4354 results about "Signal source" patented technology

The invention provides an automatic walking and positioning method and system for a signal positioning and measuring robot in a complex environment, and relates to the field of technical positioning and measuring robots, and the method comprises the steps: obtaining an indoor three-dimensional map and multi-mode perception data, carrying out the semantic segmentation through a graph neural network, and achieving the environment matching through the combination of a point cloud registration technology; and generating robot motion track planning data. Multi-dimensional signal features are collected through a phased-array antenna system, and a signal feature distribution diagram is constructed in combination with a robot pose and an environment electromagnetic propagation model. A partitioning strategy and a multi-constraint optimization algorithm are adopted to calculate three-dimensional coordinates of a signal source, and finally, the signal source is accurately positioned through a depth calibration network.

The invention relates to the technical field of electromagnetic pulses, and discloses an electromagnetic pulse data transmission method. The method comprises the following steps: collecting natural electromagnetic pulse signal data of a detection target area through a six-dimensional complementary electrode array structure, and executing Kalman filtering peak extraction and characteristic frequency locking to obtain frequency-optimized signal data; executing first-order and second-order gradient difference matrix calculation and space vector synthesis to obtain signal source position coordinate data and signal intensity data; performing spectral domain reconstruction and multilayer dynamic threshold analysis to obtain a spatial clustering abnormal data packet; and selecting an optimal transmission channel to transmit the spatial clustering abnormal data packet to a cloud server. According to the invention, the influence of an external interference source is effectively eliminated, accurate extraction and characteristic frequency locking of effective frequency band signals are realized, power frequency and harmonic interference thereof are effectively suppressed, the signal-to-noise ratio of the effective frequency band of the signals is improved, and the reliability and timeliness of data transmission in a severe field environment are ensured.

The invention provides an antenna radio frequency performance test system and method, and relates to the technical field of antenna testing, the system adopts an intelligent signal source module, uses a quantum random number generator to generate a test signal of an ultra-wide frequency spectrum, and an adaptive matching network module can automatically adjust impedance according to impedance characteristics of an antenna to be tested. The space electromagnetic environment reconstruction module accurately simulates various electromagnetic environments through an electromagnetic radiation source and a wave-absorbing material, the multi-dimensional signal acquisition module simultaneously acquires electric field, magnetic field, phase and frequency spectrum information, and the intelligent analysis and diagnosis module analyzes signal data by using an artificial intelligence algorithm and performs fault diagnosis based on a machine learning model. In general, the system and the method improve the comprehensiveness, the accuracy and the intelligentization of the antenna test, and provide a new test solution for the field of wireless communication.

The invention relates to the technical field of traffic monitoring, in particular to a Beidou-based traffic flow real-time monitoring method and system, and the method comprises the following steps: receiving signal data and analyzing a signal source based on a Beidou short message communication terminal, extracting vehicle positioning information and road occupation information in vehicle operation state data, and transmitting the vehicle positioning information and the road occupation information to a Beidou short message communication terminal; and analyzing the vehicle speed and the traffic flow density, and carrying out matching operation on the analysis result and the road section number to obtain traffic flow state data. According to the method, vehicle positioning and road occupation information is extracted through signal analysis, efficient acquisition and integration of traffic state data are realized, road section numbers are matched to improve data space precision, traffic pressure is reflected, dynamic flow analysis is supported, adjacent road section data are integrated, abnormal areas are positioned, shunting paths and proportions are optimized, and emergency response efficiency is improved. Real-time regulation and control of traffic flow are realized through dynamic pushing, the flexibility and responsiveness of traffic operation are enhanced, and the monitoring precision and the road network operation efficiency are comprehensively improved.

The invention relates to the technical field of unmanned aerial vehicle remote identification, in particular to an unmanned aerial vehicle flight path detection method and system, and the method comprises the following steps: obtaining an airspace broadcast signal, detecting a newly added signal, correcting a terminal time difference, recognizing an information source position, extracting a position sequence, reconstructing an unmanned aerial vehicle flight path, comparing a declaration path, and recognizing the identity of an unmanned aerial vehicle. And detecting an abnormal unmanned aerial vehicle and an abnormal flight path, extracting a broadcast time sequence, identifying a time overlapping interval, detecting a remote control end, and obtaining a control identification result. According to the invention, by monitoring the airspace signal in real time and dynamically detecting the newly added signal source, the monitoring precision of the signal source is improved, the time difference correction and space cross reconstruction of a plurality of monitoring terminals are combined, the positioning precision of the unmanned aerial vehicle position is optimized, and a track reconstruction method is adopted, so that the real-time monitoring capability of abnormal behaviors is enhanced; by identifying the information source of the control end and extracting the position information, the airspace safety management and illegal flight prevention capability is improved.

The invention discloses an unmanned aerial vehicle antenna dynamic adjustment method and device under single Beidou support, and relates to the technical field of antenna control, and the method comprises the steps: carrying out the positioning of a target unmanned aerial vehicle, and obtaining a spatial positioning data signal; transmitting and receiving signals to obtain a signal transmitting and receiving record log; analyzing the flight area of the target unmanned aerial vehicle to obtain terrain environment parameters, performing flight analysis, and generating flight attitude change information; performing operation calculation on an antenna module of the target unmanned aerial vehicle, generating an antenna dynamic operation coefficient, and formulating an antenna adjustment strategy; and performing optimization, generating an antenna adjustment optimization strategy, and performing dynamic adjustment. The technical problem of poor communication and positioning accuracy and stability caused by insufficient antenna adjustment precision of the unmanned aerial vehicle in a complex environment in the prior art is solved, and the technical effects of realizing accurate tracking of the antenna of the unmanned aerial vehicle on the target signal source in the complex environment and improving the accuracy and stability of communication and positioning of the unmanned aerial vehicle are achieved.

The invention discloses an extensible deep reinforcement learning unmanned aerial vehicle cluster collaborative search method based on digital twinning. According to the method, an extensible multi-agent reinforcement learning algorithm SAMARL is adopted, a multi-agent near-end strategy optimization algorithm and a multi-head attention mechanism are fused, and the effectiveness and the adaptability of the algorithm are improved. In an SAMARL framework, a complex observation space containing graphic representation and an environment cognitive map is constructed, and the target search rate, the area coverage rate, the search time and the collaborative security are comprehensively optimized. An observation space based on a view field range is designed, and the model training speed is improved. Then, a search training framework of'twin training, distributed execution and persistent evolution 'is provided, and migration of the decision model from simulation to reality is achieved; the intelligent decision model is deployed to each unmanned aerial vehicle, and the cluster uses a field intensity meter and a communication radio station to cooperatively search unknown signal sources in a distributed manner. A large number of experimental results show that the method is superior to an existing advanced strategy in search efficiency and system expansibility.

The invention discloses a multi-scene positioning and intelligent warning system and method for industrial equipment, and the system comprises a track analysis module which obtains the radar signal data of the industrial equipment, carries out the denoising, extracts a signal intensity change feature, and generates a human body dynamic track when the matching degree of the feature and a preset metal reflection model exceeds a threshold value; the abnormity judgment module is used for acquiring the vibration sensing data of the key position, generating a vibration frequency spectrum by applying wavelet decomposition, and generating abnormal vibration alarm information if the vibration frequency spectrum exceeds a range; the intrusion alarm module is used for acquiring node signal sources, performing weighted fusion to obtain an initial positioning coordinate group, inputting the initial positioning coordinate group into a three-dimensional map model, obtaining a final coordinate result through multi-source fusion, and sending out intrusion alarm information if the result falls into a dangerous fence boundary; and the safety evaluation module is used for performing time alignment and deep fusion on the human body dynamic track, the abnormal vibration alarm information and the intrusion alarm information to generate a safety evaluation report. According to the invention, omnibearing safety monitoring and management of industrial equipment are realized.

The invention relates to the technical field of dynamic weighing, and discloses an intelligent load prediction and adjustment method in a dynamic weighing system, which comprises the following steps of: separating a weighing signal into a space domain static load characteristic (pressure point distribution sparsity and gravity center geometric vector) and a time domain dynamic inertial interference component (acceleration coupling noise) through space-time double-domain decoupling; the frequency spectrum aliasing problem of a traditional filtering algorithm is eliminated from a signal source; the future interference trend is predicted based on the real-time motion state of the carrier, a feed-forward compensation signal is generated through a pre-training dynamic basis function library, and dynamic errors are actively counteracted; in combination with sensor array space consistency detection and lightweight online learning, decoupling model parameters are dynamically corrected. According to the method, space geometric analysis and kinematics feedforward prediction are combined, a traditional signal processing framework is strided out, error source separation and real-time compensation are achieved, and the dynamic weighing precision of transient working conditions such as sudden stop and speed change is remarkably improved.

The invention discloses a single-bit direction of arrival estimation method and system with signal amplitude recovery, a terminal and a storage medium, and the method comprises the steps: obtaining each snapshot time signal from a signal source of a uniform linear array receiver, introducing a non-zero threshold value to carry out the single-bit quantization of each snapshot time signal, and obtaining a signal model; reconstructing the signal model, replacing a sign function of the reconstructed signal model with a smooth hyperbolic tangent function, and performing reconstruction to obtain a target signal model; based on the target signal model, constructing a single-bit DOA estimation optimization problem by using a truncated Frobenius norm and an L2, 0 norm, and converting the optimization problem into a target optimization problem by using a semi-quadratic technology; and solving a target optimization problem by using a near-end alternating minimization algorithm and a forward-backward splitting algorithm to obtain a signal source estimation direction, a signal source estimation number and a signal estimation amplitude. According to the method, the amplitude of the signal source is accurately estimated while the prior dependence on the number of the signal source is eliminated.

The invention relates to the field of wearable equipment, and discloses a data processing method for assisting old people based on a smart watch, and the method comprises the steps: building an old people individual physiological baseline library at a smart watch end, and carrying out the dynamic comparison of the dislocation difference between a real-time collection signal and the baseline library; performing behavior interference separation on the individual compensation sequence, and generating a health time sequence feature block after interference stripping based on a multi-scene embedding model; an event entropy triggering mechanism is introduced into the health time sequence feature block, and when the drift rate exceeds an individual self-adaptive threshold value, a candidate health abnormal event is automatically marked; calibrating a signal source corresponding to the candidate health abnormal event as a local feedback loop node, and performing coupling correction on a signal in a local feedback loop; and based on the health monitoring priority feedback map, in combination with the global circadian rhythm change trend and the accumulated weight of the abnormal event, generating a multi-level interaction prompt instruction. The method has the advantage of improving the accuracy of the health monitoring data of the old people.

The invention provides an electric energy meter PCB function test method and device, a medium and equipment, and the method comprises the steps: obtaining a multi-channel signal source, automatically adjusting the characteristics of an input signal based on a feedback signal of an electric energy meter through a self-adaptive algorithm, and determining the consistency of the working characteristics of the input signal and the working characteristics of the electric energy meter; a virtual power grid environment simulation model is constructed, through integrated power simulation software and dynamic interference simulation, simulation signal source automatic adjustment signal characteristics are input into the virtual power grid environment simulation model based on the integrated power simulation software to synthesize a composite signal, and load fluctuation of an electric energy meter in actual operation is simulated based on the composite signal. Determining an electric energy meter test data set; according to the invention, the fault mode of the electric energy meter can be automatically identified, the test data can be analyzed in real time, and fault diagnosis and optimization suggestions can be automatically given. The intelligent diagnosis mode is far better than a traditional manual detection method, and the speed and accuracy of fault diagnosis can be remarkably improved.

The invention discloses a zero-sequence pilot-frequency excitation method for a double-circuit line in an incomplete power failure environment, which comprises the following steps of: applying zero-sequence pilot-frequency signal excitation on a power failure line by using a pilot-frequency power supply as an excitation signal source of a zero-sequence network of the double-circuit line, different-frequency induced current and voltage are measured at secondary sides of a current transformer and a voltage transformer at two sides of the operation line, and different-frequency zero-sequence voltage and different-frequency zero-sequence current in the double-circuit line zero-sequence network are obtained; simultaneously solving a zero-sequence network equation, and calculating mutual impedance between double circuits; performing frequency conversion to obtain the power frequency mutual impedance of the line; and finally, carrying out simulation verification by constructing a non-full-stop double-circuit line zero-sequence mutual inductance impedance measurement system. According to the method, the multi-path coupling caused by the existing power frequency interference method and the accuracy problem caused by the signal measurement error of the zero sequence increment method are solved, the test calculation can be accurately completed under the complex interference working condition, and the safety risk caused by the single-phase reclosing operation is avoided.

The invention discloses an eSIM service cost intelligent optimization method based on different operator information sources, and particularly relates to the technical field of operator network management. The method comprises the following steps: collecting eSIM tariff rules of a plurality of operators and real-time charging data of eSIM terminals, extracting tariff terms and conditional expense trigger mechanisms hidden in charging behaviors of different operators by using an association rule mining algorithm, and constructing a multi-level cost structure model; on the basis of a network registration behavior log of the eSIM terminal, predicting the probability that each operator signal source has an abnormal registration event, and evaluating the risk that each operator signal source triggers a hidden cost trap in combination with a multi-level cost structure model; and according to the real-time network signal parameters of the eSIM terminal, a signal source recommendation strategy is generated in combination with the risk assessment result, and the eSIM terminal is driven to execute a signal source switching operation, so that real-time adjustment and cost optimization control of communication network selection are realized, and the service stability of the eSIM in a cross-network use scene is improved.

The invention provides a signal blind separation and intelligent reconstruction method and system in a complex scene, and relates to the technical field of signal processing, and the method comprises the steps: receiving an aliasing signal, and converting the aliasing signal into a multi-channel signal observation matrix; performing decomposition in a wavelet domain to obtain a wavelet coefficient, matching the wavelet coefficient with the sparse dictionary, and reconstructing a target signal source after optimization; estimating the number of signal sources based on covariance matrix eigenvalue distribution; mapping a signal source to a graph structure domain, extracting space and time sequence correlation through a mixed graph convolutional network, and obtaining a target separation signal through variational reasoning optimization; and finally, carrying out quality evaluation and post-processing to obtain a final reconstruction signal. According to the invention, the signal separation precision and robustness in a complex scene are improved.

The invention relates to the field of partial discharge source positioning, in particular to a large-scale substation multi-partial discharge source positioning method and system, and the method comprises the steps: forming a sensor array through a movable ultrasonic sensor and an ultrahigh frequency sensor, and collecting ultrasonic signals and ultrahigh frequency signals generated by partial discharge sources respectively; denoising the collected discharge signals, and calculating the distance between the partial discharge source and the sensor array; denoised ultrasonic array signals are divided into a plurality of narrowband signals by discrete Fourier transform, weighted covariance matrixes of all narrowband signals after focusing transformation are subjected to mean value calculation to obtain weighted covariance matrixes of focusing frequency points, and the weighted covariance matrixes of the focusing frequency points are subjected to unitary transformation to obtain a weighted covariance matrix of a plurality of narrowband signals. Estimating the number of partial discharge sources according to a signal source number estimation equation; and carrying out spectrum peak search in a specified space parameter range according to a preset step length to obtain a space parameter corresponding to each partial discharge source, and obtaining a partial discharge source coordinate in a three-dimensional space.

The present invention discloses a computer motherboard automated integrated test and intelligent diagnosis system and method, which relates to the field of computer motherboard test diagnosis technology, including a test platform building module, multi-interface adaptation and automatic motherboard identification, including a high-precision signal source and acquisition card, a modular architecture and high-speed bus interconnection; a test project execution module, which carries out multiple functional tests and simulates multiple scenarios to adjust the load; a data acquisition and storage module, which synchronously acquires multiple types of data through multiple channels, and has distributed storage and redundant backup; an intelligent diagnosis and analysis module, big data and machine learning algorithm diagnosis, a report generation and output module, a template engine generates reports and optimizes layout; a system self-check and calibration module, which starts each self-check module. The present invention realizes efficient and accurate testing of computer motherboards, reliable data acquisition and storage, modular design facilitates upgrading, and convenient remote functions, improves test and diagnosis efficiency and quality, helps motherboard production, maintenance and research and development, and enhances industry competitiveness.

The invention relates to the technical field of numerical control system fault pre-diagnosis, and particularly discloses a fault monitoring method and system for a numerical control machine tool. The method comprises the steps that a spindle vibration signal, a cutting force signal and a spindle current signal are collected; outputting an alignment signal set; respectively executing adaptive filtering processing according to the alignment signal set; constructing a multi-dimensional feature vector; constructing a health index under the processing behavior period; then accumulated trend deviation analysis is executed through a CUSUM algorithm; and judging whether the tool wear trend is abnormal. The technical problem that in the prior art, static threshold judgment is carried out based on a single signal source, and especially in a numerical control machining scene with signal noise interference, accurate early warning of the early wear trend of a tool cannot be achieved is solved. Due to the fact that a period calibration mechanism fusing spindle vibration, cutting force and current signals, feature vector entropy weight optimization and a CUSUM trend detection mechanism are adopted, the pre-diagnosis level of the numerical control machine tool on cutter state changes is improved.

The invention provides a broadband tunable low-phase-noise photo-generated microwave signal source and device, and relates to the technical field of photo-generated microwaves. According to the invention, a low-frequency optical pulse and a high-frequency microwave signal are respectively input into the electro-optical sampling phase-locked loop; and the electro-optical sampling phase-locked loop determines the synchronization error of the optical pulse and the microwave signal based on the phase difference between the optical pulse center point and the microwave signal zero crossing point. And taking the synchronization error as a feedback signal, and continuously adjusting the frequency and the phase of the microwave signal by the voltage-controlled oscillation module until the frequency and the phase are synchronized with the Nth harmonic of the optical pulse. By setting different initial frequencies of the voltage-controlled oscillation module, microwave signals with different integral multiple optical pulse frequencies can be obtained, and broadband tuning of photo-generated microwave signals is realized. Furthermore, the repetition frequency of the frequency comb is locked through beat frequency of two beams of reference laser and two adjacent comb teeth, the stability of the reference laser is transmitted to the optical pulse, and the stability of the reference laser is further transmitted to the microwave signal through synchronization of the optical pulse and the microwave signal, so that the phase noise of the photo-generated microwave signal source is reduced.

The invention discloses an intelligent driving vehicle dynamic network redundancy configuration and switching method in a complex information source scene, and relates to the technical field of intelligent traffic and communication. According to the method, communication quality data of a plurality of communication links at different positions of the unmanned taxi in the driving process are detected, the data are broadcasted to other unmanned taxies around through a vehicle-mounted communication system, and communication quality data broadcasted by other unmanned taxies are received; the method does not depend on a centralized server or a cloud platform, and a signal map is fused and generated in a decentralized manner only based on self-detected and received data; dynamically configuring a network redundancy strategy according to the signal map and a preset task priority; after the strategy is executed, scores are recorded and broadcasted, scores of other vehicles are received, and the strategy is optimized and adjusted in advance. According to the method, the problem of unstable communication in a complex information source scene is solved, and the communication reliability and efficiency of the intelligent driving vehicle are improved.

The invention discloses an equipment management system and a server, and relates to the technical field of servers, and the equipment management system is characterized in that a topology identifier storage assembly for storing slot position signal source identifiers is moved to a server mainboard end, so that the occupation of mainboard connector pins by information transmission is reduced; and meanwhile, the management control component establishes and stores a corresponding relation between the address of the arbitration component and the screen printing area of the case. The adapter card does not need to be pre-bound and configured, can automatically identify the screen printing area of the case through the relationship by inserting any communication interface of the mainboard, and generates slot screen printing information in combination with the pre-stored signal source identifier. The problems that a large number of pins are occupied due to the fact that the adapter card hardware address signal identification configuration needs to be transmitted through a connector, and the adapter card needs to be bound with the fixed configuration and lacks flexibility in the prior art are solved. The technical effects of reducing pin occupation, realizing flexible plugging of the adapter card and automatic identification of screen printing, reducing hardware development cost and meeting the requirement of the server for reserving more bus interfaces for the connector are achieved.

The invention provides a speed reducer fault positioning method and system based on artificial intelligence, and relates to the technical field of intelligent fault detection and positioning of industrial equipment. The method comprises the following steps: synchronously acquiring vibration, temperature and acoustic emission signals by using a multi-mode sensing module, carrying out filtering, correction and improved wavelet packet decomposition processing through an edge calculation unit, extracting time domain, frequency domain and nonlinear characteristics, calculating a vibration degradation degree and a state deviation index to construct a characteristic space, and calculating a state deviation index; fault large-class identification and fault source accurate positioning are realized through deep metric learning and a graph attention network, and credibility evaluation and online incremental learning optimization are carried out in combination with a D-S evidence theory. The system comprises a multi-modal sensing module, an edge computing unit, a cloud analysis platform, a self-adaptive optimization module and a man-machine interaction terminal. The method overcomes the defects that in a traditional method, a signal source is single, feature extraction depends on experience, real-time performance is insufficient, positioning is inaccurate and the like, and fault detection accuracy and equipment operation safety are remarkably improved.

A switchable patch antenna comprises a planar conductor having an aperture (hole) formed in the middle of the planar conductor. Radiation of a sinusoidal signal is controlled by comparison of separate impedance values for two components that have separate impedance values. Each of the two components have one end coupled together at the terminal positioned at a center of the aperture and their other ends separately coupled to opposing edges of the aperture. A sinusoidal signal source is also coupled to the terminal positioned at the aperture's center. Further, when the impedance values of both components are substantially equivalent, radiation by the antenna of the provided signal and / or mutual coupling of other signals is disabled. Also, when an impedance value of one of the two components is substantially greater than the other impedance value of the other component, the provided signal is radiated and / or mutual coupling is enabled.

The invention discloses a signal sampling method for a multi-channel signal converter, and the method comprises the steps: carrying out the synchronous and parallel sampling of an analog signal source according to a multi-channel signal collection module, so as to obtain multi-channel original signal sequence data; performing inter-channel time delay calibration and amplitude normalization processing on the multichannel original signal sequence data to obtain a calibrated multichannel signal data set; the invention relates to the technical field of signal processing. According to the signal sampling method for the multi-channel signal converter, the multi-channel signal converter improves the data consistency and accuracy through the technologies of synchronous sampling, time delay calibration, amplitude normalization and the like, meanwhile, precision adjustment of self-adaptive filtering, dynamic threshold segmentation and entropy driving is adopted, signal interference and abnormal data are reduced, and the signal quality is improved. The non-uniform resampling and compression technology is adopted to optimize the storage and transmission efficiency, key data is ensured to be transmitted preferentially, hardware is accelerated through an FPGA, and the real-time performance and the processing capacity of the system are improved.

The invention relates to the technical field of machine learning, and discloses a radio measurement calibration method and system based on machine learning, and the method comprises the steps: carrying out the parameter measurement of a radio signal source, and obtaining an original measurement data set; based on the original measurement data set, extracting a frequency precision feature, a power flatness feature, a phase noise feature, a harmonic ratio feature and an environment change feature to obtain a feature data set; dividing the feature data set into a training data subset, a verification data subset and a test data subset, and constructing a first neural network model containing a double-branch neural network based on the training data subset; and inputting the verification data subset into the first neural network model for model parameter optimization to obtain a second neural network model, thereby improving the calculation precision of calibration parameters and the accuracy of environment compensation.

The invention discloses a spatio-temporal cross-scale dimension reduction characterization method, system and device for a multi-modal physiological signal. The method comprises the steps of multi-modal signal time sequence alignment, sliding variable scale segmentation, single-modal spatio-temporal feature pre-training, cross-modal coupling alignment and model migration application. In the single-mode spatial-temporal feature pre-training, single-mode signal fragments under different time processes or spatial arrangements are reconstructed through a codec, and multi-level spatial-temporal modeling and analysis of a signal source are realized; in cross-modal coupling alignment, spatial-temporal characteristics of physiological signals of other modals are reconstructed through a codec, and a coupling relationship among different modals of a physiological system is learned; the model after cross-modal coupling alignment can be migrated to a specific task for fine tuning, and the comprehensive characterization capability of physiological signals in a few modals, specified space or limited time collected by a backward device on a physiological system is enhanced. According to the method, through bottom-layer dimension reduction and migration, effective characterization of the whole complex physiological state by the easily obtained signals is achieved.

The invention provides a transformer partial discharge early warning system and method in combination with time-frequency characteristics, and relates to the technical field of partial discharge monitoring and early warning, and the method comprises the steps: inputting a partial discharge signal into a multipath signal separation model for superposed signal separation, and outputting a plurality of separated partial discharge signals; performing time-frequency feature extraction, and outputting multiple groups of time-frequency feature vectors; performing pattern recognition on the multiple groups of time-frequency feature vectors by using a partial discharge feature library, and outputting multiple discharge early warning signals; and carrying out partial discharge early warning reminding on an upper computer of the transformer according to the plurality of discharge early warning signals. According to the invention, the technical problem that the accuracy and precision of partial discharge early warning are insufficient due to the fact that complex aliasing signals of different propagation paths cannot be effectively separated in a traditional early warning method can be solved; signal interference of different paths can be effectively eliminated, the characteristic of each signal source is ensured to be clearer, and thus the precision and accuracy of transformer partial discharge early warning are remarkably improved.

The present disclosure relates to the technical field of electromagnetic wave sensors, in particular to a signal transmitting, calibration, compensation and transceiving links, an integrated circuit, an electromagnetic wave sensor and a device, wherein the transmitting link includes an analog signal source and a digital phase shifter, the analog signal source may be configured to provide an initial analog signal, and the digital phase shifter may be configured to provide a phase shift signal in a digital domain and to perform phase shifting on the initial analog signal based on the phase shift signal, so as to perform a preset phase shifting operation on the initial analog signal, thereby effectively improving the phase modulation resolution and phase modulation accuracy.

The invention relates to the technical field of cable asset health management and predictive maintenance, and discloses a cable discharge signal blind separation and enhancement processing method based on an adversarial network, and the method comprises the steps: building a multi-modal monitoring data set through collecting mixed signals and environment data in cable operation; blind separation of discharge signals is realized by using the generative adversarial network, and prior information is not needed; identifying the number of potential signal sources through covariance analysis and double-criterion estimation; iterative optimization and signal enhancement are carried out in combination with a graph neural network and variational reasoning; and finally, through multiple cross validation and quality correction, an enhanced signal with high reliability is output. According to the method, the signal processing technology is deeply fused with asset management, risk prediction and operation and maintenance decision, weak discharge signals can be effectively separated and enhanced under the condition of low signal-to-noise ratio, the accuracy and reliability of cable early fault diagnosis are improved, and credible data support is provided for cable asset health state assessment, risk prediction and operation and maintenance decision.