2480 results about "Signal quality" patented technology

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention discloses a power distribution network distributed measurement synchronization method and device based on Beidou satellite time service, and belongs to the technical field of power distribution network measurement, and the method comprises the steps: obtaining a Beidou satellite signal, calibrating a local clock source, and generating a standard time reference; when it is continuously monitored that the Beidou satellite signal quality parameter is lower than a quality threshold value, clock working environment parameters of a local clock source are collected, and a short-term clock offset predicted value is generated in combination with a standard time reference; generating topology correction according to the topology connection relation of the power distribution network; and fusing the short-term clock offset predicted value and the topology correction quantity to generate a dynamic compensation time reference, performing time marking on the acquired electrical quantity measurement data of the power distribution network, and generating synchronous measurement data with a timestamp. According to the technical scheme, when the Beidou signal is lost, the local clock drift predicted value and the adjacent node topology correction amount are fused to generate the dynamic compensation time reference, so that the measurement data synchronization precision and robustness of the power distribution network in a complex environment can be remarkably improved.

The invention relates to the technical field of equipment control, and provides an autonomous tracking anti-interference control method and system.The confidence coefficient of a sensor is determined by combining the historical precision of the sensor and the current signal quality through a sensing recognition module, and the fusion weight is dynamically adjusted based on the confidence coefficient, so that the target tracking state can accurately reflect the actual motion characteristics of a target; the trajectory planning module takes an interference type, an interference degree and a target tracking state as input parameters cooperatively, and combines an extended state space and a reward function containing an anti-interference reward, so that an explored tracking trajectory can actively adapt to an interference scene; meanwhile, the predicted collision probability is compared with a probability threshold value, so that the global updating triggering opportunity is ensured to be accurate, and the collision risk caused by an unreasonable track is effectively avoided; and the control optimization module generates a targeted feed-forward compensation amount according to the interference type and the interference degree, and generates a control instruction after superposing the basic control amount, thereby realizing cooperation of anti-interference compensation and trajectory tracking control.

A wearable device configured to monitor physiological parameters of a wearer can include: a physiological parameter measurement sensor configured to monitor a plurality of physiological parameters; a hardware processor; a display in communication with the processor; and a band configured to secure the physiological parameter measurement sensor on a wrist of the wearer. In some implementations, the hardware processor is configured to: obtain a first plurality of signals from the physiological parameter measurement sensor when the band is secured on the wrist at a first tightness; determine a signal quality responsive to the first plurality of signals; and output an indication on the display to adjust tightness of the band with respect to the wrist from the first tightness to a second tightness based on the determined signal quality.

The invention discloses a multi-modal dynamic fusion and incremental learning fault diagnosis method for deep vertical shaft equipment, which belongs to the technical field of industrial equipment fault diagnosis, and comprises the following four steps of: constructing a pre-training large model to perform feature extraction, and relying on a multi-layer Transformer encoder and a dual loss function, establishing a multi-modal dynamic fusion and incremental learning fault diagnosis model; mining cross-modal universal fault features from vibration, temperature and current multi-modal time sequence data; according to the method, multi-modal features are fused, multi-modal association is constructed, modal weights are dynamically adjusted through a modal gating unit and a time delay compensation attention mechanism to adapt to signal quality changes, and meanwhile time sequence deviation is corrected to achieve accurate association; incremental learning is realized by using a decoupling projection layer, and a lightweight projection module is designed for a newly added fault task to suppress disastrous forgetting; network training is optimized, pre-training loss, incremental learning loss and attention regularization loss are integrated through a multi-objective loss function, and model stability and diagnosis precision are improved. The method has the advantage that the model stability and the diagnosis precision are improved.

The invention provides an inertial navigation attitude resolving method and device based on sensor fusion. The method comprises the following steps: carrying out multi-clock domain synchronous calibration processing on a double-antenna GNSS system, an IMU inertial measurement unit and an antenna servo system in a vehicle-mounted communication-in-motion system; according to the time synchronization reference, carrying out quality evaluation and weight distribution processing on the GNSS satellite signal under the satellite communication in motion antenna pointing constraint; and carrying out multi-sensor fusion resolving processing on the inertial attitude of the vehicle body according to the time synchronization reference and the GNSS observation data weight distribution result. According to the method, millisecond-level time synchronization is realized by establishing a robust extended Kalman filtering model of a multi-dimensional extended state vector, GNSS signal quality is optimized by adopting an adaptive weight distribution strategy of antenna pointing constraint, and attitude fusion precision is enhanced by utilizing high-precision angle feedback of an antenna servo system. The problem that a traditional attitude resolving method in a vehicle-mounted communication-in-motion system is not high in precision is solved.

The invention relates to the field of channel management, in particular to an intelligent channel management method and system based on multi-source data fusion. The invention discloses an intelligent navigation channel management system based on multi-source data fusion. The system comprises a combined ship identification module, a positioning frequency updating module, a collision risk assessment module and an intervention management module. According to the invention, the system can sense multi-dimensional environmental parameters such as illumination intensity, visibility, AIS signal quality, radar echo signal-to-noise ratio and the like in real time by constructing a combined ship identification model containing AIS, radar, vision and infrared, and dynamically adjust the fusion weight of each sensor to compensate for the deficiency of a single sensor; according to the method, the robustness of ship dynamic monitoring is improved, high-precision tracking can be kept in severe weather, the data redundancy contradiction of a traditional multi-sensor system is avoided, and therefore continuous and stable target state input is provided for follow-up risk analysis.

The invention provides a gas ultrasonic transducer fast matching method, device and equipment and a storage medium, original measurement data such as flight time are obtained by deploying an ultrasonic transducer in a gas ultrasonic flowmeter in a gas conveying pipeline, and multi-dimensional data acquisition is carried out in combination with other sensor parameters. Wavelet noise reduction and dynamic time warping processing are carried out on collected signals, signal quality and time sequence consistency are improved, time domain, frequency domain, environment and statistical features are extracted, and multi-dimensional feature vectors are formed. Modeling is carried out through a time sequence feature branch and an environment feature branch, weighted fusion is carried out by adopting an attention mechanism, and the recognition capability of the model on key features is enhanced. A machine learning model is trained based on fusion features, a multi-objective loss function and a data enhancement strategy are adopted, the prediction precision and generalization ability of the model are improved, and a compensation value is output to calibrate the original traffic in real time. According to the method, the accuracy and stability of gas flow measurement in a complex environment are effectively improved, and the method has a good engineering application prospect.

The invention relates to the technical field of wireless communication, in particular to a wireless communication anti-interference enhancement system based on multi-mode signal fusion. Comprising an intelligent signal transceiving module, a signal sensing and preprocessing module, an interference feature coding and classification module, an interference suppression decision module, an interference suppression module, a signal fusion module, an interference suppression performance evaluation module, an interference feature library and an interference suppression strategy library, the interference feature coding and classification module adopts a three-layer progressive architecture of multi-dimensional manifold feature extraction, feature transformation and coding inspired by a group theory, and deep learning classification based on chaotic dynamics, extracts interference features from three dimensions of time domain, frequency domain and nonlinearity, and realizes accurate interference identification; the signal fusion module adopts a multi-level fusion strategy, and dynamically adjusts the fusion weight according to the signal quality; the interference suppression module selects an optimal suppression strategy according to the interference type, and executes frequency domain cancellation, space domain cancellation and time domain cancellation; and performance evaluation feedback forms a closed-loop optimization mechanism.

The invention provides a medical equipment fault detection method and system based on machine learning, and relates to the technical field of fault detection. The method comprises the steps of monitoring motor start-stop division operation cycles, synchronously collecting and processing vibration, frequency difference and voltage signals, calculating characteristic difference to generate a dynamic sequence, extracting a non-convergence trend and phase deviation, fusing voltage and frequency fluctuation characteristics, constructing a threshold rule to judge abnormity, and outputting a light fault early warning signal. According to the invention, through precise monitoring of motor start-stop nodes, period division, collection of vibration, frequency and voltage signals, combination of time alignment and noise filtering, improvement of signal quality, cross-period calculation of characteristic difference, and extraction of non-convergence trend and phase offset, the ability of capturing tiny anomalies is enhanced; according to the method, multi-dimensional indexes such as voltage fluctuation and frequency deviation are fused, a threshold judgment mechanism in a continuous window is constructed, stable identification and early warning of equipment light faults are achieved, and the detection accuracy and response timeliness are improved.

The invention relates to the technical field of fluid measurement, in particular to a multimode signal fusion and self-adaptive processing ultrasonic flowmeter and a measuring method. The multimode signal fusion and self-adaptive processing ultrasonic flowmeter comprises an ultrasonic transducer, a signal generation driving module, a signal receiving and conditioning module, a control module, a signal processing module, a signal quality evaluation module and a sound velocity calculation and compensation module. According to the method, the time difference method and Doppler method signals are fused, the self-adaptive processing strategy is adopted to improve the measurement precision of the low-flow-velocity fluid and the impurity-containing fluid, the measurement precision of the low-flow-velocity fluid can be remarkably improved, the adaptability to complex fluid is enhanced, the overall measurement reliability is improved, the Doppler method precision is optimized, and the intelligent degree is high.

The invention discloses a multi-signal quality monitoring system and method in the numerical control machining process, and belongs to the technical field of numerical control machining. The method comprises the following steps: step 1, synchronously acquiring multi-source signals in a microscopic level, a mesoscopic level and a macroscopic level in a processing process; 2, screening to form an optimal feature subset, and constructing a multi-dimensional feature representation space; 3, constructing a same-scale transverse incidence matrix, a cross-scale longitudinal incidence matrix and an interaction incidence matrix based on a bidirectional attention mechanism, and forming an incidence network structure for describing feature coupling and dependency relationships among signals of different levels; step 4, calculating deviation metric values of various signal features, identifying an abnormal diffusion path in combination with a topological structure of the association network, and analyzing a causal relationship chain between the signals; and step 5, matching the identified abnormal mode with a pre-constructed process knowledge graph, automatically generating a targeted process parameter optimization suggestion, and performing real-time evaluation to realize closed-loop adaptive control for quality abnormity.

The invention discloses a pattern recognition type metal pipeline anticorrosive coating quality detection device and system, belongs to the technical field of sensing monitoring, and aims to solve the problems of low detection precision and difficulty in positioning of damaged points of a metal pipeline anticorrosive coating under the interference of a complex environment. The method comprises the following steps: generating an environmental interference spectrum feature library through full-range frequency band pre-scanning, dynamically establishing a reference noise level, screening candidate frequencies of detection signals, and switching the frequencies as required to guarantee signal quality; synchronously acquiring a potential gradient and an electromagnetic radiation signal, and marking space-time coordinates; performing deep processing on the acquired signals, separating multi-frequency signals, correcting attenuation, and constructing a multi-dimensional feature vector; based on the feature vector, a space-time abnormal mode is extracted to position a damage point, through damage type classification, region area measurement and depth evaluation, the damage severity is quantified, accurate data support is provided for pipeline maintenance, and the accuracy and reliability of metal pipeline anticorrosive coating detection are effectively improved.

The invention discloses an enteritis patient condition severity risk early warning method, a medium and equipment. The method comprises the following steps: acquiring a borborygmus original signal and a clinical multi-dimensional physiological parameter sequence through a sensing device; constructing a borborygmus dynamic characteristic spectrum based on the borborygmus original signal to generate an acoustic biomarker time sequence; inputting the acoustic biomarker time sequence and the clinical multi-dimensional physiological parameter sequence into a multi-modal fusion early warning model to obtain an intestinal inflammation risk index; executing a signal quality self-evaluation process and generating a data quality warning code when the signal quality is abnormal; triggering a multi-node collaborative monitoring mechanism based on the risk index to generate an intestinal state multi-dimensional situation map; establishing an individualized risk baseline and generating a graded early warning instruction; and finally outputting a comprehensive early warning report. According to the method, multi-modal fusion analysis of the borborygmus signal and the clinical parameters is realized, the accuracy and timeliness of illness state early warning are remarkably improved through dynamic risk assessment and signal quality monitoring, and a reliable basis is provided for clinical decision making.

The invention relates to the technical field of data processing and analysis, and discloses a parallel sensor data analysis method and system for cable fault detection, and the method comprises the steps: synchronously collecting cable fault transient traveling wave signals through distributed monitoring terminals, and forming a multi-channel signal data set; and carrying out wavelet packet decomposition and reconstruction, and denoising to obtain a denoised data set. The method comprises the following steps: firstly, generating node time sequence data with a timestamp, constructing an undirected weighted graph model, calculating a plurality of candidate fault positions through a double-end positioning formula on the basis of corrected time data, and finally, allocating dynamic weights to effective candidate positions according to signal quality and confidence, and generating a fusion positioning result by adopting a weighted fusion algorithm. And physical correction is carried out based on cable laying constraints, and accurate fault position coordinates are output. According to the method, through multi-level data processing and fusion, the fault positioning precision and the system robustness under complex working conditions are improved.

The invention provides a dynamic wavelength allocation method based on DCI wavelength division II type equipment. The method comprises the following steps: acquiring an occupation state and a transmission quality index of each wavelength channel of a current network; establishing a wavelength channel quality evaluation model, and scoring each wavelength channel according to the transmission quality index of each wavelength channel of the network; according to the bandwidth requirement, the priority and the QoS requirement of the service request, generating a traffic characteristic matrix containing the delay sensitivity and the bandwidth requirement through a traffic prediction algorithm; based on a network state and the traffic characteristic matrix, through resource availability calculation and path quality evaluation, forming an allocable wavelength resource pool and a quality score thereof; for the allocatable wavelength resource pool, generating an optimal wavelength allocation scheme by adopting an improved heuristic algorithm and comprehensively considering the resource utilization rate, the signal quality and the service priority; and according to the performance feedback of the optimal wavelength allocation result and the network state change, continuous optimization of wavelength allocation is realized through a self-adaptive adjustment mechanism. The method can effectively deal with the dynamic flow change in the DCI network, improve the resource utilization rate, ensure the signal quality and meet the QoS requirement of the service, thereby providing an efficient and reliable wavelength allocation solution for the data center interconnection network.

The invention relates to the technical field of vehicle-mounted communication, and provides a 5G network optimization method and system based on an intelligent dynamic multi-band adaptive switching system, and the method comprises the steps: obtaining network signal quality data, environment perception data and a communication task demand set; dynamic obstacle features are extracted based on environmental perception data, and signal occlusion prediction is output through a double-branch mixed deep learning model; screening qualified candidate links to construct a candidate set, generating a dynamic weight by using a deep Q network, calculating a comprehensive score of the candidate links, determining a main communication link under a hard constraint condition, and generating a communication strategy instruction; and according to the instruction type of the communication strategy instruction, performing dynamic fragmentation and multi-path distribution on the data stream, performing verification, recombination and intelligent delivery by a receiving end, and collecting a feedback data packet for network closed-loop feedback. According to the method, multi-mode sensing and signal shielding prediction are fused, and a self-adaptive switching and cooperative transmission mechanism of multi-band communication is constructed.

The invention relates to the technical field of electroencephalogram signal processing, and provides a brain-computer AI glasses electroencephalogram signal multi-dimensional quality dynamic detection method, which comprises the following steps: preprocessing collected electroencephalogram signals; performing task frequency band component analysis on the channel signals subjected to the matrix rank analysis, and calculating a power proportion of a target frequency band; performing maximum gradient analysis on the channel signal, and calculating the maximum gradient value of the channel signal; performing root-mean-square analysis on the channel signal, and calculating a root-mean-square value of the channel signal; performing fluctuation amplitude analysis on the channel signal, and calculating a peak-to-peak value of the channel signal; performing brain hemisphere symmetry analysis on the left and right homologous channel signals, calculating the correlation and mean square error of the channel signals, and setting a dynamic threshold according to the frequency band of the channel signals; obtaining an electrode impedance value, and mapping the electrode impedance value into an impedance score through a preset exponential attenuation function; the final quality score of the channel is obtained, and dynamic detection and real-time score updating of the electroencephalogram signal quality are achieved.

The invention discloses a high-precision point cloud map construction method based on pose map optimization, and the method comprises the steps: preliminarily detecting the observation reliability of each frame of GNSS, and screening credible data; a priori factor is constructed for initial position GNSS signal quality, and map coordinate anchoring is completed; estimating a relative pose between adjacent frames, and constructing an inter-frame constraint edge; scanning context global descriptors are introduced to realize efficient screening and accurate matching of loopback candidate frames; further evaluating the quality of the GNSS by using a loopback detection result and the pose of the laser odometer, setting a GNSS factor weight, and constructing a GNSS factor edge; constructing a complete pose graph model, performing overall optimization on all pose nodes by using a nonlinear optimization method, and correcting accumulative errors; and converting the laser point cloud into a global coordinate system, and splicing to generate a global consistent high-precision point cloud map. According to the invention, the global positioning capability of the high-quality GNSS data and the local geometric precision of the laser point cloud can be effectively fused, and the high-precision point cloud map can be continuously and stably constructed.

The invention relates to the technical field of communication signal analysis, in particular to a communication signal analysis method and device, equipment and a storage medium. The method comprises the following steps: carrying out full-band signal continuous acquisition on a detection area, and extracting an original signal flow; performing potential electromagnetic interference detection and electromagnetic interference distribution marking on the original signal flow to obtain an interference source distribution diagram; dynamic interference suppression is carried out based on the interference source distribution map, real-time suppression parameter adjustment is carried out, and a calibration optimization signal flow is constructed; performing real-time intensity calculation on the calibration optimization signal flow, and constructing a regional signal intensity distribution model; and performing signal quality evaluation and real-time decision early warning based on the regional signal intensity distribution model. According to the method, the communication signal quality and stability of the area can be accurately and rapidly evaluated.

The invention discloses a satellite terminal adaptive power adjusting system. The system comprises an attitude sensor module used for collecting three-dimensional attitude data of a terminal; the antenna pattern database is used for storing theoretical gain values of the panel omnidirectional antenna in different attitudes; the gain prediction engine is used for calculating theoretical gain according to the current attitude data and the directional diagram database; the signal quality detection module is used for collecting the intensity, the signal-to-noise ratio and the bit error rate of the received signal in real time; the power control algorithm module fuses the gain prediction value and the signal quality feedback and is used for generating a dynamic transmitting power instruction; the radio frequency power amplifier is used for executing the power adjusting instruction and outputting an adjusted transmitting signal; the invention further discloses a satellite terminal self-adaptive power adjusting method. According to the invention, the problem of balance between satellite terminal communication stability and power consumption efficiency in a complex environment is solved.

The invention discloses an electrocardiosignal preprocessing system and method based on filtering and deep learning, and relates to the field of electrocardiosignal data processing. The multi-stage adaptive filtering module comprises a baseline drift elimination unit, a power frequency interference suppression unit and a myoelectricity noise removal unit, and all the units are connected in sequence to form pipelined parallel processing; the deep learning fusion module is used for performing deep feature extraction and noise classification on the output signal and feeding back a classification result to the multi-stage adaptive filtering module; the signal quality evaluation module carries out quality evaluation on the electrocardiosignals subjected to multi-stage adaptive filtering and deep learning fusion and judges whether the signal quality is qualified or not; a feature enhancement and standardization module; the technical effects of improving the self-adaptability and robustness, improving the calculation efficiency of heart disease classification, the signal fidelity and the diagnosis reliability, and enhancing the signal quality evaluability and the self-adaptive ability are achieved.

The invention discloses a self-adaptive equipment dormancy method based on a Wi-Fi signal state, and the method comprises the following steps: 1, based on the continuous monitoring of a physical layer index of associated equipment, sending a suggested wake-up instruction to the corresponding associated equipment when it is predicted that the signal quality is rapidly reduced but does not fall off a switching threshold; step 2, according to a received suggested wake-up instruction or a signal degradation trend detected by the corresponding associated device, specifically, switching from a deep sleep state to a standby wake-up state, shortening a sleep period and pre-loading authentication information; 3, the wireless access point dynamically adjusts the sending opportunity of the suggested wake-up instruction according to the service delay requirement of the associated equipment; 4, the associated device dynamically elects a main link and a slave link based on a multi-attribute decision algorithm, and the main link is responsible for receiving a wireless access point instruction and uniformly scheduling dormancy and awakening of the slave link; and 5, generating a prediction result of future data traffic, and controlling the on-demand wake-up time of the slave link by the main link.

The invention discloses a multi-scale time-frequency network sleep stage classification method based on single-channel electroencephalogram, and belongs to the technical field of medical signal processing. Aiming at the problems of capturing multi-scale time-frequency characteristics, processing variability among subjects and modeling long-range time dependence of a sleep staging method, the method comprises the following steps: firstly, acquiring electroencephalogram signal data, and performing time sequence segmentation, sleep stage category label determination and standardized preprocessing by taking a single-channel electroencephalogram signal as an analysis object; performing data set division by adopting nested cross validation, and constructing a multi-scale time-frequency network model; the model comprises a feature extraction module and a sequence learning module, wherein the feature extraction module comprises a time domain branch and a frequency domain branch; a subject adaptive feature calibration module is proposed to dynamically compensate the influence brought by individual difference and signal quality fluctuation; respectively training a feature extraction module and a sequence learning module by adopting a component type training strategy; and inputting to-be-classified electroencephalogram signal data into the trained model, and outputting a corresponding sleep stage classification result.

The invention provides a low-orbit satellite communication terminal network performance dynamic tuning method and system, and relates to the technical field of low-orbit satellite communication. Comprehensive data such as relative movement of a satellite and a terminal and signal transmission loss are acquired; preprocessing the integrated data, inputting the preprocessed integrated data into a time sequence prediction model containing LSTM and Transform, generating a signal quality and intensity index sequence and a link stability level in a future preset time window, and carrying out dynamic optimization based on the generated indexes, including adjusting a terminal modulation mode according to the signal quality; combining the link stability and the signal strength to aggregate the link, and optimizing the switching opportunity and path; allocating resources according to the signal strength and the service characteristics; and optimizing the energy consumption according to the relative motion and the business characteristics. Through accurate prediction and multi-dimensional adjustment and optimization, the communication efficiency and reliability of the low-orbit satellite communication terminal network are improved, energy consumption and delay are reduced, and good communication quality is guaranteed.

The invention belongs to the technical field of wireless information transmission, and provides a single link roaming switching device and method based on double wifi chip STA equipment, the technical scheme is that a communication link is established between a first network card and an AP, and a second network card is used for monitoring link state data of the first network card; the controller is configured to evaluate the channel quality of a current channel according to the link state data of the first network card acquired by the second network card, predict the time when the change rate is changed to be less than a signal quality threshold according to the change rate of the channel quality, and perform roaming when the predicted time is less than a time threshold; when it is detected that roaming is needed, the second network card is controlled to start to send the same content as the first network card, candidate APs in the network are scanned at the same time, a target AP is selected in a self-adaptive mode according to the channel quality, obtained through calculation, of the candidate APs, and the first network card is controlled to select the target AP to establish a link. The effective utilization of bandwidth is improved, and the probability of air interface data conflict is reduced.

The invention discloses a distribution network-oriented traveling wave fault location centralized analysis system, which relates to the technical field of power system fault location and comprises a centralized master station and a plurality of monitoring terminals. The monitoring terminals are deployed at different node positions of a power distribution network, and synchronously collect fault traveling wave signals and upload the fault traveling wave signals to the centralized master station. The centralized master station comprises a topology modeling module, a data preprocessing module, a wave head identification module, a candidate branch screening module, a matching evaluation module and an accurate positioning module. According to the invention, through establishing a full-process quality tracking and adaptive weighting mechanism from preprocessing to accurate positioning, a system can adjust a positioning strategy according to a distribution network topology structure and a signal state based on a two-dimensional comprehensive evaluation system of topology sensitivity and signal quality; high fault positioning accuracy and reliability are kept in a complex environment of the distribution network, and the robustness and practicability of the system are improved.

The invention discloses a mine roadway deformation data real-time analysis system based on edge calculation, which relates to the technical field of intelligent sensing and data analysis fusion, and comprises a data acquisition module for acquiring roadway deformation initial data through a sensor, simulating a deformation field evolution process by adopting a finite element analysis method, and analyzing the deformation field evolution process; the feature extraction module is used for extracting key stress distribution parameters from the non-uniform field distribution features, if the parameters exceed a preset threshold value, the key stress distribution parameters are marked as a risk concentration area, a monitoring position candidate set is determined, and the signal quality evaluation module is used for evaluating the monitoring position candidate set according to the monitoring position candidate set. Acquiring a signal-to-noise ratio and an integrity data quality index of a sensor signal, and performing priority grouping on the monitoring points through a clustering algorithm to obtain a dynamic priority evaluation result; according to the mine roadway deformation data real-time analysis system based on edge calculation, intelligence, real-time performance and high reliability of mine roadway deformation monitoring are achieved.

The invention relates to the technical field of optical communication, and discloses an optical fiber loss evaluation method and system. The method comprises the steps that an optical fiber sensor collects real-time optical signal data and generates a dispersion velocity data sequence; extracting a fluctuation feature vector by adopting an adaptive filtering algorithm, and determining a dispersion velocity change trend; inputting the change trend and the transmission distance into a neural network model, predicting a loss fluctuation amplitude and generating a prediction loss sequence; calculating a coupling effect correlation coefficient, judging abnormal coupling and outputting an evaluation result; adjusting signal compensation parameters according to an evaluation result, optimizing nonlinear influence and determining a compensated dispersion velocity value; calculating a signal attenuation factor according to the compensated dispersion velocity value and the transmission distance, and generating an optimized transmission scheme; and updating optical communication network parameters according to the scheme, outputting stable signal quality and generating an optical fiber loss evaluation result. According to the invention, the problem of inaccurate evaluation caused by nonlinear fluctuation and signal coupling is solved, and the stability of optical fiber transmission and the signal quality are improved.

The invention discloses a beam adjustment method and device for multiple unmanned aerial vehicles, and electronic equipment. The method comprises the following steps: acquiring a beam pre-allocation scheme of an unmanned aerial vehicle and position information in a flight process; the state vector of the unmanned aerial vehicle is determined according to the position information, the state vector is processed through a target encoder in the strategy network, an association probability matrix and a beam parameter vector are obtained, the target encoder performs semantic enhancement on the state vector through a multi-head attention mechanism, and the correlation probability matrix is obtained; the association probability matrix is used for reflecting a service relationship between the unmanned aerial vehicle and a base station beam, and the beam parameter vector is used for reflecting parameter configuration of the base station beam; and adjusting a pre-allocation beam scheme according to the association probability matrix and the beam parameter vector. According to the invention, technical problems of discontinuous communication coverage, unstable signal quality and low resource scheduling efficiency existing in a static wave beam scheme in related technologies under high-speed movement and complex scenes of multiple unmanned aerial vehicles are solved.