2265 results about "Signal interference" patented technology

The invention discloses a topographic surveying and mapping system and method of an unmanned aerial vehicle, and belongs to the technical field of topographic surveying and mapping. The system comprises the following modules: a data acquisition module which is responsible for acquiring high-precision terrain and environmental parameter data of a target area by using an unmanned aerial vehicle platform; the data fusion and correction module is responsible for fusing multi-source data and meteorological information and eliminating errors among sensors; the multi-dimensional feature analysis and recognition module is used for performing feature extraction and classification on data of each sensor through multi-dimensional feature analysis, and recognizing and separating an environment interference signal and a real building deformation signal; the interference elimination and deformation extraction module is used for carrying out pattern recognition and quantitative analysis on periodic non-deformation interference and accurately extracting real deformation characteristics of a building from complex dynamic noise; the real-time analysis module is used for realizing real-time data processing and feedback and ensuring that high-precision building change information is quickly provided; and the visualization module provides multi-dimensional dynamic visualization display including a building change heat map, an interference distribution map and a time sequence curve.

The invention discloses a hazardous chemical substance safety production risk monitoring and dynamic early warning system, particularly relates to the technical field of industrial safety monitoring, and is used for solving the problem of monitoring failure caused by signal interference or offset in a mixed leakage scene of various hazardous chemical substances in the prior art. Multi-source monitoring data such as gas concentration and temperature are acquired in real time through a data acquisition module, and a conflict identification module detects reverse fluctuation characteristics of sensor data; the leakage verification module is used for reversely positioning a leakage source and verifying path shielding interference based on production pipe network topology and real-time flow data; after the physical shielding is eliminated, the physicochemical analysis module analyzes the signal counteracting characteristic of the hazardous chemical substance combination and verifies the matching between the diffusion path and the sensor layout; the dynamic compensation module dynamically corrects the monitoring data according to a physicochemical reaction mechanism; the risk assessment module generates a mixed leakage risk grade and triggers a graded emergency response instruction; accurate identification and dynamic early warning of mixed leakage risks are realized.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

The invention discloses a low-altitude unmanned aerial vehicle trajectory tracking and monitoring method based on a 5G-A communication sensing integrated base station, and relates to the technical field of low-altitude traffic management and communication sensing fusion, and the method comprises the steps: firstly collecting multi-source data such as a communication sensing fusion signal, environment interference and unmanned aerial vehicle attributes, and carrying out the alignment and packaging of a unified timestamp and a coordinate system into a synchronous data frame; then, deep fusion and anti-interference processing are carried out on the data frames, noise is filtered out, and pure fusion data is generated; and furthermore, real-time track calculation and motion trend prediction are carried out on pure data by utilizing multi-base-station cooperative calculation and prediction. Based on this, through a multi-target feature recognition and clustering separation mechanism, independent individual trajectories are accurately stripped from a complex mixed data stream, and compliance verification and anomaly judgment are performed on the trajectories in combination with an airspace rule base. In this way, the problems of signal interference and multi-target aliasing in a complex environment can be effectively solved, and therefore high-precision global tracking of the low-altitude unmanned aerial vehicle and real-time monitoring of abnormal behaviors are achieved.

The invention discloses a radio frequency signal interference intelligent suppression management system and method based on image recognition. The system comprises a multi-source sensing module, a fusion analysis module, a dynamic suppression module and an execution feedback module. The multi-source sensing module forms a radio frequency original signal set containing frequency spectrum features and time domain waveforms; the fusion analysis module receives the radio frequency original signal set and extracts an interference source feature vector in the radio frequency original signal set through a pre-trained convolutional neural network to generate a fusion decision signal; the dynamic suppression module receives the fusion decision signal and activates a corresponding algorithm in a preset suppression strategy library according to the interference type identifier; and the execution feedback module receives the real-time control signal and drives the tunable filter, the phased-array antenna and the power amplifier to execute interference suppression operation. According to the intelligent suppression management system and method for the radio-frequency signal interference based on image recognition, the problems of fuzzy positioning of the radio-frequency interference source, single suppression strategy and lack of environmental perception capability can be solved.

The invention discloses an online multi-channel rapid gas chromatographic analysis system and method, particularly relates to the technical field of gas chromatographic analysis, and is used for solving the problems of baseline noise rise and trace component detection precision reduction caused by physical coupling and signal interference when multiple detectors work in parallel in the prior art. Interference signal features of adjacent detection channels are extracted, a chaotic or linear compensation signal is dynamically generated in combination with harmonic primary and secondary sorting and recursive quantization factor analysis, and common-mode interference of a target channel is suppressed. Electromagnetic isolation parameters of a shielding structure are adjusted in real time based on the baseline signal after noise suppression, and accurate matching of impedance and interference spectrum is achieved; further separating time domain overlapped signals and analyzing asymmetric distortion characteristics, and eliminating system errors through normalization calibration; dynamic interference suppression, electromagnetic shielding optimization and signal distortion traceability are combined, the anti-interference capability and analysis precision of multi-channel detection are remarkably improved, and the method is suitable for high-throughput rapid detection scenes of complex samples.

The invention discloses an intelligent traceable hazardous waste management system based on RFID, particularly relates to the technical field of hazardous waste intelligent supervision, and is used for solving the problems of unreliable data acquisition, misreading and misreading and state updating delay caused by signal interference of an existing RFID system in a high-density hazardous waste storage scene. The method comprises the following steps: dynamically dividing warehouse sub-regions, distributing a unique grouping identification code, and combining a directional antenna array partition scanning technology to avoid signal crosstalk; by analyzing the multi-path phase fluctuation rate and the polarization direction offset, a main interference frequency band is dynamically identified by using a covariance decomposition algorithm, and the communication frequency is switched, so that the tag identification rate in a complex electromagnetic environment is improved; scheduling a target sub-region scanning process based on a warehouse-in and warehouse-out instruction, synchronously verifying a label state and an actual position, generating an operation link space-time atlas, actively tracing cross-region time logic contradictions and triggering full-disk scanning; and full-process accurate tracing of the hazardous waste container and complete data management and control in a high-density scene are realized.

The invention belongs to the technical field of medical image processing, and particularly relates to an automatic positioning and typing method for cervical posterior longitudinal ligament ossification, which is characterized in that ossification focus spatial distribution characteristics of CT images and spinal cord morphological parameters of MRI images are synchronously analyzed based on CT and MRI images, a vertebral body-ossification-spinal cord spatial relationship is explicitly modeled through anatomical structure topological constraint, and a cervical vertebra-ossification-spinal cord spatial relationship is obtained. Diffuse calcification artifacts, heterogeneity signal interference and multi-modal data registration deviation are overcome, accurate positioning and typing diagnosis of the ossification are achieved, multi-dimensional feature fusion analysis is achieved, the limitation of traditional single iconography index diagnosis is broken through, space continuity and clinical parameters are integrated through a hybrid classifier, and the accuracy of diagnosis is improved. The accuracy and robustness of typing judgment are improved, a structured diagnosis report is automatically generated in the whole process, and the clinical decision-making efficiency and the standardization level are remarkably improved; the principle is scientific and reliable, and the ossification continuity index, the spinal canal invasion rate and the spinal cord compression grading parameters are automatically calculated according to the typing standard.

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 discloses a GNSS real-time deception jamming detection method and system based on deep learning, and belongs to the field of satellite navigation signal processing, and the method comprises the steps: collecting Doppler frequency shift signals of a GNSS receiver in real time, and obtaining an initial training data set; an LSTM prediction model with an incremental learning mechanism is constructed, the LSTM prediction model comprises a double-layer LSTM network and a full connection layer, a gating mechanism is introduced into the double-layer LSTM network, and the initial training data set is adopted to add Gaussian white noise to the prediction model for model training; performing sliding window processing on the initial training data set, and extracting a multi-dimensional feature vector; inputting the trained prediction model for prediction to obtain a model prediction value; calculating a residual error between a model predicted value and a real value, calculating a standard deviation of the residual error after eliminating a preheating period, and dynamically setting a threshold value based on the standard deviation of the residual error; and when the absolute value of the residual exceeds a threshold value and the sampling point processed by the sliding window is located in a preset interference time window, judging that a signal interference event occurs.

The invention relates to the technical field of power system communication, and discloses a fault diagnosis method and system for a substation communication network, and the method comprises the steps: obtaining multi-band signal data, and carrying out the fast Fourier transform to obtain spectrum distribution data; according to the spectrum distribution data, performing frequency band division to obtain an initial power ratio; performing adaptive filtering according to the initial power ratio and the multi-band signal data to obtain a fundamental wave amplitude fluctuation characteristic; performing wavelet decomposition according to the fundamental wave amplitude fluctuation characteristics to obtain abnormal signal time-frequency energy distribution; performing K-means clustering division according to the abnormal signal time-frequency energy distribution, and matching a harmonic fault database to obtain a fault type identifier; and according to the fault type identifier and the abnormal signal time-frequency energy distribution, ring network topology propagation time delay analysis is carried out, a fault position coordinate is positioned, and a fault diagnosis report is generated. According to the method, the network fault can be accurately diagnosed for multi-band complex signal interference.

The invention relates to an unmanned aerial vehicle detection countering method, which comprises the following steps of: deploying a multi-dimensional sensing network to carry out full-band instantaneous scanning, synchronously acquiring noise characteristics of radio signals, material characteristics of a terahertz wave band and Doppler effect characteristics, mapping to a virtual twinborn body, reconstructing physical parameters, a communication protocol and a behavior mode of a target unmanned aerial vehicle, performing depth comparison by combining a disguise sample library generated by the antagonism generation network, and generating a holographic target file containing a three-dimensional motion vector, an energy feature and a potential threat intention; a target quantum encryption communication link is interfered through a quantum entanglement signal, a controllable plasma cloud cluster is generated, navigation and image transmission signals of the controllable plasma cloud cluster are selectively attenuated, the controllability of the target is judged, and an uncontrollable target is repelled through coded sound waves; and sending a control instruction containing a biological heuristic obstacle avoidance algorithm in stages, and guiding the control instruction to an intelligent recovery cabin. According to the invention, accurate detection, intelligent identification, safe countering and reliable takeover of the unmanned aerial vehicle are realized, and the airspace safety guarantee capability is improved.

The invention relates to the field of waterproof sockets, and discloses a multi-scene adaptive intelligent waterproof socket remote regulation and control system and method, and the method comprises the steps: obtaining multi-source sensing data of a socket deployment environment, and constructing a multi-scene adaptive input data set in combination with a parameter fusion strategy and a signal interference compensation mechanism; environment feature recognition is carried out on the multi-scene adaptive input data set, and typical application modes in different scenes are extracted by using a socket state tag extraction algorithm; based on the state mapping relation model, a remote regulation and control strategy optimization algorithm is adopted to dynamically analyze the operation state of the socket, and the matching degree of the current operation mode and the environment scene is identified; an intelligent judgment mechanism is introduced, pre-execution verification is carried out on the multi-scene regulation and control suggestion set, and potential control conflicts are recognized in combination with historical operation data and real-time control instructions; and performing instruction issuing management on the remote control behavior of the socket based on the optimized execution strategy. The invention has the advantage of improving the universality of the intelligent waterproof socket.

The invention relates to the technical field of lithium ion battery testing, and discloses a lithium ion battery life loss evaluation method and device, a medium and equipment, and the method comprises the following steps: S1, collecting multi-modal dynamic data of a lithium ion battery in a charge-discharge cycle process; s2, performing time-frequency domain conjoint analysis on the multi-modal dynamic data, and extracting a battery aging sensitive feature set; s3, constructing a multi-scale coupling model of battery life loss; by constructing a multi-modal data fusion mechanism and a dynamic feature extraction system, during lithium ion battery life loss evaluation, change trends of electrochemical impedance spectroscopy and heat distribution key parameters are captured in real time based on time-frequency domain conjoint analysis, sensor signal distortion and drift problems can be identified, the extraction precision of aging sensitive features is improved, and the accuracy of lithium ion battery life loss evaluation is improved. The problem of characteristic errors caused by signal interference in traditional evaluation is solved, and the accuracy and reliability of life loss evaluation are ensured.

The invention relates to the technical field of artificial intelligence, in particular to an agent integration optimization method and system based on multi-modal interaction, and the method comprises the following steps: obtaining a time point statistical frequency difference value of a multi-modal event, comparing a trend with offset, screening out a non-linkage event, extracting a semantic comparison context, positioning a modal in-situ, and obtaining a multi-modal event time point statistical frequency difference value; and calculating the priority of a response offset sorting channel, judging interlaced and densely set interference marks, screening fast and undisturbed channel rebinding content, and obtaining an integrated optimization scheme. According to the method, abnormal modal recognition is achieved through modal frequency fluctuation and response offset trend conjoint analysis, semantic fragments are combined with context continuity and task density constraints to improve screening precision, channel response delay difference sorting is clear in priority relation, and a signal interference area is positioned through amplitude mutation and time intersection nodes. And channel path elimination and content remapping guarantee response smoothness, accurate matching between expression intention and channel response is realized, and modal collaboration consistency and task execution stability are enhanced.

The invention discloses a self-adaptive suppression method and system for multi-band signal interference of a mobile terminal, and the method comprises the steps: collecting a multi-band radio-frequency signal power spectrum, extracting an interference feature vector, inputting the interference feature vector into a neural network prediction model, outputting future interference intensity probability distribution, generating a prediction frequency spectrum according to the probability distribution, and combining the current channel state information, pre-compensation processing is carried out through a double-level equalizer; the balancing strategy is dynamically adjusted according to the resource state of the mobile terminal, specifically, when the CPU load exceeds a first preset value or the electric quantity is lower than a second preset value, a second-level equalizer in the double-level equalizer is forbidden, and the steps S20-S40 are executed again till the CPU load and the electric quantity meet preset conditions; and a sliding window verification mechanism is adopted, DTW distances of the predicted frequency spectrum and the actually measured frequency spectrum are compared, and the frequency band weight coefficient of the feature extraction matrix is dynamically updated. According to the method, the signal interference suppression capability is improved through the synergistic effect of time-frequency feature enhancement, hybrid model prediction, dynamic resource scheduling and closed-loop optimization.

The invention relates to the technical field of analysis of physical and chemical properties of soil, in particular to a dry farming soil fertility rapid detection and fertility improvement method, which comprises the following steps: sorting a colloidal phase and a dissolved phase of a humic acid complex based on density difference and dielectric property difference through a gradient centrifugation and dielectrophoresis enriched physical field synergistic separation technology; a two-channel detection time sequence is optimized by adopting a genetic algorithm, a non-overlapping time sequence signal flow is generated by taking a signal interference suppression ratio as a fitness function, and time domain cross interference is suppressed. A programmable resistance-capacitance compensation model is constructed, parallel capacitor array parameters are dynamically adjusted to correct electrochemical phase angle offset, and a three-dimensional fertility distribution curved surface and a residual map are generated in combination with a Kriging interpolation model. On the basis of residual spectrum spatial distribution characteristics, a cross-linked slow-release system is deployed to improve the organic matter binding capacity of a humic acid deficiency area, a mesoporous zeolite-based passivator is applied to regulate and control an ion migration path, a closed-loop feedback mechanism is formed through dynamic phase response monitoring, and accurate detection and directional remediation of soil fertility are achieved.

The invention provides a regional interference signal detection method and device based on a Beidou router, and the method comprises the steps: obtaining reference broadcast ephemeris data of a network platform and local ephemeris data received by the Beidou router, carrying out the data integrity calculation, and judging whether the local ephemeris data are missing or not; performing same-epoch data comparison on the reference broadcast ephemeris data and the local ephemeris data, and judging whether the local ephemeris data has deception signals or not; calculating a visible satellite integrity rate in a fixed time window based on the observation data of the Beidou router, and judging whether interference signals exist in the observation data or not; analyzing by utilizing the cycle slip ratio and the carrier-to-noise ratio of the observation data, and judging whether interference signals exist in the observation data or not; under the condition that local ephemeris data do not have missing and deception signals and the observation data do not have interference, whether the observation data have the deception signals or not is judged based on pseudo-range single-point positioning and inter-epoch carrier phase difference velocity measurement, and the method can improve the reliability of Beidou signal interference detection.

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 discloses an electric power utilization anomaly detection and early warning method based on the Internet of Things, which relates to the technical field of electric power utilization and comprises the following steps of: performing fitting comparison on identified inductive current fluctuation and a behavior residual error template generated based on a real load behavior; analyzing a response time window, a fluctuation amplitude track and a behavior continuity characteristic, and judging whether the inductive current fluctuation generated by a non-target loop belongs to a real load change behavior or not under the condition that a plurality of loops are grounded together according to the response time window, the fluctuation amplitude track and the behavior continuity characteristic; and combining the behavior residual error fitting score, the phase consistency value and the frequency response stability value to generate a signal credibility judgment sequence, and carrying out credibility grade division on the inductive current fluctuation. According to the method, the problem of false fluctuation misjudgment caused by inductive signal interference under the condition that multiple loops share the grounding condition is solved, accurate identification and dynamic regulation and control on the authenticity of inductive current fluctuation are achieved, and therefore the accuracy and reliability of power utilization anomaly detection and early warning are improved.

The invention relates to the technical field of unmanned aerial vehicles, in particular to an unmanned aerial vehicle detection tracking device, method and equipment, which comprises a data sensing unit, a calculation unit, a mechanical and power supply unit, a communication and storage unit and a countering execution unit, and is characterized in that the data sensing unit is used for acquiring image data and servo turntable rotation angle data; the calculation unit is used for deploying a lightweight neural network model and a multi-algorithm fusion process, processing sensor data and realizing unmanned aerial vehicle target detection and tracking logic control; the mechanical and power supply unit is used for providing physical support, servo motion capability and energy supply for the device; the communication and storage unit is used for real-time data transmission and storage and encryption archiving of historical data and image videos; and the countering execution unit is used for implementing signal interference or physical interception on the detected and tracked target. Therefore, the problems of easy target loss, low small target identification precision, poor complex environment adaptability, insufficient countering means and the like of a traditional device are solved.

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

The invention provides an unmanned aerial vehicle navigation signal interference method and system based on an electromagnetic shielding array. According to the method, firstly, a navigation signal sent by a target unmanned aerial vehicle is monitored, positioning information transmission characteristics in the navigation signal are identified, then a periodic dynamic interference mode synchronous with a navigation signal transmission period is generated according to the positioning information transmission characteristics, and then, through spatial gradient barrier distribution of an electromagnetic shielding array, a dynamic interference mode is generated. Converting the periodic dynamic interference mode into directional space interference energy, and finally controlling the directional space interference energy to be continuously applied to a navigation signal receiving path of the unmanned aerial vehicle so as to complete navigation signal interference of the unmanned aerial vehicle. According to the technical scheme provided by the invention, a frequency hopping anti-interference mechanism of the unmanned aerial vehicle is effectively cracked, invalid energy consumption is remarkably reduced, the interference efficiency is improved, and error interference on non-target equipment is avoided.

The invention relates to the technical field of radar signal processing and quantum computing, in particular to a vehicle-mounted radar signal interference noise reduction method based on a quantum convolution region proposal network. According to the technical scheme, the vehicle-mounted radar signal interference noise reduction method based on the quantum convolution region proposal network can efficiently extract features in a vehicle-mounted radar signal by combining the parallel processing advantages of the quantum convolution region proposal network and quantum calculation, accurately identify and suppress various complex interference signals, and improve the noise reduction performance of the vehicle-mounted radar signal. Self-adaptive processing can be carried out according to the characteristics of different interference signals, it is ensured that the system can cope with variable interference sources, the parallel processing capacity of quantum computing is utilized, rapid and accurate feature extraction and interference suppression can be achieved in the signal processing process, and compared with a traditional high-cost laser radar or visual image recognition system, the system has the advantage that the system is more accurate in feature extraction and interference suppression. On the basis of the radar signal processing method and the advantage of low power consumption of quantum calculation, lower hardware cost and energy consumption can be realized.

Aspects of the present disclosure improve wireless networking performance by reducing or limiting performance issues caused by undetected or unknown interference sources and / or attenuation sources. Feedback from client devices can be utilized to reduce or limit wireless network performance issues caused by varying interference parameters and / or attenuation parameters observed by each client device. The feedback, associated with wireless communication conditions of corresponding client devices, in conjunction with a machine learning algorithm can be used to enable a dynamic selection of one or more particular operating bands, channels, and / or channel widths to avoid or limit signal interference and / or signal attenuation observed by each client device. Feedback from a plurality of client devices can be used to recommend one or more channels and / or one or more channel widths of one or more operating bands to use that reduces latency, CCI, and / or ACI and improves speed, throughput, and / or QoS.

The invention discloses a time stretching sweep frequency interference high-precision distance measurement method and device based on double auxiliary interferometers, and relates to the technical field of non-cooperative target absolute distance measurement. In order to solve the technical defects that in the prior art, due to initial wavelength fluctuation of a light source and frequency sweeping nonlinearity, drift of a ranging result is large, and interference signals are interfered by zero-frequency noise, the technical scheme provided by the invention comprises the following steps: acquiring linear frequency modulation optical signals output by a common-source frequency sweeping laser; the signals are respectively transmitted to a measurement interferometer, an auxiliary interferometer and a resampling auxiliary interferometer to form a homologous three-interference structure; a dispersive medium is introduced through the reference arm, so that the reference light generates time stretching and interferes with the measurement light to form a chirp beat frequency signal; k-domain resampling and cross-correlation processing are carried out on the beat frequency signals, and target distance information is extracted. The method is suitable for high-precision and wide-range non-cooperative target absolute distance measurement work, and is especially suitable for the fields of laser radar three-dimensional modeling, precision industrial detection, robot calibration and the like.

The invention relates to the technical field of unmanned aerial vehicle communication management, in particular to a communication method and system for inspection of an offshore wind power unmanned aerial vehicle, and the method comprises the following steps: collecting the signal receiving frequency and power information of the unmanned aerial vehicle, extracting the signal intensity and fluctuation amplitude, analyzing the signal attenuation condition during the inspection of the unmanned aerial vehicle, and evaluating the signal interference degree. And generating a signal quality evaluation result. According to the invention, the communication data of the unmanned aerial vehicle, including signal receiving frequency and power information, are collected in detail, the signal attenuation condition of the signal source is accurately analyzed, the transmission error rate and the signal-to-noise ratio are further calculated, the interference degree of each signal source is evaluated, and the signal occupancy rate and the response speed of the channel are optimized according to the signal quality. The signal transmission path is optimized, the signal packet loss rate is reduced, the time delay change is improved, the communication continuity and stability are effectively improved, and the method is suitable for special environments such as offshore wind power and the like.

The invention relates to a cooperative control method and system for realizing self-organizing protection of a multi-unmanned aerial vehicle cluster, and the method comprises the steps: M1, enabling the unmanned aerial vehicle cluster to execute a task, obtaining the data information of the remaining power, RSSI signal intensity and IMU attitude of each unmanned aerial vehicle in real time based on an airborne built-in sensor, and acquiring data information of distances and relative speeds of obstacles around the unmanned aerial vehicles in real time based on an airborne external millimeter-wave radar, constructing a dynamic threshold function stop, and if the RSSI signal strength of each unmanned aerial vehicle is greater than the stop, outputting data information of the residual electric quantity and IMU attitude of each unmanned aerial vehicle. According to the method, misjudgment (such as transient packet loss due to electromagnetic shielding) caused by temporary signal interference is effectively avoided, the misjudgment rate is reduced by 40%, unnecessary return or cluster recombination is reduced, continuous task execution is guaranteed, it is ensured that leader handover is completed quickly (at the millisecond level) when a host fails, and the situation that a cluster falls into disorder due to single-point failure is avoided.

The invention relates to the technical field of information, and discloses a radar dynamic anti-interference method and system based on interference source positioning. The method comprises the following steps: acquiring current signal data and a historical signal sequence, and determining an initial deviation value; extracting a historical deviation sequence according to the initial deviation value to obtain a deviation change trend vector; calculating a trend slope and a fluctuation amplitude, and determining an adjustment trigger signal; extracting feature interaction influence according to the adjustment trigger signal, and determining a weight coefficient update increment; iterative optimization is carried out in combination with the convergence rate parameter, and an optimized convergence rate value is obtained; adjusting deviation compensation model parameters according to the convergence speed value, and outputting a deviation compensation result when the compensation residual error is lower than a preset threshold value; and carrying out positioning calculation according to a deviation compensation result, and determining a corrected positioning coordinate. The model is dynamically optimized through technologies such as a sliding window and gradient descent, the problem of positioning deviation caused by complex environment signal interference is solved, and the positioning precision and the response speed are improved.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.