892 results about "Time signal" patented technology

The invention relates to the technical field of radar signal processing, in particular to a radar target analytical calculation method based on multi-dimensional data fusion and a radar device. Comprising the following steps: deploying a multi-band radar sensor array comprising an X band, a C band and a Ku band in a radar monitoring area; performing pulse compression and Doppler processing on the time domain echo signal, and extracting a time domain feature; spectral analysis is carried out on the frequency domain signals, and frequency domain features are extracted; performing angle estimation on the spatial signals, and extracting spatial features; a dynamic weight distribution model is constructed, a fusion weight is calculated through an adaptive algorithm based on three-dimensional quality indexes of a real-time signal-to-noise ratio (SNR), feature stability (SI) and data integrity (CI), and a joint representation vector containing time domain, frequency domain and space multi-dimensional information is generated. According to the invention, by deploying the multi-band radar sensor array, the recognition capability of the subtle feature difference of the target is improved.

The invention provides a data transmission rapid verification method of a non-standard Type-C interface, which comprises the following steps: firstly, obtaining a verification request triggered when target equipment accesses the non-standard Type-C interface, extracting interface real-time signal characteristics including a voltage fluctuation sequence and the like based on the physical connection state identifier, and sending the extracted interface real-time signal characteristics to the non-standard Type-C interface; and then dynamically matching the real-time signal characteristics with a standard transmission parameter library, generating a test instruction set containing a priority ranking verification action and a trigger condition threshold value, calling the test instruction set to perform staged signal injection and feedback capture on the interface, recording an execution result and marking a time sequence, and finally generating a verification report according to the result. And the abnormal nodes which do not meet the transmission stability index are mapped to the interface physical structure defect description, so that rapid, comprehensive and accurate data transmission verification of the non-standard Type-C interface is realized.

The invention provides a ship radar identification method based on deep learning, and relates to the technical field of ship radar signal processing. The method comprises the following steps: denoising and filtering echo signals reflected by a target, and generating spatial features and filtered target feature signals based on the denoised and filtered signals; carrying out enhancement and normalization processing on the filtered target characteristic signal, and further carrying out space-time decomposition and nonlinear fusion to obtain a fused space-time signal matrix; windowing processing is carried out on the fused space-time signal matrix, and space-time feature optimization is carried out; generating data after spatio-temporal feature fusion through a spatio-temporal feature adaptive optimization algorithm; and inputting the data after spatio-temporal feature fusion into the target recognition model, outputting a prediction result and a confidence value of the target category, and optimizing the target recognition model. The problems that a traditional radar target recognition method is difficult to effectively extract accurate spatio-temporal features and lacks adaptive ability, and the generalization ability of a target recognition model is insufficient are solved.

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

The invention provides a self-adaptive radar target detection method, system and device and a medium, and belongs to the technical field of self-adaptive radar target detection, and the method comprises the steps: carrying out the high-resolution decomposition and Gaussian fitting of a radar echo signal, analyzing the spectral centroid and spectral width characteristics, and determining the frequency domain interval of the radar echo signal; decomposing the radar echo signal into a plurality of intrinsic mode components, and screening out the mode components in a target frequency domain interval for reconstruction; key features in the reconstructed radar echo signals are extracted from the time domain dimension and the frequency domain dimension, and features with distinguishing force on target detection are reserved through a feature screening method; the method comprises the following steps: segmenting a long-time signal in a radar echo signal into a plurality of small windows, and respectively carrying out VMD decomposition and reconstruction; and carrying out target detection on the extracted key features by using a CA-CFAR algorithm. The method improves the precision of target detection under the background of complex sea conditions and strong sea clutters, and improves the accuracy and robustness of target detection.

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 provides a signal shielding method based on deep reinforcement learning, and belongs to the technical field of artificial intelligence and communication crossing. The method comprises the steps that S1, a large number of historical wireless communication signals are acquired to construct a data set to train a signal interference strategy generation model; s2, deploying the signal interference strategy generation model to a signal shielding device; s3, verifying and analyzing the signal shielding instruction to obtain a shielding parameter, collecting a real-time wireless communication signal, and inputting the real-time wireless communication signal and the shielding parameter into a deployed signal interference strategy generation model to obtain a real-time signal interference strategy; and S4, the signal shielding device modulates the real-time interference signal based on the real-time signal interference strategy, and the power amplifier performs power amplification on the real-time interference signal and then transmits the real-time interference signal to the outside through the radio frequency antenna so as to perform signal shielding. The method has the advantages that the accuracy, the real-time performance and the expansibility of signal shielding are greatly improved, and the power consumption of signal shielding is reduced.

The invention provides a remote intelligent lock control method based on a cloud platform, and relates to the technical field of artificial intelligence, and the method comprises the steps that the cloud platform issues a sound wave signal emission instruction to a target intelligent lock, and triggers the intelligent lock to send multi-band sound wave signals to a preset auxiliary signal enhancement device deployment position A and a preset auxiliary signal enhancement device deployment position B; and the cloud platform receives sound wave feedback signals returned by the position A and the position B, calculates a final communication path based on signal propagation time delay, frequency band interference intensity and attenuation parameters in combination with a real-time signal quality index reported by the remote communication module, and sends a dynamic encryption instruction to the remote communication module through the final communication path. According to the method, the optimal transmission path is dynamically calculated, the traditional single channel transmission limitation is broken through, reliable transmission of the control instruction can still be guaranteed in a complex electromagnetic environment or an obstacle shielding scene, and the communication interruption rate is reduced.

The invention discloses an industrial Internet of Things timestamp-level rapid anomaly detection method based on frequency domain spectrum difference. A non-neural network scheme is adopted to execute fine-grained industrial sensor signal anomaly detection. Comprising the following steps: proposing an evaluation index which only takes frequency spectrum difference in a frequency domain as timestamp-level anomaly detection; point-level and sequence-level spectrum difference calculation modules are designed as two complementary branches to jointly generate the spectrum difference of each timestamp in a frequency domain; designing an exception scoring strategy to generate an exception score for each timestamp in combination with point-level and sequence-level spectrum differences; wherein the point-level spectrum difference calculation module is used for identifying dominant anomalies in the industrial time signals, and the sequence-level spectrum difference calculation module is used for identifying implicit anomalies in the industrial time signals. According to the industrial Internet of Things timestamp-level rapid anomaly detection method based on the frequency domain spectrum difference, high-speed and high-precision anomaly detection can be realized on industrial Internet of Things edge equipment with limited resources.

A while-drilling multi-element signal sensing and early warning method for coal rock dynamic disasters comprises the steps that in the early stage of coal seam recovery, bedding gas extraction drilling is utilized, while-drilling signals generated in the drilling period of each drill rod are collected in real time through a multi-parameter sensing system integrated with a drilling machine, and while-drilling data are obtained based on the while-drilling signals; constructing a mechanical parameter inversion model, and calculating the compressive strength of the coal rock mass; a gas parameter inversion model is constructed, and gas pressure and coal rock mass permeability are obtained through decoupling; according to the method, coal rock mass compressive strength, gas pressure and coal rock mass permeability are used as core indexes, a spatial interpolation method is adopted to realize continuous representation of disaster risk distribution of a whole area, and a comprehensive risk index is established accordingly; and according to the value range of the comprehensive risk index, further quantifying the risk level of the coal and rock mass dynamic disaster based on the comprehensive risk index. According to the method, real-time sensing, accurate inversion and active prevention and control of coal rock dynamic disasters can be realized, and the problems of low detection accuracy, poor real-time performance, insufficient overall performance and the like in the traditional technology can be effectively solved.

The invention provides a tracking type unmanned aerial vehicle countering method and system, and belongs to the technical field of unmanned aerial vehicle countering, and the method comprises the steps: monitoring a communication frequency band of a tracking type unmanned aerial vehicle in real time, and determining real-time signal data and a signal feature vector; extracting a frequency hopping behavior of the tracking type unmanned aerial vehicle, determining frequency hopping data, and analyzing the frequency hopping data to determine a frequency hopping type; determining a communication interference signal and a frequency hopping interference signal of the unmanned aerial vehicle; transmitting a communication interference signal and a frequency hopping interference signal, and acquiring interference signal data of the tracking type unmanned aerial vehicle after the communication interference signal and the frequency hopping interference signal are transmitted; and dynamically adjusting a communication interference signal and a frequency hopping interference signal, and evaluating a countering effect. The method can optimize the countering strategy in real time, improves the pertinence and accuracy of the interference effect, guarantees the effectiveness of countering measures in a complex environment, provides a more flexible and more efficient technical means for the safety management of the unmanned aerial vehicle, remarkably enhances the defense capability, and remarkably improves the intelligent level of the monitoring and management of the unmanned aerial vehicle.

The invention discloses a multi-mode fusion time service method and system based on dynamic calibration of DCP equipment. The method comprises the following steps: completing initial time reference data acquisition based on a multi-mode satellite signal; meanwhile, time data of a ground time service network are collected, and motion parameters of DCP equipment are monitored in real time through an inertial navigation unit; establishing a correlation model of the motion parameter and the time service error; quantitative analysis of error sources is completed based on the correlation model; fusing the multi-source time data; dynamically adjusting the fusion weight of each data source according to the real-time motion state of the DCP equipment; redundant time slots are automatically distributed to high-error equipment; outputting the calibrated time signal to DCP equipment through an interface to complete clock synchronization; and continuously monitoring the time deviation of the DCP equipment, and dynamically updating the calibration parameters to form closed-loop optimization. And the environmental adaptability and the precision stability of the timing system are improved.

The invention discloses a high-reliability precise time service device, and particularly relates to the field of precise time service, and the device comprises a GNSS antenna, a Beidou satellite receiver, a main control module, a rubidium atomic frequency standard module, a frequency distribution module, a time code generation module, a network time service module, a display control module and a power module. Standard time signals are obtained through the Beidou satellite receiver, time calibration and taming are carried out through the rubidium atomic frequency standard module, and it is ensured that the output time signals and standard time are kept synchronous; the frequency distribution module distributes and amplifies a 10MHz standard frequency signal output by the rubidium clock and then divides the 10MHz standard frequency signal into multiple paths for output; the time code generation module completes 1pps and B code signals; the network time service module provides standard time information generated by the equipment to a computer with a time service requirement in a network by using an NTP standard network time service protocol; and the power supply module provides conversion from 220V to direct current through AC / DC conversion, provides power supply for each functional module and controls the power-on sequence.

The invention relates to the technical field of electric power facility anomaly monitoring, in particular to a computer monitoring system anomaly monitoring method for large-scale electric power construction engineering, which comprises the following steps of: acquiring an insulator leakage current signal, a temperature signal and an ultrasonic signal, and calculating a corresponding energy gradient; calibrating the correlation coefficient of the sensor based on the physical characteristics of an insulator material, and calculating the dynamic weight according to the energy gradient, thereby achieving the dynamic weighting of a fault sensitive signal; generating a fusion reference signal through weighted summation; constructing a dynamic threshold by combining the thermal physical parameters of the equipment, the real-time signal characteristics and the environmental parameters; calculating a signal difference quantity between the original signal and the fusion signal, and if the signal difference quantity exceeds a dynamic threshold value, triggering abnormal early warning; noise influence on a single sensor is suppressed, the signal-to-noise ratio of a fusion signal is effectively improved, the problems of abnormal missing report and false report easily caused by a fixed threshold value are avoided, and the insulator abnormal detection precision is improved.

A disaster relief system using AI-powered intelligent antennas in drone swarms, consisting of: a plurality of drones configured to collect environmental data in real time, with the drones being deployed in a disaster area environment; a smart antenna system mounted on each drone, each smart antenna system configured to perform adaptive beamforming to maintain communication links between drones; an artificial intelligence (AI) processing unit configured to receive real-time signal data, including obstacle information, drone positions, and interference patterns from the disaster environment, analyze the received signal data using reinforcement learning algorithms, generate optimized beamforming parameters based on the analysis, and transmit the optimized beamforming parameters to the intelligent antenna systems; a swarm communication protocol module configured to establish communication links between the plurality of drones using the optimized beamforming parameters, coordinate task assignments within the drone swarm, and maintain network integrity during operation; and a central control system configured to monitor all swarm operations, process environmental data, and adapt swarm behavior based on disaster relief requirements.

The invention discloses satellite communication transmission equipment, which relates to the field of communication transmission, and comprises a transmission control module used for providing access authority of a signal receiving end and a signal transmitting end and carrying out editing operation of operation instructions of all functional modules; the signal monitoring module is used for monitoring the signal-to-noise ratio, the bit error rate and the received signal strength of signal receiving according to a preset period, calculating a signal quality index in real time, and performing trend analysis and display on the signal quality index; the feedback generation module is used for aggregating the calculation signal quality indexes from the signal monitoring module and generating comprehensive feedback information; the method can quickly respond to the change of signal quality, compares the real-time signal quality with a preset standard threshold, generates a control signal, intelligently adjusts the transmitting power according to the environmental change, generates a missing report according to historical and current data, timely identifies and records a missing data packet, and uses a prediction model to predict the missing data based on the historical data and the real-time signal state. And intelligently analyzing a potential data missing trend, and generating a recovery scheme.

The invention discloses a link bandwidth allocation method and system for multilink aggregation of a multimodal mobile network. The method comprises the following steps: acquiring a mobile network base station, an equipment position and real-time environment data; an intelligent power control algorithm is adopted, and transmission power is dynamically adjusted according to environment data and network loads; acquiring real-time signal intensity data and historical data from the network equipment to the base station; predicting a minimum transmission point of signal transmission based on real-time and historical data; adjusting the direction and position of an antenna according to the position of the base station, and designing connection by adopting a beam forming antenna; setting a threshold value according to the real-time weather and the minimum transmission point data, and deploying a signal enhancer; according to the link bandwidth allocation method and system for multi-link aggregation of the multi-modal mobile network, the link quality is monitored in real time, the network is dynamically adjusted, the network load is balanced through an intelligent algorithm, the signal transmission environment is evaluated in combination with real-time weather data, the signal enhancer is deployed, and the signal coverage range is expanded.

The invention discloses a wireless network coverage optimization method based on spatial positioning and transmitting power regulation and control. The method comprises the following steps: S1, collecting real-time signal quality and link state data of a base station and user equipment; s2, dynamically adjusting the transmitting power of the base station according to the collected signal quality and link state data; s3, constructing an interference sensing matrix, further calculating a conflict weight according to the interference sensing matrix, and optimizing frequency and time slot resources based on the conflict weight; s4, adjusting a frequency subset, a time slot subset and a power upper limit according to an optimization result; s5, dynamically adjusting the transmitting power and the interference coordination strategy by receiving interference information from other base stations in real time and the feedback of the user; and S6, adjusting power distribution, spectrum resources and channel selection of the network according to the real-time feedback of the network environment. According to the method, an efficient and scientific optimization scheme can be provided in wireless network coverage optimization, and remarkable technical values and economic benefits are brought to practical application.

The invention belongs to the technical field of bearing fault diagnosis, and particularly discloses a press machine bearing fault diagnosis method and system, a terminal and a medium, and the method comprises the steps: collecting multi-source signal data of a press machine bearing in an operation process; performing denoising and standardization processing on the signal, and extracting time domain and frequency domain features; different sensor features are fused, and multi-dimensional feature representation is constructed; inputting the features into a deep learning model combining a convolutional neural network and a long-short-term memory network for training and reasoning, and introducing a multi-kernel maximum mean value difference strategy to realize feature distribution alignment; and performing fault identification on the real-time signal based on a multi-label classification mode, and triggering an alarm when the prediction probability meets a preset condition. According to the method, fusion modeling of multi-source data and accurate recognition of composite faults are achieved, and the method has high cross-working-condition adaptive capacity and real-time diagnosis capacity and is suitable for intelligent operation and maintenance scenes of press equipment.

The embodiment of the invention provides an emergency data quality control analysis method and device based on big data. Wherein a microcirculation dynamic mapping model with time-space correlation is constructed and integrated through a nanofiber sensing array of the flexible electronic skin device. And performing time reference synchronous compensation and space coordinate transformation mapping on the model and spatio-temporal data of emergency treatment monitoring equipment to form a fusion data field, thereby realizing joint feature distribution of microcirculation parameters and emergency treatment parameters. The fused data field is subjected to mode coupling verification with a microcirculation compensation track map in a shock compensation mode feature library through a dynamic quality control engine, and when it is detected that mode mismatching exists between a parameter evolution path and a historical track, space-time signal fragments are reconstructed based on phase features and space distortion features; and generating a quality control report associated with the abnormal skin perfusion gradient node. According to the technical scheme provided by the embodiment of the invention, the quality control effect of the emergency data of the shock patient is improved.

To provide a surveying system, a surveying method, and a surveying program that can be utilized also in an area in which communication of a portable telephone or the like cannot be received and can ensure precision of a survey by repeatedly performing a survey and comparing survey data.SOLUTION: A survey system includes: a reception section for receiving signals of a position and a time of a positioning satellite and a time signal, and a surveying section for surveying land. The surveying section includes: a reception section for receiving input of information on land of a survey site from a user and displaying the information on the land of the survey site; a storage section for storing predetermined information inputted by the user; a position calculation section for calculating position information of the surveying section installed in the land in the survey site on the basis of signals of the position and the time of the positioning satellite received by the reception section; and a calculation section for acquiring multiple pieces of position information of the surveying section for the surveying section to calculate a measurement difference of the pieces of position information as a difference within a set, and calculating the measurement difference of the position information of each set as a difference between sets.SELECTED DRAWING: Figure 1

The invention provides an intelligent shelter equipment operation state monitoring method and system based on machine learning, and the method comprises the steps: firstly obtaining a synchronous operation real-time signal set which comprises the real-time parameters of all operation parts, the interaction between equipment and the comprehensive influence of a shelter environment, and carrying out the mutual feedback correlation processing of the synchronous operation real-time signal set to extract a mutual feedback correlation feature set; inputting the set into an operation state deduction model to generate a short-term operation state evolution sequence, identifying a potential abnormal evolution trend and determining an abnormal association influence range based on the short-term operation state evolution sequence, and generating collaborative regulation and control demand information; then, inputting the collaborative regulation demand information and the mutual feedback correlation feature set into a collaborative regulation model to generate a multi-device collaborative operation regulation instruction, after the multi-device collaborative operation regulation instruction is sent, collecting an execution feedback signal, updating a feature extraction rule, and adjusting model parameters; the comprehensive, dynamic and accurate monitoring and regulation of the operation state of the square cabin equipment are realized, and the stability and the reliability of the equipment operation are improved.

The invention discloses a blade strain field prediction method and system based on a blade tip timing band-pass reconstruction technology. The method comprises the following steps: firstly, constructing a three-dimensional finite element model of a rotor blade; secondly, establishing a generalized band-pass sampling model of a blade tip timing signal; determining a sensor arrangement scheme; constructing a frequency domain band-pass filtering model and solving a tip timing signal time domain interpolation reconstruction function; building a to-be-tested rotor test platform according to the sensor arrangement scheme; performing time sequence reconstruction of the blade tip timing signal, and calculating to obtain a blade vibration displacement reconstruction signal by using the measured blade tip timing signal and the interpolation reconstruction function; establishing a transient strain field reconstruction model based on a displacement-modal transfer ratio; and finally, blade transient strain field reconstruction and result visual output are carried out. According to the method, the number of the sensors and the sampling period can be controlled to be minimized while the blade tip timing signal reconstruction accuracy is ensured, so that the applicable range of the method is widened.

The invention discloses a high-precision high-speed pipeline time-to-digital converter. The converter comprises a pulse selection circuit, a parallel delay line TDC, a margin extraction module, a time register type TDC, a vernier type TDC and a digital encoder. The pulse selection circuit converts an input bipolar time signal into a unipolar time signal and generates a polarity bit, the three TDCs are sequentially in transmission connection to form a three-stage running water working mode, and the first-stage parallel delay line TDC is used for roughly quantizing and determining a value of a high binary bit; meanwhile, the output end is connected with the margin extraction module to generate time margin and transmit the time margin to the second stage for fine quantization, thereby compensating the quantization error of the first stage and improving the accuracy. And after the second-stage time register type TDC is quantized, the time margin can be directly generated and transmitted to the third-stage vernier type TDC for fine quantization, so that the use of a digital time converter is reduced, and the conversion efficiency is improved. And the digital encoder encodes and outputs the TDC quantized data.

The embodiment of the invention provides a high-stability crystal oscillator taming device, system and method, and solves the problems that in the prior art, the precision is insufficient, the stability is insufficient, and the time cannot be kept at high precision by depending on a GPS (Global Positioning System) or under a specific condition. The high-stability crystal oscillator taming equipment comprises a judgment module which is used for receiving the time signal of the satellite and carrying out validity judgment; and the phase detection module is used for receiving a second signal sent by the high-stability crystal oscillator to be detected and performing phase difference detection on the second signal and a time signal of a satellite to obtain a comparison signal. And the aging module and the temperature module respectively carry out aging compensation and temperature compensation on the comparison signal and then send the comparison signal to the adjusting module. And the adjusting module is used for converting the comparison signal into an adjusting signal and sending the adjusting signal to the to-be-tested high-stability crystal oscillator, and is used for adjusting the output frequency of the high-stability crystal oscillator. The frequency accuracy and the long-term frequency stability of the high-stability crystal oscillator are effectively improved by combining Kalman filtering, aging compensation, temperature compensation, PID (Proportion Integration Differentiation) adjustment and other technologies.

The invention discloses a fetal state dynamic monitoring method and system based on multi-modal deep learning, and the method comprises the steps: segmenting a fetal heart rate signal and a uterine contraction signal into a plurality of segmented time signals through a sliding window, and carrying out the denoising preprocessing of each segmented signal; performing wavelet transformation on the signal to obtain a time-frequency spectrogram, and inputting the time-frequency spectrogram into a 3D convolutional neural network to extract features; a cross attention module is introduced to fuse the two modal features, a transform encoder is put into use to complete time sequence feature extraction, and finally fetal state monitoring is achieved through a full connection layer. According to the method, the multimodal fusion and deep learning technology is utilized, and the fetal state can be accurately and dynamically monitored. Manual monitoring errors can be effectively reduced, the monitoring efficiency and accuracy are improved, powerful support is provided for medical staff to know the fetus condition in real time, the abnormal state of the fetus can be found in time, and the safety of the fetus is guaranteed.

The invention relates to the technical field of flight verification, and discloses a flight verification processing method and system for a land-based navigation system, and the method comprises the steps: creating a flight verification task of land-based navigation equipment, and configuring a self-positioning device for verifying an airplane; the autonomous positioning information of the verification aircraft under the autonomous positioning device is collected, the real-time signal data of the land-based navigation equipment in the verification flight task is collected, and the signal deviation of the land-based navigation equipment is detected in real time; setting a signal deviation early warning mechanism of the land-based navigation equipment, configuring a multi-index monitoring network of the land-based navigation equipment, identifying and verifying the flight interval of an airplane, and creating a parameter dynamic calibration system of the land-based navigation equipment; and in combination with an autonomous positioning device, a multi-index monitoring network, a signal deviation early warning mechanism and a parameter dynamic calibration system, executing flight verification processing of the land-based navigation equipment to obtain a flight verification result. According to the invention, the accuracy of land-based navigation system performance evaluation can be improved.

The invention relates to a detection-interference integrated unmanned aerial vehicle electromagnetic interference device, which belongs to the technical field of electronic countermeasure, and comprises an integrated design of an antenna module, a radio frequency front end module, a digital signal processing module and a main control module, the radio frequency front-end module comprises a detection receiving channel and an interference transmitting channel to process signal conversion, the digital signal processing module executes signal detection and analysis, intelligent interference decision and anti-self-interference signal processing based on a self-adaptive filtering algorithm, and the main control module is responsible for overall control and power supply. According to the invention, the technical problems of large response delay, complex system cooperation and incapability of continuous reconnaissance in the interference process of traditional anti-unmanned aerial vehicle equipment are solved. Real-time signal cancellation is achieved through the normalized least mean square algorithm, the reconnaissance capability is kept while interference is applied, the method has the advantages of being rapid in response, accurate in interference, low in power consumption and the like, and the countering efficiency is remarkably improved.

The invention discloses a power distribution network fault early warning method and system based on online monitoring, and relates to the field of fault early warning, and the method comprises the steps: continuously collecting real-time electrical signals; signal waveform prediction of the real-time electrical signal is completed according to the real-time signal prediction result, and whether an early fault signal exists in the target power distribution network is judged; if the target power distribution network has the early fault signal, obtaining power distribution network information, environment prediction data and geographic position data; constructing a topological structure of the power distribution network, and extracting early fault information of the target power distribution network; constructing an icing evolution model of the target power distribution network; completing fault risk coupling between the icing evolution model and the early fault information; and carrying out risk accumulation analysis on the topological structure of the power distribution network, and outputting fault early warning information of the target power distribution network according to a risk accumulation analysis result. According to the invention, the power distribution network fault early warning precision and efficiency can be effectively improved.

The invention provides a radio frequency multichannel amplitude-phase calibration method based on a full-automatic error variable, and the method comprises the steps: carrying out the length-set sampling data of the amplitude of a real-time signal, carrying out the fast Fourier transform, and carrying out the amplitude-phase error estimation, thereby forming a full-automatic error variable reference, and carrying out the calibration of the amplitude-phase of the full-automatic error variable. Therefore, the automatic calibration of the amplitude phase error between the channels is conveniently and quickly realized, the calibration precision is high, the amplitude and phase difference between the channels can be effectively eliminated, the directivity accuracy of the system beam is ensured, and the evaluation system is ensured to simulate the orientation, distance and other related information of the target according to actual combat requirements. And rapid and efficient evaluation on the receiving and transmitting channel performance of the radar can be realized.