and the dynamic interference judgment map, and diagnosing the electromagnetic compatibility state of the electrocardiograph. According to the invention, through multi-dimensional data acquisition and interference identification, through a dynamic reference threshold and a compensation mechanism, environment and equipment state changes are adapted, the diagnosis stability of a complex scene is improved, through real-time data comparison and traceability, the equipment state is rapidly judged, the interference source is positioned, the debugging time is shortened, and finally the measurement accuracy of the electrocardiograph is guaranteed.

The invention relates to the technical field of environmental monitoring of artificial intelligence, and particularly discloses a dangerous waste storage environment real-time monitoring and risk early warning system based on digital twinning, which collects multi-modal environmental data in real time through an intelligent sensor network deployed in a dangerous waste storage facility, and constructs a digital twinning model synchronized with a physical entity; a multi-modal data fusion and physical embedding technology is adopted, and virtual risk parameters of an area which is not actually measured are calculated based on an environment evolution rule; an abnormal association mode between the measured data and the virtual risk parameters is analyzed and identified through the dynamic association network, and risk level judgment and early warning signal triggering are achieved; a sensor monitoring strategy is dynamically adjusted according to an early warning result to form closed-loop management and control; according to the invention, the sensor limitation of the traditional monitoring system is broken through, the advanced accurate early warning of the hidden risk is realized, and the monitoring efficiency is obviously improved through adaptive resource configuration.

The invention provides a metering data automatic calibration method and system, and the method comprises the steps: obtaining an actual measurement output value of a target intelligent sensor for a measured physical quantity in a normal operation process of metering equipment, accessing a standard reference signal outputted by an external standard device subjected to the traceability of the quantity value, reading a pre-stored standard reference value and a standard threshold value, and carrying out the calibration of the measured physical quantity. According to the actually measured output value, the standard reference value and the standard reference signal, obtaining a standardized output value through a fitting mode; when the standardized output value exceeds a standard threshold value, switching to a calibration mode, providing forward and reverse standard input quantities by an external standard device, acquiring a response signal, and calculating a nonlinear compensation coefficient and a hysteresis correction coefficient; and updating the output correction parameter according to the compensation coefficient. By adopting the scheme of the invention, the cost constraint of different error attention in measurement data calibration can be realized, and the calibration robustness is improved.

The invention discloses a belt conveyor intelligent transportation method and system based on an intelligent sensor and electronic equipment, and the method comprises the steps: collecting key parameters from a belt conveyor through a multi-source heterogeneous intelligent sensor, including a state parameter of the belt conveyor, a state parameter of a belt and an environment change parameter; performing synchronous calibration, noise suppression and feature extraction on the multi-source heterogeneous data, and performing multi-source heterogeneous data fusion; constructing a belt conveyor operation multi-dimensional state vector based on the fused data; and inputting the state vector into a prediction model to obtain a future operation state prediction result. According to the method, the problems of'inaccurate prediction, insufficient advance and high missing report rate 'caused by'strong data isomerism, key feature weight deficiency, rare fault samples, insufficient model stability' and the like are solved.

The invention relates to the technical field of power equipment on-line monitoring and fault diagnosis, and discloses a converter transformer bushing end screen voltage abnormity detection device and early warning method.The method comprises the steps that voltage data are collected through a wireless intelligent sensor, soft synchronization and self-adaptive windowing are achieved through a commutation notch feature locking mechanism, and a preprocessing signal is output; topological correlation clustering is executed to construct a virtual health reference, a dynamic residual sequence is extracted through time domain difference, and system side and equipment side features are decoupled; constructing a variational mode decomposition model, and calculating a characteristic entropy value of the intrinsic mode function; and comparing the entropy value with a threshold value, triggering an active impedance disturbance verification mechanism, controlling a sensor to access a load and comparing an actual voltage drop rate with a theoretical voltage drop rate, and determining an insulation degradation or poor contact fault. High-precision synchronization can be achieved without a hardware clock, and the problems that weak fault features are difficult to extract and virtual connection and insulation faults are difficult to distinguish in a high-impedance loop are effectively solved.

The invention relates to the technical field of intelligent sensors, and particularly discloses a smoke sensor monitoring sensing method and system. The system comprises a multi-source environment sensing module, a feature extraction and fusion module, a dynamic threshold decision module and an execution control module, a comprehensive environment state vector is constructed by fusing multi-dimensional environment data, and an alarm threshold is dynamically adjusted based on a deep reinforcement learning model, so that accurate fire identification and hierarchical control are realized, and the system is suitable for popularization and application. And the monitoring reliability, the environmental adaptability and the agriculture and forestry equipment operation efficiency are effectively improved.

The invention belongs to the technical field of hypersonic flight vehicle control, and particularly relates to a hypersonic flight vehicle protection control method considering non-starting of an air inlet channel. The method specifically comprises the steps of establishing a quasi-one-dimensional nominal mechanism model to describe flow field changes of an isolation section and a combustion chamber; adopting a physical information neural network to construct a reduced-order model as a real-time agent of the quasi-one-dimensional model; designing a dual extended Kalman filtering algorithm to realize joint online estimation of model parameters and blind area states, and introducing shock wave position pseudo measurement to form multi-loop feedback; a dual-channel fusion early warning system of a model channel and an intelligent sensor channel is constructed, and robust early warning is realized through adaptive weight; and designing a continuous adaptive model prediction controller, and dynamically adjusting a control strategy according to risk indexes and parameter uncertainty to realize active protection control.

The invention relates to the technical field of micromolecule mineral water monitoring, and discloses a micromolecule mineral water quality monitoring system based on an intelligent sensor. The system comprises a multi-channel spectrum sensor array, an ion selective field effect transistor group, a microfluidic preprocessing module, a dynamic compensation database, a self-adaptive analysis engine, a cloud verification interface and a feedback execution mechanism. The micro-fluidic pretreatment module is used for eliminating bubbles of an original water sample, screening particles and outputting stable liquid flow; the spectrum sensor array captures full-band absorption spectrum data to generate a characteristic matrix, and the transistor group detects concentration fluctuation of calcium ions, magnesium ions and silicate; the adaptive analysis engine performs multi-modal fusion and abnormal marking on the time sequence alignment data stream; the cloud interface performs cross validation on the local result and the regional water quality center reference library to generate a credibility report; and the feedback execution mechanism adjusts the microfluidic flow velocity and the sensor integration time according to a report to realize efficient water quality monitoring.

The invention discloses a liquid cooling case overload monitoring method based on an intelligent sensor. The method comprises the following steps: deploying a sensor network at a key node of a cooling flow channel of a liquid cooling case; all the sensors are connected with an edge computing gateway; the edge computing gateway synchronously collects data of the sensor and generates a time sequence data set; dynamically calculating the overall heat dissipation power P (t), the system flow resistance coefficient K (t) and a parameter eta (t) representing the cooling efficiency based on the time sequence data set; comparing the calculated value with a reference value, and performing overload risk assessment by applying a preset judgment logic; when the evaluation result exceeds a preset risk threshold value, triggering overload early warning, and generating a control instruction; and according to historical operation data of the system, carrying out periodic self-adaptive calibration on the reference value. By deploying the sensor network at the key node of the liquid cooling case and analyzing the collected data through the edge computing gateway, the response speed and the control precision of the system to the dynamic change of the heat source power are improved.

The invention discloses a geological feature-based regional landslide hidden danger automatic identification system and method, and relates to the technical field of intelligent sensors. The geological feature-based regional landslide hidden danger automatic identification method comprises the steps of S1, acquiring and preprocessing multi-source monitoring data and geological constraint data, and constructing a landslide monitoring database; s2, based on the weighted sensitivity and residual accumulation of the multi-source monitoring data, analyzing the credibility of the physical property parameters of the slip band; s3, spatial coupling analysis is carried out through multi-source deformation and geometric parameter data, and sliding band form recognition and connectivity adjustment operation are dynamically optimized; s4, dynamic instability analysis is carried out through deformation rate and sliding surface geometric fusion data, and the slip band instability trend is quantified; and S5, performing comprehensive judgment on the stable state of the monitoring unit by fusing the three-dimensional sliding surface probability body and the spatio-temporal evolution index. The problems of inaccurate slip zone parameter inversion, low slip surface judgment precision and early warning lag in regional landslide hidden danger identification are solved.

The invention discloses a self-adaptive scheduling industrial internet data acquisition control system and strategy, and relates to the technical field of industrial internet data acquisition and self-adaptive scheduling control, and the system comprises a data sensing layer, a deployment multi-type intelligent sensor group, an edge gateway and a hybrid communication module. The sensor supports multi-protocol adaptive access and dynamically switches sampling frequency, the edge gateway preprocesses original data, and the communication module guarantees stable transmission through wired and wireless hybrid networking; the self-adaptive scheduling layer comprises a task grading unit, a load monitoring unit and a dynamic scheduling unit; the edge calculation layer is used for processing tasks with high real-time performance; the cloud collaboration layer stores data and mines, analyzes and constructs an equipment model; the security protection layer realizes identity verification, data encryption and intrusion detection; and the system management layer manages the full life cycle of the equipment and divides user permissions. According to the system, the problem of traditional industrial internet data acquisition scheduling pain points is solved, and the overall operation efficiency and safety of the system are effectively improved.

The invention discloses a fan inspection route planning method and system for an unmanned aerial vehicle carrying an intelligent sensor, and relates to the technical field of inspection planning, and the method comprises the steps: carrying out the grid processing of fan blades, and calculating the prior defect probability of each blade grid; a detection balance decision is made by using the prior defect probability and the shutdown cost; if the detection balance decision is a startup detection decision, obtaining a blade time sequence movement track of the fan blade, and configuring a time sequence target detection position in combination with a prior defect probability; the method comprises the following steps: acquiring structural data of fan blades, performing blade rotation wind field disturbance sensing, and establishing disturbance sensing factors; and executing following mode and static suspension mode decisions by using the disturbance perception factor and the time sequence target detection position, configuring an inspection route, and executing inspection management. The technical problems that in the prior art, route planning is not intelligent, the detection efficiency is low, and the influence of wind field disturbance is large in the fan blade inspection process are solved, and the technical effect of improving the inspection precision and stability is achieved.

The invention relates to the technical field of environment monitoring and intelligent sensors, and particularly discloses a soil pollutant detection and analysis system based on an intelligent sensor. The system comprises a sensor array module, a data preprocessing and fusion module, an ecological toxicity dynamic evaluation module and a decision output module. According to the invention, the biological response sensing unit capable of directly reflecting the ecological effect is integrated, and the ecological toxicity dynamic evaluation model fusing the concentration, the biological response and the environmental factors is innovatively constructed, so that direct and dynamic quantification from the pollutant content to the ecological risk is realized. According to the method, the problem that an existing assessment method is insufficient in biological correlation is solved, the risk assessment result is closer to the real ecological environment effect, and a reliable basis is provided for precise treatment.

The invention relates to the technical field of automatic control, and discloses a magnetic recloser adaptive control method and system for circuit design. The method comprises the steps of collecting multi-dimensional electrical parameters of a distribution line through an intelligent sensor array, constructing a mixed fault feature vector by utilizing a time-frequency domain fusion algorithm, separating instantaneous and permanent fault components by adopting three-stage fault recognition, obtaining fault severity level evaluation based on dynamic weight distribution, and performing fault severity level evaluation based on dynamic weight distribution. And generating a self-adaptive control instruction through grading reclosing strategy matching to execute the opening and closing operation of the magnetic recloser. According to the invention, the technical problem that the magnetic recloser cannot carry out accurate fault recognition and adaptive hierarchical control in a multi-transient fault and permanent fault mixed scene is solved.

The invention relates to the technical field of protective clothing testing, and discloses a protective clothing combustion testing device based on an intelligent sensor, which comprises a control console, a testing cabinet is arranged on the control console, a simulation mechanism for simulating real motion is arranged on the inner wall of the testing cabinet, and an exhaust mechanism for ventilation is arranged on the inner wall of the testing cabinet. According to the device, in the rotating process of the fire protection clothing, it can be ensured that heat flow of subsequent flame testing is uniform, testing and detection are free of dead corners, the real wearing state is simulated, the flame testing quality of the device on the fire protection clothing is improved, the testing combustion precision of the device on the fire protection clothing is improved, and the testing efficiency is improved. And meanwhile, a temperature sensor on the surface of the human body model can test the internal temperature of the fire protection clothing, all-directional combustion test detection of the device on the fire protection clothing can be improved through rotary movement of a flame gun, and the combustion test effect of the device is improved.

The invention relates to the technical field of coal mine safety monitoring and intelligent early warning, and discloses an edge computing driven coal mine underground multi-parameter self-adaptive sensing and anomaly recognition sensor network, which comprises a plurality of mining intrinsic safety type intelligent sensor nodes for forming a distributed monitoring network, a multi-parameter acquisition module, a data processing module and a data processing module, various monitoring parameters of the underground coal mine are collected in real time; the edge calculation module is deployed with a lightweight long and short term relation-improved gating cycle unit time sequence prediction model, performs adaptive drift correction on a monitoring parameter background value to dynamically generate a threshold range, and generates an abnormal initial judgment result; the cooperative communication module is used for performing data interaction with adjacent nodes after the abnormal initial judgment result is generated, and generating a space-time cooperative verification result; and the exception decision module is used for generating a multi-parameter fusion judgment result according to the exception initial judgment result and the time-space cooperation verification result and outputting graded early warning. According to the method, the accuracy, the real-time performance and the engineering landing performance of coal mine underground dynamic disaster precursor identification are remarkably improved.

The invention relates to the technical field of battery safety management, and discloses an intelligent sensor-based battery safety monitoring system for a battery replacement cabinet. The system comprises the steps of dynamically aggregating sampling points based on a multi-dimensional data change rate to generate monitoring fragments, and screening candidate abnormal fragments; performing multi-window sliding analysis on the candidate segments to obtain a multi-scale form spectrum and generate a comprehensive anomaly representation vector; aggregating all vectors to construct a system-level abnormal feature space, and calculating a discrete diffusion index; performing space-time transformation and matrix decomposition on the complete data stream, and calculating a modal steady-state coefficient; and fusing the two indexes to obtain a comprehensive state level, and adaptively adjusting a data acquisition and analysis period according to the comprehensive state level. According to the method, the early abnormal waveform characteristics of the battery can be deeply identified, and adaptive closed-loop monitoring based on the global risk situation of the system is realized.

The invention relates to the technical field of intelligent robot attitude sensing and dynamic monitoring, and discloses a five-axis robot attitude monitoring method based on an intelligent sensor. Global and local coordinate systems of all joints are established on the five-axis robot, a six-axis IMU is arranged, and acceleration and angular velocity are periodically collected; taking a plurality of groups of samples in a stationary period, calculating a mean value, offset and covariance, fitting an error ellipsoid, and obtaining a robust model through characteristic decomposition and threshold value clamping; a normalized matrix is constructed to perform unitization and dimension recovery, and die length tolerance checking is set; establishing unit quaternion integral update, and generating an equivalent angular velocity by using gravity consistency to perform primary attitude correction; the chained cascade joint rotation matrix outputs rolling, pitching and yawing, and the attitude and change rate sequence is subjected to statistical segmentation.

The invention relates to the technical field of intelligent sensors, and discloses an optical cable handover state monitoring method based on an intelligent sensor, and the method comprises the steps: synchronously collecting optical fiber real-time performance parameters and environment interference parameters of an optical cable handover point, so as to obtain an optical cable original data packet, compensating the real-time performance parameters of the optical fiber in the original data packet of the optical cable based on an environmental parameter compensation algorithm, stripping the influence of environmental interference to obtain intrinsic performance parameters representing the state of the optical fiber connector, and evaluating the state degradation degree of the optical fiber connector based on the state degradation degree comprehensive evaluation model. Performing comparison and trend analysis on the intrinsic performance parameters and dynamic historical performance baselines of the intrinsic performance parameters to obtain a comprehensive health index representing the health degree of the optical cable junction point, performing multi-level threshold judgment based on the comprehensive health index, and generating corresponding state early warning or warning information when the comprehensive health index is lower than a preset threshold; according to the invention, the efficiency of optical cable handover state monitoring based on the intelligent sensor can be improved.

The invention relates to an oil and gas pipeline leakage online monitoring method and system, and relates to the technical field of oil and gas pipeline monitoring. According to the method, transient physical signals transmitted by multiple different types of sensors arranged along the oil and gas pipeline are received, an intelligent sensor network is formed, and comprehensive collection and feature labeling of leakage signals of the oil and gas pipeline are achieved. Then, calculating to obtain a DOA vector subset; and determining a suspected leakage point candidate set based on the DOA vector subset and the actual position coordinates of the sensors. In addition, through a multi-dimensional feature matching mechanism, the reliability of a positioning result is remarkably improved; and finally, rejecting reflection virtual image points from the suspected leakage point candidate set according to the target suspected leakage point, and finally realizing accurate identification and positioning of a real leakage point. According to the method, the technical problem of accurate positioning of the leakage point in a complex pipeline environment is effectively solved, and high-precision and high-reliability online monitoring of oil and gas pipeline leakage is realized.

The invention discloses an oil tank liquid level real-time monitoring method and system based on an intelligent sensor, and the method comprises the following steps: S1, collecting and preprocessing oil tank liquid level data, and constructing a liquid level time sequence; s2, extracting a time sequence fragment in a sliding manner, analyzing a trend, calculating a change rate and an amplitude, and generating a trend feature vector; s3, constructing a prediction model for reasoning, performing transformation, gating and mapping on a result, and generating a trend prediction and abnormal probability sequence; s4, fusing the sequence to calculate a mean value and a standard deviation, and constructing a threshold value based on Z-score to identify an abnormal time step; s5, calculating an error between a predicted value and a reference value, and dynamically adjusting PID parameters to generate a control instruction; and S6, comparing predicted data with real-time data before control, and updating model parameters according to deviation. According to the method, intelligent sensor collection, BEMD decomposition, deep learning trend prediction and a PID control feedback mechanism are fused, and the prediction precision and the regulation response capability of oil tank liquid level monitoring are improved.

The invention relates to the technical field of intelligent sensors, in particular to an unmanned aerial vehicle track intelligent tracking method based on a millimeter-wave radar, which comprises the following steps: receiving echo signals collected by the front end of the millimeter-wave radar, carrying out constant false alarm rate detection and Doppler frequency shift extraction on the echo signals, generating an unmanned aerial vehicle target space coordinate set, and carrying out target tracking on the unmanned aerial vehicle. And mapping the target space coordinate set of the unmanned aerial vehicle to a three-dimensional Euclidean space. According to the method, a non-convex optimization objective function containing beam switching time cost and precision constraint is constructed, and mathematical optimal solution of radar beam resources is realized. The generated adaptive scheduling instruction is used for adjusting the pulse repetition frequency and the beam residence time, it is ensured that radar front-end parameters dynamically evolve along with the target state, overall centroid tracking is emphasized for rigid body formation so as to save resources, independent high-frequency refreshing is emphasized for discrete targets so as to ensure the precision, and the accuracy is improved. And the continuity and robustness of trajectory tracking of the unmanned aerial vehicle in a complex electromagnetic environment are improved.

The invention discloses a full-slewing drilling machine jamming detection method and device combined with intelligent sensors, and relates to the technical field of full-slewing drilling machines, and the method comprises the steps: collecting multiple types of original time sequence data in a drilling process in real time through multiple types of intelligent sensors deployed on a full-slewing drilling machine; the multiple types of original time series data are preprocessed, model input features are constructed and input into a pre-trained drilling tool jamming detection model, the drilling tool jamming detection model adopts a fusion framework and comprises multiple feature extractors, multiple drilling tool jamming classifiers and multiple domain classifiers, and the multiple domain classifiers are used for training the multiple feature extractors and the multiple drilling tool jamming classifiers; and on the basis of output fusion of the plurality of drilling tool sticking classifiers, generating a drilling tool sticking early warning signal, and executing corresponding control operation. The technical problem that in the prior art, the drilling tool jamming detection precision is not stable under different geological conditions is solved, and the technical effects that the drilling tool jamming phenomenon is accurately monitored in real time, and the safety and efficiency of drilling machine operation are improved are achieved.