51 results about "Local spectrum" patented technology

The invention relates to the technical field of image processing, and discloses a printed matter detection method and device. The method comprises the following steps: acquiring multispectral image data of a printed matter to be detected in different spectral bands; performing spectral feature extraction and multi-level fusion on the multispectral image data to obtain a comprehensive spectral feature map; acquiring sample spectral feature maps of a plurality of sample printed matters to perform frequent feature mode extraction, generating a spectral fingerprint database, and performing region division and matching degree search to obtain a reference spectral fingerprint map; carrying out multi-scale decomposition on the comprehensive spectrum feature map and the reference spectrum fingerprint map to obtain a plurality of scale layers, and carrying out local spectrum similarity calculation and adaptive threshold segmentation to obtain a binary mask map of a defect region; performing spectral feature clustering analysis to obtain defect categories; and performing defect root cause analysis and process adjustment on the printing process parameters to generate a process adjustment scheme, thereby improving the printing quality detection accuracy of the printed matter.

The invention provides a hyperspectral image classification method based on a state space model. The problem that an existing method is poor in classification precision is mainly solved. According to the scheme, the method comprises the following steps: 1) inputting a hyperspectral image, and preprocessing the hyperspectral image; 2) eliminating spectrum variability by using a neighborhood spectrum fusion method, sensing spectrum detail differences by using a sub-spectrum scanning method, and obtaining image spectrum classification information; 3) constructing a spatial feature extraction network by using an SS2D layer, an improved spatial attention mechanism and a plurality of residual blocks, and acquiring spatial classification information of the hyperspectral image by using the network; and 4) fusing space and spectrum classification information at a decision layer to obtain a final classification result. According to the method, the influence caused by spectrum variability can be reduced in a relatively small range, local spectrum information is not ignored on the premise that comprehensive spectrum information can be perceived, and spatial information is combined, so that the classification effect of the hyperspectral image is effectively improved.

The invention relates to the technical field of integrated circuit board finished product detection, in particular to a computing processing module finished product monitoring method and system, and the method comprises the steps: scanning a plurality of computing processing module finished products passing through a finished product conveying belt in real time through a linear array laser scanner disposed above the finished product conveying belt, and carrying out the real-time monitoring of the plurality of computing processing module finished products; further obtaining a spectrum pattern corresponding to each calculation processing module finished product; identifying local spectrum patterns corresponding to the plurality of chip assemblies in the spectrum pattern; and sequentially detecting whether each local spectrum pattern has a plane flaw, if so, evaluating the damaged surface of the plane flaw, and evaluating the damage degree of the damaged chip assembly based on the proportion of the damaged surface. According to the monitoring method, the spectral pattern representing the height information is generated in real time through the linear array laser scanner, the local areas of the multiple chip assemblies are accurately recognized in combination with the RGB color threshold value and the image feature extraction algorithm, interference caused by dense arrangement of the multiple assemblies is effectively avoided, and the positioning precision and the anti-noise capacity of plane defect detection are improved.

The invention relates to the technical field of millimeter wave near-field imaging, in particular to a handheld SAR (Synthetic Aperture Radar) three-dimensional fast near-field imaging method based on local spectrum compression, which comprises the following steps: acquiring original echo data, and dividing a complete comprehensive array into 2M-1 primary sub-arrays; calculating a uniform sampling grid with a uniform sampling rate of each primary sub-array in the (u, v, n) coordinate system, and converting the uniform sampling grid into a primary non-uniform sampling grid in the (x, y, z) coordinate system; reconstructing the (2M-1) first-level sub-arrays in the first-level non-uniform sampling grid to obtain (2M-1) first-level sub-images; and performing pairwise coherent superposition on the 2M-1 first-level sub-images to obtain at least two second-level sub-images, judging whether the second-level sub-images meet a preset condition or not, and if not, iteratively executing a pairwise coherent superposition process until the preset condition is met to obtain a three-dimensional reconstruction result. Therefore, the problems that a traditional imaging method is low in imaging quality, too high in calculation complexity and the like are solved.

The invention discloses a window-based decoupling short-time fractional Fourier transform time-frequency analysis method. The method comprises the following steps: S1, acquiring non-stationary signal data generated under power quality disturbance in a power grid; s2, segmenting the non-stationary signal into a plurality of parts based on signal waveform characteristics, and setting an initialized virtual window function for discrete short-time fractional Fourier transform, wherein the window function only provides an interface for calling of a specific window function; s3, calling different window functions for each segmented sub-segment signal to obtain windowed sub-segment signals under a specific window function; and S4, discrete fractional Fourier transform is applied to the sub-segment signals obtained in the step S3 to obtain time-frequency results of the sub-segment signals under different windows, and finally, frequency spectrums of the sub-segment signals are spliced according to a time sequence and output by an oscilloscope. Based on the characteristics of non-stationary signals generated under power quality disturbance in a power grid, different window function analysis signals can be called in one task by setting a virtual window function interface, and the window-based decoupling short-time fractional order Fourier transform time-frequency analysis method is formed. According to the method, the coupling between fractional Fourier transform and a specific window is reduced, the expandability of a program is improved, and more accurate signal local spectrum features can be obtained.

The invention discloses a distribution line fault detection method and device, equipment and a storage medium, and relates to the technical field of electric power inspection, hidden danger features are extracted through LSTM time sequence analysis, time sequence modeling is performed on current and voltage waveforms, harmonic distortion rate, waveform distortion and other features are extracted, and early abnormality of the equipment can be accurately identified through quantitative analysis of the features, so that the fault detection accuracy is improved. Early warning before a fault is achieved, and the passive situation that an existing system can only conduct positioning after the fault occurs is changed. The fault type is subjected to weighted fusion judgment by using the random forest + SVM algorithm, and the advantages of the two algorithms are integrated, so that the possible misjudgment condition of a single algorithm is greatly reduced, and the accuracy of fault judgment is improved. Secondly, waveform correction is introduced into a traditional double-end positioning formula, local spectrum analysis of edge nodes is combined, traveling wave propagation speed errors are corrected, dependence on clock synchronization of equipment at the two ends is reduced, and the fault positioning precision is improved.

The invention discloses an optical frequency domain reflection demodulation method based on an adaptive local spectrum matching method. The method comprises the following steps: obtaining a reference signal and a measurement signal; respectively converting the reference signal and the measurement signal into a wavelength domain to obtain a global reference spectrum and a global measurement spectrum; respectively intercepting the global reference spectrum by adopting different interception lengths to obtain a plurality of local reference spectrums with different wavelength ranges; respectively intercepting the global measurement spectrum by adopting a plurality of interception lengths which are the same as the global reference spectrum to obtain a plurality of local measurement spectrums with different wavelength ranges; and calculating an offset error value of each local reference spectrum according to each local reference spectrum and each local measurement spectrum with the same interception length, and selecting the local reference spectrum with the minimum offset error value, thereby taking the optimal offset of the local reference spectrum as the wavelength offset between the reference signal and the measurement signal. The invention discloses an optical frequency domain reflection demodulation device adopting the optical frequency domain reflection demodulation method and a sensing system.

The invention relates to the field of image processing, in particular to an intelligent defect identification method and system for spunlace non-woven fabrics, and the method comprises the steps: dividing a standard defect-free spunlace non-woven fabric image into a plurality of local image blocks according to spatial positions; constructing local spectrum prior maps in one-to-one correspondence with the image space positions; the method comprises the following steps of: partitioning an image to be detected to obtain a plurality of original image blocks, and dynamically generating a personalized frequency domain mask for each original image block based on a local spectrum priori map to suppress a frequency band corresponding to normal texture; constructing a preliminary defect residual image and a spectrogram thereof; in response to the fact that the proportion of the energy of the low and medium frequency region in the full frequency domain energy in the spectrogram exceeds a preset threshold value, mask parameters are updated, the updated mask parameters are applied to the personalized frequency domain mask, and a final defect residual image is obtained; and performing threshold segmentation and / or connected domain analysis on the final defect residual image, and outputting a defect position. The defect detection method has the effect of improving the defect detection accuracy.

A LIBS system based on dual-mode spectrum acquisition, comprising a laser excitation apparatus (1), a first spectroscopic component, a second spectroscopic component, and a data processing device. The laser excitation apparatus (1) emits a laser beam towards a sample (3), so as to excite the sample (3) to generate plasma; the first spectroscopic component is used acquire a full spectrum signal obtained after the sample (3) has been excited and convert the full spectrum signal into a first digital signal; the second spectroscopic component is used to acquire a local spectrum signal of a target frequency band obtained after the sample (3) has been excited and convert the local spectrum signal into a second digital signal, the target frequency band being an acquisition frequency band determined on the basis of material properties of the sample; and the data processing device is used to obtain and fuse the first digital signal and the second digital signal and generate a spectrum acquisition result.

The present invention provides a deconvolution method for seismic data, belonging to the technical field of seismic exploration. The method comprises: acquiring seismic data; performing spectrum analysis on each seismic data body in the seismic data to determine the main frequency of each seismic data body, performing statistical analysis on the main frequency of each seismic data body, and selecting the main frequency with the highest distribution probability as the main frequency of the seismic data; performing surface consistency prediction deconvolution on the seismic data to retain the main frequency characteristics of the seismic data; performing single-channel small step prediction deconvolution or single-channel pulse deconvolution on the seismic data after surface consistency prediction deconvolution to retain the local spectrum characteristics of the seismic data, and obtaining the final seismic data after deconvolution. The present invention includes two deconvolution processing processes when performing deconvolution processing on seismic data, which can retain both the overall spectrum characteristics of the original seismic data and the local spectrum characteristics of the original seismic data, and has a good deconvolution processing effect.

The invention relates to the technical field of satellite navigation, in particular to a use method of a satellite navigation positioning receiver, which comprises the following steps: S1, performing FFT (Fast Fourier Transform) on data sampled by a 2-cycle local code 1 / 4 chip to obtain local code spectrum data, performing intermediate frequency down-conversion on satellite data according to intermediate frequency values of satellites with different channel numbers, and then performing 1 / 4 chip resampling to obtain local code spectrum data; performing frequency mixing on the resampled satellite data and a local carrier of a preset specified Doppler frequency to complete Doppler down-conversion, performing FFT on the data subjected to Doppler down-conversion to obtain satellite spectrum data, performing bidirectional cyclic shift on the satellite spectrum data, performing conjugate multiplication on the satellite spectrum data subjected to cyclic shift and the local spectrum data, and performing frequency multiplication on the satellite spectrum data subjected to cyclic shift and the local spectrum data. The satellite navigation positioning receiver can effectively solve the problem that an existing satellite navigation positioning receiver can only passively receive the navigation signal and cannot correct the navigation signal, so that the precision of the navigation signal is poor.

The application relates to the technical field of semiconductor manufacturing, and discloses a wafer defect detection method, a wafer defect detection device and a medium. The method comprises the following steps: performing image processing on a target wafer image of a wafer object to be detected to obtain a plurality of local spectrum regions; performing complex filtering processing on the local spectrum regions in a plurality of scales and a plurality of directions to obtain response information of each local spectrum region under different scales and different directions; determining a phase consistency residual graph, a frequency band leakage residual graph and a direction offset residual graph based on phase responses and energy responses in the response information, a pre-constructed spectrum phase reference field, a main frequency band and a main direction; performing multi-scale residual back projection processing on the target wafer image to obtain a multi-scale residual back projection graph; generating a defect saliency graph based on any one or several of the phase consistency residual graph, the frequency band leakage residual graph, the direction offset residual graph and the multi-scale residual back projection graph; and performing defect detection based on the defect saliency graph to obtain a target detection result.

The invention discloses a two-stage frequency estimation method and device based on single-bit weighted fusion. According to the algorithm, a single-bit weighted fusion DFT calculation structure is provided, and nonlinear distortion caused by single-bit quantization is reduced while low calculation complexity of a single-bit DFT technology is kept. Then, through a two-stage frequency estimation algorithm, after calculation of the low-point-number FFT algorithm is completed, local spectrum interval refinement is carried out, and the limitation that traditional FFT depends on point number to improve resolution is broken through; and finally, aiming at the FFT fence effect, providing a segmented quantization Rife (SQ-Rife) algorithm which is more suitable for an FPGA (Field Programmable Gate Array) platform. According to the method, deployment is carried out in the FPGA by utilizing the thoughts of multi-bit and single-bit fusion, frequency spectrum refinement and frequency estimation, and high-speed detection of the radar signal frequency is realized.

The invention is suitable for the technical field of point cloud registration, and provides a point cloud registration method based on outlier suppression, which comprises the following steps of: obtaining a feature descriptor between a source point cloud and a target point cloud, and constructing a corresponding relationship between the point clouds, taking the corresponding relations as input, and obtaining feature embedding between the corresponding relations through a graph feature extractor; obtaining a consensus set by using coarse-to-fine sampling: firstly, obtaining a kernel point through grid point sampling, and secondly, gathering a corresponding relation with consistent characteristics around the kernel point to form the consensus set; weighted SVD is executed for each consensus set to generate corresponding rigid transformation, the weighted SVD considers the weight of each corresponding relation, and the weight is obtained through local spectrum matching; the optimal rigid transformation is selected from the rigid transformations generated in all the consensus sets. According to the method, the length consistency and the LRF compatibility are explicitly utilized, so that the performance of the outlier suppression algorithm based on learning in point cloud registration is enhanced, and the method is more accurate and reliable.

The invention discloses a cooperative spectrum prediction method based on minimum Bayesian risk, and relates to the technical field of cognitive radio and wireless communication. According to the method, each secondary user independently completes local spectrum sensing and power prediction; the fusion center performs system perception judgment by using a historical optimal fusion rule, calculates a channel state prior probability, and fits a predicted power condition probability density function of each user in two system states; then, according to a minimum Bayesian risk criterion, through alternative iteration optimization of each user judgment threshold and a system fusion rule, an optimal threshold and a fusion rule are obtained; and finally, obtaining a system-level cooperative spectrum prediction result through threshold comparison and fusion. The method does not depend on prior channel information, has good flexibility and expansibility when the number of users changes dynamically, improves prediction precision and robustness in a complex environment, and can balance main user protection and spectrum access efficiency through a cost factor.

This application proposes a method and system for monitoring the corona degradation of a generator stator based on partial discharge. The method includes: acquiring a multi-fault superimposed global partial discharge pulse (PRPD) map of the generator stator at various times within a preset time period, and marking multiple corona discharge local spectrum regions in the multi-fault superimposed global PRPD map; acquiring the number of discharges at each pulse amplitude and pulse phase within positive partial discharge pulses and the number of discharges at each pulse amplitude and pulse phase within negative partial discharge pulses at various times within the preset time period; determining the discharge pulse duty cycle, number of discharge pulses, and discharge pulse amplitude within each corona discharge local spectrum region at various times within the preset time period; and determining whether the corona performance of the generator stator has deteriorated based on the discharge pulse duty cycle, number of discharge pulses, and discharge pulse amplitude. The technical solution proposed in this application can accurately and effectively monitor the corona state of partial discharge in the original generator insulation.

This invention discloses a land cover classification method based on local spectral energy and adaptive contrast enhancement, belonging to the field of geographic information processing technology. The method first collects an image dataset of the land cover to be identified and divides it into a training set and a test set. The training set is used to train the land cover classification model, and the test set is used to test the model's performance, ultimately obtaining a recognition model that can be used for land cover classification. The improvement method involves introducing a new LFE-ACEMA module before stage 2 and stage 3 of the Swin Transformer. This module includes SEA branches, contrast branches, and spectral energy branches, and incorporates an adaptive contrast enhancement mechanism. By designing and training the improved land cover classification model, this invention achieves collaborative representation of global semantic context and local texture details, effectively suppressing inter-class confusion while accurately identifying land cover categories.

This invention relates to the field of gear testing technology, specifically to a method and system for detecting gear wear in a geared motor. The invention first obtains all fault characteristic frequencies based on the historical vibration signals of the faulty gear. Then, it divides the vibration signal of the gear under test into several local impact segments and obtains the local spectrum of each local impact segment and the kurtosis of each frequency within it. Furthermore, it combines the power distribution at each fault characteristic frequency in the time spectrum to fit a fuzzy estimation signal of the vibration signal. Then, it modulates the vibration signal based on the motor load conditions. Finally, it analyzes the spectral signal of the modulated vibration signal to assess the early wear type of the gear under test. This invention modulates the vibration signal by analyzing the masking interference of noise and motor load on local wear impacts, and further analyzes the frequency modulation and amplitude modulation effects in the frequency domain of the modulated vibration signal, thereby accurately distinguishing early wear types and improving the gear wear detection effect.

The application relates to the field of image processing, in particular to a spunlace non-woven fabric defect intelligent identification method and system, which comprises the following steps: dividing a standard non-defect spunlace non-woven fabric image into multiple local image blocks according to spatial positions; constructing a local spectrum prior atlas corresponding to the image spatial positions; performing block processing on a to-be-detected image to obtain multiple original image blocks, and dynamically generating a personalized frequency domain mask for each original image block based on the local spectrum prior atlas to suppress the frequency band corresponding to normal textures; constructing a preliminary defect residual graph and a spectrum graph thereof; in response to the proportion of the medium-low frequency region energy in the total frequency domain energy in the spectrum graph exceeding a preset threshold, updating the mask parameters, applying the updated mask parameters to the personalized frequency domain mask, and obtaining a final defect residual graph; performing threshold segmentation and / or connected domain analysis on the final defect residual graph, and outputting a defect position. The application has the effect of improving defect detection accuracy.

A computer-implemented method for managing radio spectrum in a cellular network including periodically analyzing a local spectrum utilization data to determine spectrum use levels at the local base station. The method may include periodically receiving, at the local base station, neighbor spectrum utilization data from at least one neighbor base station in the cellular network. The neighbor spectrum utilization data is associated with spectrum use levels at the at least one neighbor base station. The method may include receiving a spectrum assignment request from a mobile computing device and, in response, determining a spectrum assignment for the mobile computing device based on at least one of the neighbor spectrum utilization data and local spectrum utilization data. The method may include transmitting the spectrum assignment to the mobile computing device.

This application relates to the field of intelligent conditioning technology for grating filter sensing signals, and discloses a grating filter sensing signal conditioning method based on machine learning. The invention acquires observation response characterization data and conditional constraint state quantities, first performs spectral axis registration processing based on a preset reference frequency window to eliminate the influence of measurement coordinate offset, then performs residual frequency offset decoupling processing on the preset target frequency band to extract residual frequency offset characterization quantities, and constructs a residual dynamic state sequence by combining historical control states, further utilizes a preset time-series inference model to output voltage compensation quantity and conditioning reliability results, and finally determines the final control voltage through reliable gating conditioning and local spectrum scanning verification, thereby effectively distinguishing pseudo offset from the preset target frequency band true offset, and reducing the interference of original observation signal fluctuations, environmental changes and uneven internal electric field distribution on the control results.

Described are examples for machine learning based interference detection for tiered licensing deployments. A network entity in a general authorized access (GAA) deployment checks a local spectrum access database of GAA users to determine that a portion of shared use spectrum is free from known local users in a geographic area. The network entity receives samples of a wireless signal including at least a desired signal on the portion of shared use spectrum. The network entity determines whether the wireless signal includes multiple independent signals. The network entity identifies an interfering signal in response to determining that the wireless signal includes multiple independent signals.

The invention provides a self-adaptive frequency selection anti-interference method based on spectrum sensing in wireless communication, which comprises the following steps: acquiring network parameters and a time slot number, determining a time-frequency bit of a C1 channel in a frame period according to the time slot number, and judging whether to acquire the use right of the C1 channel, if so, generating a spectrum information broadcast message, and if not, sending the spectrum information broadcast message to a server; frequency spectrum information broadcast messages are repeatedly sent based on the time-frequency position in a frequency hopping manner; if not, local interference condition information, global sensing information, neighbor node local spectrum sensing information and C3 channel use frequency information are calculated; identifying whether the signal has unicast frequency point interference, if so, handshaking with a target node in a C3 channel on an MAC layer and replacing a high-quality frequency point; judging whether broadcast frequency point interference exists or not, and if yes, selecting a high-quality frequency point to generate broadcast channel frequency point information; judging the new and old conditions of the frequency information Fx used by the C3 channel, updating the local frequency if the frequency information Fx is new, and discarding the local frequency if the frequency information Fx is old; according to the invention, the frequency points with good channel quality are selected for communication according to the frequency spectrum, the anti-interference purpose is achieved, and reliable communication is realized.

This disclosure describes the operating concept and medium access control protocols of a wireless communication system that uses Code Division Multiple Access with Interference Avoidance (CDMA-IA) as its physical layer. The system can dynamically share a common band with other networks without a central radio resource controller. In one embodiment, the wireless communication system includes several radio nodes forming a wireless mesh network, where pairs of radio nodes use individually optimized time-division duplexing. At least one radio node includes a software-defined radio, memory, and an electronic processor. The electronic processor is configured to control the software-defined radio to transmit a pilot signal and share various status information with the other nodes in the network.The shared information includes local spectrum usage and node connectivity sets. The dominant sharing of spectrum usage among all nodes allows for maximizing the use of the shared band.

The invention relates to the technical field of hyperspectral image processing, and discloses a parallel dynamic multi-hop graph and composite multi-scale convolution hyperspectral sparse unmixing network, which comprises a dynamic multi-hop graph interactive attention module (DMGIAM) and a composite multi-scale convolution space-spectral attention module (CMS2AM). The DMGIAM captures long-distance spatial dependency features through dynamic jump perception graph convolution and a cross-head interactive attention mechanism; the CMS2AM extracts local spectrum-space features through a multi-scale convolution residual error and a double-convolution attention module. In addition, the invention further provides a self-adaptive weighted total variation loss function (SGATV) based on a Sobel operator and a Gaussian function, noise is effectively restrained, and edge details are reserved. According to the parallel dynamic multi-hop graph and the composite multi-scale convolution hyperspectral sparse unmixing network provided by the invention, the defects of an existing method in the aspects of long-distance feature capture, multi-scale information fusion and calculation efficiency are overcome, and the precision and robustness of hyperspectral unmixing are remarkably improved.

The invention discloses a respiration detection method and device, electronic equipment, a storage medium and a product, and relates to the technical field of communication. The breath detection method comprises the following steps: acquiring window time domain data of at least two subcarriers in channel state information of a WiFi signal, and determining spectrum data corresponding to the window time domain data; determining full spectrum energy of each subcarrier and local spectrum energy in a preset frequency range according to the spectrum data, and determining a local energy ratio according to the local spectrum energy and the full spectrum energy; determining a target subcarrier in the subcarriers according to the local energy ratio, and determining a subcarrier range according to a preset number of the target subcarrier; according to the method, the frequency spectrum energy mean value of all the subcarriers in the subcarrier range at each preset frequency position is determined, the target frequency position in the preset frequency positions is determined according to the frequency spectrum energy mean value, and the respiration detection condition is determined according to the target frequency position, so that the accuracy of respiration detection is improved, and the false respiration false report problem caused by interference is effectively reduced.

The application discloses a multi-modal driver anger emotion regulation method based on hearing and olfaction, and comprises the following steps: performing global acoustic feature extraction on a driver voice signal to obtain global feature information; performing local spectrum feature extraction on the driver voice signal to obtain local feature information; fusing the global feature information and the local feature information, and performing emotion classification on the fused feature information to obtain an emotion classification result; preparing an audio and an odor; and regulating an angry emotion in the emotion classification result by playing the audio and releasing the odor. The application has high emotion recognition accuracy, wide application range and good emotion regulation effect.

The application belongs to the technical field of wireless communication, and discloses a regional autonomous security cooperation spectrum sensing method, system, device and terminal, determines the number of partitions k and initializes the divided regions; makes the sensing time slot n=1 and initializes the SU trust value T(n); makes regional decision and global decision by local spectrum sensing R(n), calculates Z(n) and D(n); updates the trust value T(n+1) and judges whether n≤N is true; if n≤N exists, makes n=n+1 and returns to the step of local spectrum sensing R(n); if n≤N does not exist, judges whether T i <thT exists; if T i <thT does not exist, the SU i is judged to be an honest node; if T i <thT exists, the SU i is judged to be a malicious node. The regional autonomous cooperation spectrum sensing model of the application relieves the sensing error influence caused by malicious attackers on the whole system, detects and positions to the MU, and realizes cooperation spectrum sensing with better security and accuracy. Through simulation experiments, the application has better global decision performance and good MU detection and positioning ability.

The present invention discloses a wellhead data acquisition modeling system, comprising a data acquisition module, a hydraulic feature processing module, a well-layer coupling modeling module and an interactive control module; the present invention belongs to the field of well water monitoring and management technology, and based on an adaptive time-frequency analysis method, introduces a dynamic window function with enhanced local spectrum entropy to perform feature extraction and abnormal warning on transient hydraulic fluctuation signals, thereby significantly improving the time-frequency resolution and change recognition capability of hydraulic signals; by combining a multi-scale parameter coordination mechanism, a dynamic coupling model between a pumping system and an aquifer is constructed, and a void compression hysteresis function and a joint parameter calibration method are introduced to achieve the accuracy and dynamic adaptability of well-layer response modeling; the interactive control module implements model prediction control according to a combination of hydraulic feature vectors, hydraulic abnormality warning signals and well-layer coupling prediction parameters, and outputs wellhead data acquisition decision support data.

The invention provides a multi-satellite spectrum data interference identification method based on decentralized acquisition, and belongs to the technical field of satellite communication. According to the method, unified modeling is performed on frequency spectrum data, local frequency spectrum data are acquired according to all earth ball stations, conversion from intermediate frequency to radio frequency is performed according to the equipment connection relation of the stations, and then data collection and analysis are performed on the converted frequency spectrum data and satellite communication equipment monitoring information. Communication state confirmation, frequency interference discovery, frequency interference positioning and historical spectrum backtracking are realized. According to the invention, the unified management of the whole network spectrum resource use state in the multi-satellite multi-transponder satellite communication system can be realized, the centralized management and control of the frequency resources by satellite communication commanders can be assisted, and a powerful basis can be provided during frequency adjustment or emergency disposal.