439 results about "Adaptive filtering algorithm" patented technology

The invention discloses a data acquisition and processing method based on an SF6 pressure gauge of a transformer substation, and relates to the technical field of data processing, and the method comprises the steps: collecting the environment data of the transformer substation in real time through an SF6 pressure sensor, a temperature sensor and a humidity sensor; inputting the substation environment data into a multi-sensor fusion algorithm, performing temperature compensation and humidity correction, and generating a corrected multi-dimensional data set; inputting the extracted key features into an artificial intelligence model for fault prediction, and outputting a fault early warning signal; and according to the fault early warning signal, an alarm mechanism is triggered and operation and maintenance personnel are notified, and an actual fault processing result is fed back to the central processing platform. Through a multi-sensor fusion technology and a self-adaptive filtering algorithm, the problem that SF6 pressure data in the prior art is easily influenced by environment temperature and humidity is effectively solved, and the pressure monitoring precision is remarkably improved.

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 provides a road construction project carbon emission ratio toughness analysis method, and belongs to the technical field of carbon emission, machine learning and big data analysis and mining, and the method comprises the steps: obtaining a historical change trend of a carbon emission ratio from historical data of a road construction project, and constructing a dynamic change model of the carbon emission ratio in combination with real-time monitoring data; determining the influence degree and the action mechanism of the interference factors on the carbon emission ratio dynamic change model by analyzing the interaction relationship between the carbon emission ratio and the interference factors; key fluctuation characteristics are extracted from the corrected change trend of the carbon emission ratio, and whether fluctuation of the carbon emission ratio exceeds an acceptable range or not is judged in combination with a preset threshold value; if the fluctuation of the carbon emission ratio exceeds a preset threshold value, performing noise reduction processing on the data by adopting an adaptive filtering algorithm to generate noise-reduced carbon emission ratio data; and training the segmented analysis result by adopting a random forest model in a machine learning algorithm to generate a prediction model of the carbon emission ratio.

The invention relates to a building module data interface intelligent monitoring system based on the Internet of Things and a deployment method, and belongs to the technical field of building information monitoring. The system is composed of a distributed sensing unit, an edge computing gateway, a cloud platform and a visual terminal, a self-adaptive filtering algorithm and an abnormal mode recognition model are built in the edge computing gateway, the cloud platform adopts a time sequence database to construct a multi-dimensional data warehouse, and structural health degree evaluation is carried out by fusing an LSTM neural network and a random forest algorithm. The deployment method comprises the steps of optimizing a sensor distribution strategy based on a BIM model, establishing a wireless Mesh ad hoc network communication architecture, and configuring a grading early warning mechanism and a fault tracing function. The innovation point is that a dynamic threshold adjustment algorithm and an interface performance degradation prediction model are provided, and real-time monitoring of the connection state of the building module and life prediction are realized. The system has the advantages of flexible deployment, high detection precision and low maintenance cost, and the intelligent level of building structure safety monitoring is effectively improved.

The invention provides a sewage treatment plant drainage water quality evaluation method and system, and the method comprises the steps: collecting and processing water quality parameters of a plurality of point locations of a sewage treatment plant, recognizing abnormal data points through a deep learning anomaly detection model, and carrying out the dynamic correction or elimination of the abnormal data points through employing a particle swarm optimization-based adaptive filtering algorithm, thereby achieving the evaluation of the drainage water quality of the sewage treatment plant. Obtaining normal water quality data; constructing a water quality prediction model by using an integrated learning method, and predicting a water quality change trend; generating a target water quality improvement measure combination by using a first water quality improvement strategy recommendation mechanism based on a multi-objective optimization algorithm; and establishing a closed-loop feedback mechanism, evaluating the application effect of the target water quality improvement measure combination by using a reinforcement learning algorithm, adjusting key parameters of the water quality prediction model and the multi-target optimization algorithm, optimizing the first water quality improvement strategy recommendation mechanism, and generating an optimal target water quality improvement measure combination. The system realizes comprehensive, accurate and real-time monitoring and management of the drainage water quality of the sewage treatment plant, and improves the water quality improvement effect.

The invention provides a power supply equipment temperature characteristic detection method and system. The method comprises the following steps: acquiring temperature data sets of power supply equipment under different working conditions; performing partial derivative calculation on the mathematical model, quantifying the contribution degree of each working condition to the temperature characteristic, and determining a main influence factor and a secondary influence factor; a time sequence analysis method is introduced, the temperature data set is analyzed, the dynamic change trend of the working conditions is captured, and nonlinear relation characteristics between the working conditions and temperature characteristics are identified; a self-adaptive filtering algorithm is adopted, changes of working conditions are tracked in real time, and parameters and structures in the mathematical model are dynamically adjusted, so that the mathematical model can adapt to the dynamic changes of the working conditions; and training and optimizing the adjusted mathematical model by using a support vector machine algorithm, thereby improving the prediction precision and generalization ability of the mathematical model.

The invention discloses an optical fiber distributed sound wave sensing device based on double chirped pulses, which relates to the technical field of distributed optical fiber sensing and comprises a laser module (1), a chirped pulse modulation module (2), a sensing optical fiber module (3) and a Rayleigh scattering signal separation and receiving module (4), laser generated by the laser module passes through the chirped pulse modulation module to obtain double chirped pulse signals with different frequency bands, excites Rayleigh scattering signals in the sensing optical fiber module and inputs the Rayleigh scattering signals to the Rayleigh scattering signal separation and receiving module for signal separation and demodulation. The sensing optical fiber is inquired through double chirped pulses of different frequency bands, two Rayleigh scattering signals are excited to achieve a wide range and high sensitivity, the advantages of the two signals are combined through an adaptive filtering algorithm, the dynamic range of distributed sound wave detection is improved by one order of magnitude, and the detection accuracy is improved. The acquisition of large chirp bandwidth Rayleigh scattering signals is realized at a low sampling rate, and a distributed sound wave sensing technology with a large dynamic range is realized.

The invention discloses a gas cylinder storage leakage point positioning detection method and system, and the method comprises the steps: building a gas cylinder leakage model under the conditions of no wind and wind, simulating a leakage process through a gas turbulence diffusion model, and introducing a sensor array to detect a gas leakage point; an adaptive filtering algorithm AF is introduced to carry out noise removal on data detected by the sensor array; extracting time difference information hidden in a gas sensor signal by using continuous wavelet transform (CWT) and a modulus maximum value method, judging the position of an optimal leakage point, and obtaining positioning information of a certain time point; a window function is introduced into a Kalman filtering algorithm, multiple sets of positioning information at different time points are obtained through processing of the improved Kalman filtering algorithm, interference of measurement invalid signals not in the range of the window function is filtered out, and the optimal positioning point is estimated. Compared with the prior art, the sensor array is introduced to realize leakage detection and positioning, a window function of a related time point is introduced into Kalman filtering, and the detection accuracy is effectively improved.

The invention discloses a combined navigation method and system of an unmanned surface vehicle. The method comprises the following steps: forming a GNSS (Global Navigation Satellite System) / INS (Inertial Navigation System) integrated navigation system by using a GNSS (Global Navigation Satellite System) and a strapdown inertial navigation system (INS), and establishing a mathematical model of a GNSS / INS integrated navigation system filter; the mathematical model comprises a state equation and a measurement equation; establishing a mathematical model of the influence of the GNSS auxiliary information on the GNSS positioning precision so as to preliminarily adjust the measurement noise in the measurement equation; through an improved Sage-Husa adaptive filtering algorithm, adaptive estimation is carried out on the preliminarily adjusted measurement noise, and a measurement noise estimation final value is obtained; and according to the motion characteristics of the unmanned surface vehicle, adding motion constraint conditions to the measurement equation. The GNSS auxiliary information mathematical model based on multivariate function fitting is established, the influence of the external environment on the positioning precision is objectively reflected, the measurement noise in the filter is dynamically adjusted, and the positioning precision of the integrated navigation system under the interference condition is remarkably improved.

The invention provides an intelligent conference memo generation method based on a robot, and the method comprises the steps: collecting a multi-channel voice signal through a microphone array, and enhancing the voice of a target speaker through a beam forming technology; background noise is separated by adopting a self-adaptive filtering algorithm, and the voice signal quality is improved; speech features are extracted, language model parameters are adjusted, and a transliteration text is generated; analyzing the text structure, and extracting conference themes, participants and decision contents to form structured information; calculating semantic similarity among the knowledge graph nodes, and if the semantic similarity is higher than a preset threshold value, associating historical records to generate extension information; constructing a structured memorandum based on the extended information, organizing a conference theme, participants, decision contents and associated historical records, and generating an initial memorandum; and monitoring a memorandum editing operation, updating a knowledge graph node relationship, and generating a final memorandum document. According to the method, the accuracy, integrity and availability of conference records are effectively improved, and the conference efficiency is remarkably improved.

The invention relates to the technical field of adaptive filters, and discloses a wide linear adaptive filter, which comprises the following steps of: performing tensor integral solution on a system weight vector at the moment of the wide linear adaptive filter to obtain two adaptive weight vectors; constructing an input matrix based on the sampling value of the input signal, and calculating an estimation signal of the moment based on the product of the input matrix of the moment and the two adaptive weight vectors; and calculating the difference value between the expected signal and the estimation signal at the moment, obtaining an estimation error signal, and updating the gain vector at the obtaining moment and the estimation error signal by using a minimum error mode criterion, updating the two adaptive weight vectors at the obtaining moment, and reconstructing the system weight vector at the moment of the wide linear adaptive filter. According to the invention, the recursive adaptive filtering algorithm resistant to impulse noise interference is realized with low calculation complexity by using weight decomposition and selection of a robust cost function.

The invention discloses a tunnel advanced geological forecast monitoring method and related equipment, and relates to the technical field of intelligent monitoring, and the method comprises the steps: obtaining the time domain response of a transient electromagnetic signal; processing the time domain response of the transient electromagnetic signal by using an adaptive filtering algorithm to obtain processing data; generating a three-dimensional geologic model according to the processing data by using a three-dimensional imaging algorithm; and predicting the geological condition based on an abnormal density function, a pattern recognition algorithm and the three-dimensional geological model.

The invention discloses a state monitoring system for a railway electric switch machine. The state monitoring system comprises a multi-mode sensor module, a data preprocessing module, an intelligent analysis and decision module and a visualization and interaction module. The multi-mode sensor module is distributed at a key part of the switch machine and is used for acquiring multi-dimensional data such as the rotating speed of a power transmission shaft, displacement and vibration of an actuating rod, an expression notch image, temperature and humidity, air pressure and the like; the data preprocessing module performs noise reduction through a Kalman adaptive filtering algorithm and fuses asynchronous data by using a space-time alignment technology; the intelligent analysis and decision-making module constructs a hierarchical index system, determines a weight in combination with an analytic hierarchy process and an entropy weight method, dynamically evaluates a health level through a fuzzy grey clustering algorithm, predicts and represents a gap size trend by adopting an ARIMA model, performs monthly early warning on jamming faults by applying a random forest model, and optimizes and maintains resource configuration according to historical data; the visualization and interaction module develops a Web mobile terminal interface and supports multi-level health state display, real-time alarm and maintenance work order generation.

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 BIM (Building Information Modeling)-based large-span post-tensioned bonded prestressed beam construction method. The BIM-based large-span post-tensioned bonded prestressed beam construction method comprises the following steps: collecting a vibration signal of a working rib and a matt rib reference signal of a matt rib; inputting the dummy rib reference signal into an adaptive filtering algorithm to which phase preserving constraint is applied, and performing noise cancellation processing on the working rib vibration signal to generate a purified pressure wave signal; performing multi-scale decomposition on the purified signal, extracting propagation time delay of the direct wave based on coherence analysis among components, and generating a phase-time delay characteristic matrix; and based on pre-configured geometric parameters of the BIM three-dimensional model, a real-time stress distribution field is calculated through inversion according to the phase-time delay characteristic matrix, and the real-time stress distribution field is synchronously updated to the BIM three-dimensional model. According to the method, the phase distortion problem under noise interference is solved through a physical reference channel and phase preserving filtering, the accuracy of time delay extraction is improved through a multi-path separation technology, and high-precision and high-robustness real-time monitoring of the prestress is achieved.

The invention provides an audio and video synchronization method and device, computer equipment and a storage medium so as to improve user experience in a complex network environment. Firstly, audio and video data packets are received and cached in a playing buffer area; calculating data packet receiving time through an adaptive filtering algorithm to obtain basic delay evaluation data; and the particle filter identifies network fluctuation and delay jitter data to obtain real-time network state evaluation data. Based on these data, the system determines overall delay assessment data for historical periods of time to more accurately predict and adjust synchronization; the system dynamically adjusts the size of a playing buffer area and the cache amount of data packets, and ensures that enough cache data exist in audio and video synchronous processing. And finally, performing synchronization processing on the data packets in the playing buffer area to obtain and play target audio and video data. Generally speaking, the scheme effectively deals with network changes, reduces audio and video asynchronization and improves user experience by dynamically adjusting the size of the buffer area and the buffer amount.

The invention provides a power factor correction method and system for AC-DC conversion of a server power supply, and relates to the technical field of server power supplies, and the method comprises the steps: collecting an input AC signal and a DC bus output signal, calculating a real-time power factor value, constructing a compensation function based on the power factor value, and calculating a deviation value; and generating a compensation parameter matrix through recursive calculation, processing a direct current output signal by adopting an adaptive filtering algorithm, fusing the direct current output signal with the compensation parameter matrix, calculating to obtain a current adjusting signal and a duty ratio adjusting signal, and accurately adjusting a power factor.

The invention discloses a patient improvement effect analysis method for controlling spinal cord electrical stimulation through an implantable brain-computer interface, and relates to the technical field of medical rehabilitation, and the method comprises the steps: multi-dimensional collaborative data collection: implanting electrodes in a target brain region and below a spinal cord injury segment, installing a detection element at an exoskeleton key part, and carrying out multi-dimensional collaborative data collection; a sensor is attached to a lower limb preset muscle group, electroencephalogram signals, SCS stimulation parameters, EXS motion data and neuromuscular response data are synchronously collected, and time correlation marks are embedded; according to the method, the reliability of motion intention decoding is remarkably improved by adopting a mode of combining multi-source signal preprocessing and a multi-mode intention recognition model, and in the signal preprocessing stage, the self-adaptive filtering algorithm combining Kalman filtering and wavelet threshold denoising is applied, so that the motion intention decoding efficiency is improved. SCS electrical stimulation interference, EXS motor noise and physiological noise in the electroencephalogram signals are effectively removed.

The invention discloses a signal anti-interference method for a satellite remote sensing application system, and the method comprises the steps: receiving an electromagnetic signal from a remote sensing satellite through a satellite antenna, and carrying out the preprocessing; interference detection is carried out on the preprocessed signals, and when energy of partial scales is abnormally high, it is judged that interference exists in the digital signals; combining features extracted in interference detection into a feature vector, inputting the feature vector into a classification model constructed based on a convolutional neural network for interference classification, outputting the probability of each interference type, and selecting the type with the maximum probability as a judgment result; according to the output judgment result, interference signals existing in the digital signals are suppressed by adopting a corresponding self-adaptive filtering algorithm, and purified signals are obtained; performing inverse transformation on the purified signal by using a mother wavelet function, and reconstructing the signal; and storing the reconstructed signal in a digital file form, and transmitting the reconstructed signal to a ground station or a data processing center for subsequent analysis and application.

The invention discloses an unmanned aerial vehicle inertial navigation model parameter identification method based on robust adaptive filtering. The method comprises the following steps: 1, establishing an unmanned aerial vehicle motion model and a maneuvering action library; 2, simulating and generating an ideal flight path of the unmanned aerial vehicle; 3, simulating error characteristics of the inertial navigation assembly to generate a gyroscope, and adding inertial navigation assembly output; 4, setting an initial estimation error value of the navigation physical quantity of the inertial navigation system; 5, adding the set navigation error into the reference trajectory to obtain actual navigation data; step 6, setting an initial parameter of a robust adaptive filtering algorithm; and the like. According to the method, identification of the error state quantity of the inertial navigation model of the unmanned aerial vehicle is realized, a software platform supporting modeling simulation and parameter identification of the inertial navigation model of the unmanned aerial vehicle is also established, and inertial navigation model parameter identification with good identification precision, real-time performance and reliability is realized; therefore, the inertial navigation model capable of supporting high-fidelity flight autonomous navigation simulation of the unmanned aerial vehicle is established.

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.

An AI-driven emotion recognition system for learning adaptation in educational institutions, comprehensive: • an emotion recognition unit that tracks students' facial expressions in real time and categorizes the resulting expressions into a specific emotional class from a given set using a Convolutional Neural Network (CNN) trained on a large emotion dataset; • a real-time processing unit that extracts facial features and dynamic emotional characteristics of students through adaptive filter algorithms; • an LMS integration unit, adapted to combine discovered emotional knowledge with online and offline learning environments for customizable learning adjustments; • a personalization engine programmed to intelligently adapt training content, response approaches, and training materials according to the identified emotional states, so that each learner receives individually tailored and more intensive learning experiences; • a feedback loop that sends educators real-time alerts and recommended videos to address emotional feedback or lesson gaps, allowing teachers to tackle negative signals in the moment (e.g., frustrated face, etc.); • AI data storage and adaptive learning model unit that stores anonymized data for emotion recognition, updates the AI ​​model and improves it through learning over time, and ensures that the use of the information is in compliance with data protection regulations; • A user interface module specifically designed for educators, providing a visual overview of students' emotional patterns and real-time analysis of their emotions and engagement as a basis for important decisions that lead to improvements in teaching processes.

The invention discloses a control system for a cooling fan, and relates to the technical field of cooling fan control systems. The temperature acquisition module is used for monitoring the temperature of a key heating part in real time by adopting a high-precision temperature sensor and converting an analog signal into a digital signal through a data acquisition card; the data processing module is used for receiving the data from the temperature acquisition module and analyzing the data by using an embedded processor; a self-adaptive filtering algorithm is built in the module so as to reduce the influence of environmental noise on temperature reading; and the core of the control algorithm module is a PID (Proportion Integration Differentiation) controller, and a hybrid control strategy is formed by combining fuzzy logic control. Through accurate temperature monitoring and an intelligent control algorithm, the system can quickly respond to temperature change, automatically adjust the rotating speed of the fan, realize efficient heat dissipation and prolong the service life of equipment; the system adopts a variable-frequency speed regulation technology, the operation of the fan is adjusted according to actual requirements, unnecessary energy consumption is avoided, and the energy-saving effect is achieved.

The invention provides a permanent magnet frequency conversion double-sine modulation swimming pool pump energy-saving control method, and relates to the technical field of motor driving and control, and the method comprises the steps: building a double-sine carrier modulation model of a permanent magnet synchronous motor, and calculating a dynamic harmonic suppression factor and a double-sine modulation depth in real time; feedforward compensation is carried out on the inverter current based on the DHSIF, and a q-axis current reference value is generated by adopting double-closed-loop vector control; dynamically adjusting a carrier frequency range according to the DSMD, generating double sine carriers with a phase difference of pi / 3, and superposing the double sine carriers into a composite modulated wave; the modulation wave amplitude is corrected through a DHSIF, and fifth and seventh harmonics are eliminated in combination with an adaptive filtering algorithm; a carrier wave ratio is dynamically optimized to generate a PWM signal, and a transient compensation mechanism is triggered to correct a voltage instruction when a load suddenly changes; and an energy efficiency optimization database is constructed to call historical optimal parameters, and nonlinear mapping of DHSIF and motor characteristics is established through parameter self-tuning. According to the invention, cooperative control is realized through dynamic harmonic suppression, modulation depth optimization and intelligent thermal protection.

The invention discloses a tear total IGE detection method and system based on multi-modal data fusion. The method comprises the following steps: acquiring ocular surface images, physiological parameters and environmental allergen data, and acquiring historical medical data from a hospital information system; preprocessing and feature extraction are performed, patient symptom description is analyzed in combination with a natural language processing technology, and related image block features are recognized; adjusting a space-time attention weight based on frequency domain analysis and transfer learning, inputting a lightweight Transform model, and predicting a tear total IgE concentration and an allergy risk score; whether a trace tear sampling program is started or not is judged, and the actually measured concentration of total tear IgE is detected through a disposable micro-fluidic chip; and integrating all information by using a Bayesian adaptive filtering algorithm to determine the total tear IgE level. By implementing the method provided by the invention, the sampling amount is extremely small, the detection time is short, the environmental interference can be dynamically corrected, and the absolute concentration value is output, so that the dual requirements of clinical precise diagnosis and treatment and home monitoring are met.

The invention relates to the technical field of digital signal generation, in particular to a broadband sine wave digital signal generation method and system, and the method comprises the steps: obtaining an initial broadband sine wave digital signal; adopting a signal decomposition algorithm to obtain each modal component and residual component of the initial broadband sine wave digital signal; determining an interference influence factor of each modal component; determining a period difference degree and a superposition influence coefficient of each modal component; determining a relative difference factor and a relative influence coefficient of each modal component; determining a step parameter of each modal component; and in combination with an adaptive filtering algorithm, reconstructing the filtered modal component and residual component to obtain a de-noised broadband sine wave digital signal. According to the invention, the step length parameter of the adaptive filtering algorithm can be adjusted, the denoising effect of the adaptive filtering algorithm is improved, and then the high-quality broadband sine wave digital signal is generated.

The invention relates to the technical field of playing frequency response equalization, and discloses a playing frequency response adaptive equalization method and device, and the method comprises the steps: obtaining an original playing signal of a loudspeaker and an audio signal collected by a microphone; designing a room transfer function between the original playing signal and the audio signal by using a preset adaptive filtering algorithm; estimating a frequency response compensation curve according to the room transfer function; and based on the frequency response compensation curve, designing a corresponding IIR filter to carry out frequency response compensation on the original playing signal. The method can achieve the effects of improving the measurement precision of the frequency response, being simple in measurement method, enhancing the robustness, and remarkably improving the frequency response performance of the loudspeaker system.

The invention discloses an adaptive filtering response method and system of a VHF frequency band, relates to the field of VHF frequency band signal processing, effectively solves the problems of signal interference and multipath effect in VHF frequency band communication through the adaptive filtering response method, and combines a delay detection algorithm and an adaptive filtering algorithm through time domain and frequency domain analysis, so as to realize the adaptive filtering response of the VHF frequency band. Accurate separation of the reflection path signal and the main path signal is realized. Furthermore, interference of reflection path signals is eliminated through a phase offset mechanism, and the transmission quality of the signals is remarkably improved.

The invention relates to the technical field of sensing data processing, and discloses a sensing data chip method based on multi-source information fusion and dynamic parameter adjustment. The method comprises the following steps: collecting original data streams of a plurality of heterogeneous sensors, and generating synchronized sensing data through timestamp alignment and format standardization processing; extracting an environment noise component, and dynamically suppressing noise by using an adaptive filtering algorithm to obtain de-noised sensing data; performing multi-dimensional feature decomposition on the de-noised data, calculating time domain, frequency domain and space domain feature vectors, and generating a multi-dimensional feature set; dynamically adjusting a feature weight distribution strategy in combination with a current system load state and a multi-dimensional feature set historical distribution rule, and generating an optimized fusion weight matrix; and carrying out weighted aggregation on the multi-dimensional feature set and the optimized fusion weight matrix, and outputting a multi-source fusion feature vector. According to the method, the problems of data synchronization, noise suppression and feature fusion suitability in multi-source heterogeneous sensing data processing can be solved, and the processing requirements of different scenes are met.

The invention discloses an unmanned aerial vehicle airborne geophysical prospecting method and system based on big data, and the method comprises the steps: obtaining the positioning data of a global navigation satellite system and the measurement data of an inertial measurement unit, carrying out the correction and compensation through the combination of a differential positioning technology and a terrain height model, and obtaining an accurate three-dimensional coordinate data set; acquiring a magnetic field signal and a spectral signal according to the three-dimensional coordinate data set, and performing de-noising processing on the magnetic field signal and the spectral signal by adopting an adaptive filtering algorithm to obtain preprocessed signal data; judging whether the signal data volume of the preprocessed signal data exceeds a preset threshold value or not, if the signal data volume exceeds the preset threshold value, performing fragmentation processing on the preprocessed signal data through a distributed computing framework, extracting feature vectors in each piece of fragmented data by adopting a convolutional neural network, and generating a feature vector set; according to the invention, full-link optimization from data acquisition to ore body prediction is realized, and the precision and efficiency of geological exploration are improved.