14726 results about "Frequency spectrum" patented technology

The invention provides a home abnormal state signal detection method and system based on multi-modal sensing, and relates to the technical field of detection, and the method comprises the steps: collecting human body motion, acoustics and environment parameters through a millimeter wave radar, an acoustic sensor and an environment sensor, inputting the parameters into a deep fusion network, a spatio-temporal attention mechanism and a multi-scale convolutional neural network are used to extract a spatio-temporal feature sequence, and joint probability features are formed in combination with an acoustic feature spectrum analysis result. And inputting the joint probability features into a time sequence knowledge graph, and obtaining scene adaptive features after processing by a graph attention network and a double-flow auto-encoder network. And finally, the multi-task learning network is combined with the risk propagation neural network and the recursive neural network to carry out anomaly prediction and risk level evaluation, and is combined with historical early warning information to output an early warning strategy. According to the invention, multi-modal data can be effectively fused, the accuracy and reliability of home abnormal state detection are improved, and a more accurate risk assessment and early warning strategy is provided.

The invention discloses an experimental calibration method based on multi-sensor signal fusion processing, particularly relates to the field of data analysis, and comprises the steps of a stepped confrontation disturbance protocol, data stream processing and real-time analysis, and multi-stage logic judgment and calibration execution. A stepped confrontation disturbance protocol is created for the first time, potential errors of the sensor are excited in real time and corrected in a closed loop mode under the condition that system operation is not interrupted, and a traditional shutdown calibration mode is thoroughly replaced; a multi-stage disturbance defense system is constructed, and composite interferences such as spectrum aliasing, overload distortion and signal-to-noise ratio attenuation caused by electromechanical thermal multi-physical field strong coupling are effectively suppressed; laser space calibration and dynamic rigidity modulation technologies are fused, and space drift errors caused by deformation of the mounting base and mechanical loosening are accurately diagnosed and compensated. Finally, long-term precision stability and data fusion reliability of the multi-source sensor under complex working conditions are achieved.

The invention provides a mining circuit fault self-diagnosis method and system. According to the method, current and voltage waveforms and three-dimensional vibration signals of a cable are collected, and anti-interference data are generated through self-adaptive noise reduction and time sequence synchronization; a waveform interception window is dynamically adjusted based on the correlation between the mechanical vibration intensity and the current transient rate, the wave crest slope variable quantity is extracted from the current transient segment, high-frequency harmonic energy is separated from the voltage transient segment, time-frequency analysis is carried out on the vibration signal to extract an energy sudden increase frequency point, and a mechanical damage spectrum feature set is constructed; inputting the features into a space-time correlation model, and verifying space-time consistency of current distortion and voltage abnormity to generate composite features; dynamically correcting a fault threshold based on the environmental interference factor; and finally, a short-circuit peak or open-circuit oscillation diagnosis result is output according to the association strength of the electrical and mechanical characteristics. According to the invention, accurate fault self-diagnosis of the mining cable under a complex working condition is realized.

The invention provides an unmanned aerial vehicle countering plan generation method and system based on an artificial intelligence technology, and the method comprises the steps: analyzing the dynamic monitoring data flow of a target unmanned aerial vehicle, recognizing the flight path characteristics and electromagnetic signal frequency spectrum of the target unmanned aerial vehicle, and constructing a three-dimensional space threat situation model to evaluate the threat level. Decomposing behavior patterns and attack intention parameters of a target unmanned aerial vehicle based on threat levels, generating a dynamic decision factor set in combination with geo-fence constraint conditions and environmental interference factors, generating a plurality of candidate countering plans by matching countering means basic plans in a plan knowledge graph, and constructing a plan logic tree; a multi-dimensional evaluation parameter set is generated based on a three-dimensional space threat situation model, a dynamic decision factor set and a countering efficiency index, collaborative optimization processing is performed on a plan logic tree, an optimal countering plan is generated and converted into an executable instruction set, the executable instruction set is synchronized to a countering device for execution, and the real-time performance and scene adaptability of the unmanned aerial vehicle countering plan are improved.

The invention discloses a self-adaptive frequency spectrum monitoring and interference suppression method for a railway power transformer. The method comprises the following steps: S1, collecting original multi-source signal data; s2, performing high-order filtering and Z-score normalization processing on the original multi-source signal; s3, inputting the original multi-source signal into a multi-scale residual fusion time-frequency transformation network, and extracting a time-frequency feature tensor; s4, inputting the time-frequency feature tensor into the interference identification network fused with the attention mechanism; s5, dynamically activating an interference suppression module according to an identification result; s6, constructing a multi-dimensional tensor data structure, extracting sparse dictionary morphological features and spectral domain statistics, and generating a composite feature vector set; s7, inputting the composite feature vector into a health state evaluation module; and S8, uploading the diagnosis result to a remote monitoring platform through the embedded communication module. According to the invention, multi-dimensional perception and adaptive modeling are fused, and intelligent identification and remote monitoring of railway transformer faults are realized.

The invention discloses a bridge detection method and system based on a digital twin technology, and relates to the field of bridge structure health monitoring. The method comprises the following steps: acquiring a strain distribution value, a vibration spectrum value and an environmental load spectrum value in real time through a sensor network deployed in a physical bridge, generating a structural response data set, and synchronizing the structural response data set to a digital twinborn body; calculating a damage index value and an accumulated damage quantity value based on the structural response data set; inputting the damage index value and the accumulated damage quantity value into a preset safety criterion, and calculating a safety margin coefficient value and a failure risk grade value; calculating a residual life prediction value based on the safety margin coefficient value and the environmental load spectrum value, and synchronously correcting a degradation rate value of the digital twin; and generating a priority maintenance instruction according to the failure risk grade value, the residual life prediction value and the safety margin coefficient value, and feeding back maintenance effect data to the digital twinborn body to complete updating after execution. The bridge operation and maintenance efficiency and safety are remarkably improved.

The invention provides a resource dynamic monitoring and control method and system based on quota tracking. According to the pipe gallery deformation monitoring and resource optimization system based on optical fiber and sound wave sensing, underground pipe gallery structure stress field propagation data are obtained through an optical fiber sensor array, energy attenuation characteristics of stress waves at reinforcing rib intersection points are analyzed, and a dynamic mapping model of structure deformation risks and monitoring node resource requirements is established. Meanwhile, the frequency domain propagation characteristics of abnormal vibration signals are captured through acoustic sensors arranged at the expansion joints, and a resource allocation strategy matrix is formed; and constructing a quota tracking database, comparing the actual consumption rate of the frequency spectrum quota with a predicted value, generating a resource use efficiency report, and performing frequency spectrum redistribution of frequency hopping interval optimization and transmitting power adjustment on the nodes in the high stress concentration region. According to the technical scheme provided by the invention, the resource dynamic optimization of the underground pipe gallery structure is realized, and the risk early warning precision and the communication efficiency are improved.

The invention provides an efficient flaw detection method of an ultrasonic flaw detector for automatic detection of a welding seam, and belongs to the technical field of welding seam detection.The efficient flaw detection method comprises the steps that firstly, a welding seam three-dimensional digital model is established, an optimal scanning path is determined, reflection echo signals are processed in real time through an adaptive beam forming algorithm, and defect features are extracted and subjected to quantitative analysis; and establishing a neural network model based on the defect characteristic spectrum, and performing deep learning training by using the sample library. A multi-angle scanning strategy is adopted to implement full-coverage detection, and cross scanning is applied in a key area to improve the detection probability. And calculating the accurate position of the defect by using a time-difference method, and constructing a defect three-dimensional distribution diagram by combining a multi-angle data fusion technology. The optimal filtering parameter is determined through the acoustic feature optimization function, and the defect signal-to-noise ratio is improved. And finally, a quality evaluation report is automatically generated based on a detection result, so that the problems of defect identification accuracy and detection efficiency in weld flaw detection are effectively solved.

The invention discloses a computing power load stabilizing and energy-saving management system based on a flywheel energy storage system, and belongs to the technical field of energy storage and management, and the system comprises a data collection module which is used for collecting and calculating a real-time current harmonic spectrum of a cluster and server rack vibration data to generate an electric energy quality characteristic spectrum; the prediction modeling module is used for establishing a prediction model output computing power demand dynamic distribution matrix based on multi-dimensional space-time correlation; the optimization control module is used for analyzing a peak load phase in the power demand dynamic distribution matrix to generate a dynamic coordination control strategy; and the power execution module is used for executing the dynamic coordination control strategy to adjust the phase parameter of the reverse electromotive force of the flywheel so as to offset the voltage harmonic component of the server bus. Harmonic phase compensation is realized through dynamic reverse electromotive force modulation, the calculation load prediction precision can be effectively improved, and the energy utilization efficiency and the equipment operation reliability are remarkably improved.

The invention provides an AI intelligent diagnosis method and system based on an intelligent system, and the method comprises the steps: collecting a building electromechanical equipment operation data flow containing multi-source heterogeneous data, such as a vibration spectrum, a thermal imaging characteristic and an energy consumption fluctuation parameter, through an edge calculation node, and carrying out the state time sequence coding to generate a coding sequence; and inputting the sequence into a fault prediction neural network to obtain a multi-dimensional fault feature distribution matrix. And then obtaining a standard diagnosis report of the expert knowledge base and an emergency diagnosis report of the field operation and maintenance record. And then a composite training objective function is constructed based on the result and equipment operation characteristic hidden space mapping, a fault prediction neural network parameter space is optimized through a back propagation algorithm, and a target fault prediction neural network is generated. And finally, fault prediction and diagnosis are carried out on the operation data flow of the building electromechanical equipment by using the network, so that comprehensive, accurate and intelligent fault diagnosis is realized.

Various embodiments herein provide techniques for sidelink communication on unlicensed spectrum. For example, embodiments include a sensing and resource selection (or reselection) procedure to select resources on which to perform a sidelink transmission. A listen-before-talk (LBT) procedure may be performed on the selected resources prior to the transmission. Embodiments further include techniques to handle LBT failures. Furthermore, embodiments provide techniques for mapping between a channel access priority class (CAPC) and a ProSe per packet priority (PPPP) and / or PC5 quality of service (QOS) indicator (PQI). Other embodiments may be described and claimed.

The invention provides a stratum disturbance analysis method and system under a shield construction coupling effect. Cutting vibration spectrum data are collected in real time through a cutter vibration sensor, and a feature fingerprint database containing a vibration energy distribution mode and a critical grouting interval is constructed in combination with soil parameters. And aligning the vibration spectrum with the soil bin pressure in a space-time manner, and generating a disturbance field distribution diagram for displaying an energy gradient distribution curve and a stress diffusion path topology. And matching the energy distribution curve and correcting formation interface propagation parameters through the feature matching network optimized by transfer learning, and outputting a formation type identification result and disturbance dynamic parameters. And based on the mapping relation between the parameters and the critical grouting interval, the ground surface displacement data are linked to dynamically regulate and control the grouting pressure, graded early warning and parameter regulation instructions are generated, and a stratum disturbance monitoring-regulation and control closed loop is formed. According to the technical scheme, dynamic optimization of the grouting pressure is achieved, and the stratum deformation risk caused by shield construction is remarkably reduced.

The invention provides a directional energy multi-target interference system based on an ultra-wideband phased array technology, and the system integrates signal perception, target recognition, interference signal generation and phased array control, so as to achieve the precise interference of an unmanned aerial vehicle cluster. The signal sensing module scans an airspace target through the ultra-wideband phased-array antenna array and obtains spectrum characteristics of a target signal. The target identification module analyzes signal features, identifies an individual target and predicts a motion track and a communication protocol of the individual target. And the phased array control module calculates and adjusts antenna beam forming parameters, and dynamically controls the radiation direction, phase and transmitting power of the antenna. The interference signal generation module generates a matched interference waveform based on the target communication protocol and the trajectory information. The ultra-wideband phased-array antenna array adopts a digital beam forming technology. According to the invention, efficient and accurate directional energy interference can be realized under multiple frequency bands, the confrontation capability of the unmanned aerial vehicle cluster is improved, and the method is suitable for defense tasks in electronic warfare and complex electromagnetic environments.

The invention provides a wind generating set fault monitoring method and system based on voiceprint recognition, and the method comprises the steps: collecting gear box voiceprint signal data in real time, recording signal fluctuation caused by gear surface wear, and obtaining an original signal data set containing a frequency spectrum high-frequency component enhancement feature; a time-frequency analysis method is adopted for the original signal data set, non-linear interference of vibration signals is recognized, multi-scale decomposition is carried out, initial wear frequency spectrum narrow-band characteristics and medium-term harmonic components are separated out, and a frequency spectrum component set is obtained; extracting characteristic parameters related to modulation depth abnormal fluctuation from the frequency spectrum component set, identifying a gear pair meshing frequency change rule, and determining a distribution mode of a tooth surface contact noise proportion; and if the feature matching result shows that the deviation between the spectrum high-frequency component diffusion distortion feature and the reference feature library exceeds a threshold value, adding the tooth surface fatigue crack noise feature into the feature library to obtain a target feature library.

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention relates to the technical field of fault positioning, and discloses a power distribution network grounding fault positioning method and system. The method comprises the following steps: acquiring a fault initial transient section signal, an arcing continuous section signal and an arc quenching recovery section signal of the power distribution network, and executing spectral analysis to obtain a fault feature matrix; performing zero-sequence transient frequency spectrum reconstruction on the fault feature matrix to obtain reconstructed frequency spectrum features and time-varying frequency spectrum features; calculating a transient characteristic index and a fault type based on the reconstructed spectrum characteristic and the time-varying spectrum characteristic; performing fault positioning calculation on the power distribution network according to the transient characteristic index and the fault type to obtain an initial fault point position; and performing transient fingerprint matching and probability density accumulation compensation on the initial fault point position to obtain a target fault point position. According to the method, high-precision characterization of the rapid change signal in the short time window is realized, interference of the compensation state on fault positioning is eliminated, and the grounding fault positioning accuracy of the power distribution network is improved.

Systems, methods, and apparatuses for providing dynamic, prioritized spectrum utilization management. The system includes at least one monitoring sensor, at least one data analysis engine, at least one application, a semantic engine, a programmable rules and policy editor, a tip and cue server, and / or a control panel. The tip and cue server is operable utilize the environmental awareness from the data processed by the at least one data analysis engine in combination with additional information to create actionable data.

The invention relates to the technical field of vehicle motors, in particular to a vehicle motor stator and rotor life prediction method and system, and the method comprises the steps: obtaining temperature distribution data, vibration spectrum data and current harmonic characteristic data during the operation of a stator and a rotor; performing time-frequency domain fusion analysis based on the temperature distribution data and the vibration spectrum data to obtain thermal coupling degradation characteristics, and obtaining electromagnetic performance degradation characteristics based on the current harmonic characteristic data; training to obtain a life prediction model based on a mapping relation between historical degradation characteristics and a failure threshold value, inputting the thermal coupling degradation characteristics and the electromagnetic performance degradation characteristics into the life prediction model, and outputting a residual life prediction value of the stator and the rotor; and dynamically adjusting operation parameters of the motor according to the residual life prediction value. According to the method, the problems that the life prediction precision is low and the operation strategy cannot be dynamically optimized due to the fact that the collaborative degradation effect of the stator and the rotor of the vehicle motor under the multi-physical field coupling effect is difficult to quantify in real time are effectively solved.

The invention relates to the technical field of wireless signal detection, and discloses a wireless anti-interference signal detection method, device and equipment and a storage medium, and the method comprises the steps: collecting a standardized interference feature vector through a multi-scene wireless signal receiving device; performing interference feature enhancement processing on the standardized interference feature vector to obtain an interference feature sample set; constructing a dual-array cooperative detection system comprising a passive detection array and an active detection array, and performing real-time monitoring to obtain dual-path interference signal monitoring data; performing spatial domain and time-frequency domain dual feature extraction and fusion processing on the dual-path interference signal monitoring data to obtain a comprehensive interference feature; the receiver filtering parameter matrix and the detection threshold vector of the double-array cooperative detection system are dynamically adjusted according to the comprehensive interference characteristics, the method can adapt to changes of different interference environments in a self-adaptive mode, comprehensive coverage of communication spectrums is achieved, and the wireless anti-interference monitoring range and the spectrum sensing capacity are greatly expanded.

The invention relates to the technical field of environment detection, and discloses a GIS map-based multi-class environment parameter automatic prediction method, which comprises the following steps: collecting noise intensity, vibration spectrum, sewage turbidity, illumination intensity and PM2.5 concentration in real time through a distributed sensor network, combining social behavior data, carrying out space-time alignment, and generating a multi-dimensional space-time matrix; calculating an energy overlapping degree by adopting a space-time diagram attention network, marking a high-risk collaborative pollution area, and predicting a pollution diffusion path; constructing a self-adaptive prediction model containing physical and behavior driving channels, and dynamically adjusting the weight to optimize the prediction precision; based on the optimization model, reversely deducing a pollution source of an overproof area, matching equipment characteristics and generating a control instruction; and opening an AR (Augmented Reality) interface to verify the treatment effect, and when the virtual-real data deviation exceeds a data deviation threshold, triggering federal learning to update the global model, and generating an environmental protection compliance report. According to the invention, the accurate decision-making efficiency of environmental governance can be improved.

The invention is suitable for the technical field of electrical equipment state monitoring, and provides an electrical equipment state monitoring method based on a non-contact leakage current sensor, and the method comprises the steps: collecting a leakage current signal of the surface of an insulating part of electrical equipment, and obtaining infrared thermal image data and an ultrasonic signal; variational mode decomposition is carried out on the leakage current signal to obtain a plurality of intrinsic mode functions; separating a leakage current effective component and an independent noise source based on a blind source separation algorithm; extracting a time-frequency characteristic of the effective component of the separated leakage current, extracting a local temperature gradient characteristic of the infrared thermal image data and a frequency spectrum energy characteristic of the ultrasonic signal, and generating a multi-modal characteristic vector; analyzing the space-time relevance of the multi-modal feature vector, dynamically distributing each modal weight coefficient for fusion, and generating a comprehensive fault feature; according to the method, the signal-to-noise ratio of the weak leakage current signal is improved, and the misjudgment rate is effectively reduced.

The invention discloses a smart power grid line loss intelligent detection method based on Internet of Things big data, and relates to the technical field of smart power grid electric energy metering, and the method comprises the following steps: S1, extracting a voltage signal and a current signal of each electric energy metering node in a set time window, carrying out the frequency domain transformation to obtain a frequency spectrum, calculating the energy concentration index of a high-frequency band, and calculating the energy concentration index of the high-frequency band; and identifying a high-frequency harmonic interference time section according to the change of the energy concentration degree. According to the method, a closed-loop mechanism of high-frequency harmonic interference identification, error traceability, cause discrimination and dynamic correction is constructed, fragment-level and node-level error compensation is realized, and the accuracy and the stability of line loss anomaly identification of the smart grid are improved by combining electric quantity consistency verification and trend model updating; and the reliability and the intelligent level of the system in a complex interference environment are obviously enhanced.

The invention provides a high-precision five-axis rotary table cooperative control method and system based on multi-closed-loop feedback, and the method comprises the steps: obtaining a real-time acceleration signal of a five-axis rotary table, carrying out the spectrum analysis of the acceleration signal, determining a current resonant frequency drift range and a main frequency component, and carrying out the frequency spectrum analysis; dynamically simulating the turntable structure by adopting a pre-established finite element model, and predicting the vibration mode change trend of the structure by comparing the deviation between the modal frequency obtained by simulation and the actually measured signal spectrum; according to the vibration mode change trend, matching similar vibration mode modes from a pre-established rotary table vibration mode database, obtaining corresponding vibration energy transmission path distribution, and determining an energy transmission abnormal region by analyzing the distribution of energy on each transmission path to obtain vibration energy transmission characteristics; according to an optimized motion instruction of a five-axis turntable, the dynamic response of the turntable is estimated, vibration frequency domain characteristics are extracted, and frequency domain compensation parameters are generated in combination with a preset frequency domain error matrix.

The invention discloses a dynamic spectrum autonomous collaborative optimization method based on a large language model, and the method comprises the steps: converting a channel state, a historical operation track and a conflict feedback signal into text-numerical value mixed tensors, and carrying out the splicing of the tensors to form a multi-dimensional environment state mixed representation; a distributed semantic inference engine is designed, a sparse Transform architecture and an adaptive conflict prediction mechanism are constructed in the distributed semantic inference engine, and a candidate strategy set of spectrum access and power modes is generated through a dynamic context window based on a sliding window local attention mechanism. The strategy generation module is used for performing real-time strategy generation and risk pre-judgment in a complex spectrum environment; through a conflict prediction module, performing similarity comparison on feature vectors extracted based on a multi-head attention mechanism and a learnable conflict prototype, and dynamically evaluating an interference risk coefficient of a candidate strategy; and dynamic spectrum autonomous collaborative optimization is carried out based on multi-dimensional environment state mixed characterization and an interference risk coefficient. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The invention relates to the technical field of audio noise reduction, and discloses a Bluetooth headset environment noise adaptive adjustment method and system, and the method comprises the steps: collecting multi-band sound data of headset environment noise through a preset acoustic sensor array, generating a noise spectrogram, extracting a noise masking threshold through time-frequency joint analysis, and collecting a headset audio signal; next, noise suppression is carried out on the audio signal by using a multi-order digital filter bank to obtain a primary noise reduction signal, the primary noise reduction signal is optimized into an optimized noise reduction signal based on a signal-to-noise ratio, and the frequency response matching degree of the optimized noise reduction signal and the original audio signal is calculated; then, performing phase correction and amplitude-frequency equalization processing according to the frequency response matching degree to obtain an audio equalization parameter; and finally, monitoring residual noise energy based on the parameter, generating a noise regulation and control instruction, driving adaptive sampling, and generating an adaptive noise reduction scheme. According to the invention, the audio noise reduction effect of the Bluetooth earphone can be improved.

The invention discloses a sensing data fusion-based radiator abnormal state detection method, relates to the technical field of radiator state detection, and is used for solving the problem of poor radiator state detection efficiency. According to the method, multiple types of sensors are arranged in combination with a radiator structure, acquisition time alignment is completed, segmentation and de-noising processing is performed on multi-modal data based on a sliding time window, and a frequency domain texture feature is constructed by extracting a frequency spectrum information entropy and a high-frequency energy ratio; constructing the feature values into graph nodes, establishing a complete connection graph, compressing the complete connection graph into a skeleton graph through conditional independence test, generating a directed acyclic graph in combination with an intersection structure and topological sorting, and calculating a causal weight to realize structure updating; and extracting a current window node state, comparing the current window node state with a prediction state, identifying an abnormal node, executing path backtracking, calculating path cost, tracing to a root cause node, extracting path evaluation information, and generating a response signal, thereby improving the abnormality diagnosis precision and scheduling linkage responsivity of the radiator.

The invention relates to the technical field of agricultural equipment state monitoring, and discloses an agricultural equipment state monitoring system based on the Internet of Things, which comprises an acquisition unit, an analysis unit, a stripping unit, a fusion unit and a decision unit, and is characterized in that vibration, rotating speed, torque, soil hardness and environmental data are acquired in multiple dimensions through an Internet of Things sensor network; a physical compensation factor is generated through working condition synchronous analysis, interference of soil hardness on vibration data is effectively stripped, a coupling wear index is generated through fusion of a residual feature vector and environment temperature and humidity, a state threshold value is dynamically adjusted through combination of historical fault data, and then interference of soil hardness changes on a vibration spectrum is accurately eliminated. The misjudgment of the abrasion degree caused by frequency spectrum distortion is avoided, the dynamic and accurate monitoring of the abrasion state of the harvester blade is realized, and the accuracy of monitoring the state of agricultural equipment in dry farmland operation is improved.

The invention discloses a seabed karst stratum tunnel shield data monitoring and analyzing method and system. The method comprises the steps that shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are synchronously collected; shield construction mechanical parameters, karst stratum acoustic emission signals and seawater osmotic pressure data are subjected to seepage-stress coupling constraint, a hydraulic connectivity strength index is generated through fracture network seepage tensor inversion, and the filler migration risk level is judged based on pore water pressure dynamic mutation characteristics. And fusing the cutterhead torque frequency spectrum characteristic and the propulsive force fluctuation characteristic to generate a shield disturbance entropy value. According to the method, the hydraulic connectivity index is generated through seepage stress coupling inversion, the grouting path and the rheological parameters are dynamically optimized based on the fracture probability field, shield tunneling, seepage sudden change and grouting prevention and control cooperation is achieved, and the problem of prevention and control disjunction is radically solved.

The invention relates to the technical field of electrical fault monitoring, in particular to an intelligent mobile substation electrical fault monitoring method, which comprises the following steps of: synchronously acquiring an electrical monitoring signal, a multi-dimensional environment noise signal and a dynamic working condition parameter of a mobile substation through a multi-source sensor; the method comprises the following steps: constructing an environmental noise floor vector group by adopting multi-scale spectrum mode decomposition, filtering an environmental noise interference component from an electrical monitoring signal through orthogonal projection filtering, and outputting a baseline correction signal; a working condition disturbance response field matrix is constructed based on time domain and frequency domain correlation analysis of dynamic working condition parameters, gradient sensitivity coefficients are calculated, and components strongly related to working condition disturbance and residual components weakly related to equipment faults are separated out; reconstructing the residual component into a pure fault feature vector; and finally, fault type diagnosis and risk early warning are carried out on the basis. The method effectively solves the problem of fault feature annihilation caused by noise pollution in a complex environment and the problem of false alarm and missing alarm caused by confusion of working condition disturbance and real fault signals.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.