314 results about "Electromagnetic spectrum" patented technology

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

The invention relates to the technical field of data visualization, and particularly discloses a three-dimensional electromagnetic spectrum map construction method based on tensor completion, which comprises the following steps: S1, acquiring a to-be-detected area in a simulation scene, and acquiring spectrum data in the to-be-detected area to obtain a missing data set; s2, based on a tensor singular value decomposition method and the missing data set, obtaining a complemented tensor; and S3, constructing a three-dimensional electromagnetic spectrum map based on a Poisson surface reconstruction algorithm and the complemented tensor.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention provides an unmanned aerial vehicle full-band countering method and system based on acousto-optic-electric composite detection. According to the invention, through synchronous cooperative detection of sound wave sensing, optical imaging and electromagnetic spectrum analysis, a composite detection signal is generated; based on the relevance between the acoustic characteristic frequency band and the electromagnetic control frequency band, extracting the working mode characteristics of the unmanned aerial vehicle and generating dynamic interference parameters; generating a synchronous interference instruction according to the time domain modulation characteristic of the dynamic interference parameter; in combination with abnormal noise distribution characteristics captured by the acoustic module and flight path offset identified by the optical module, power distribution and frequency band coverage of the interference instruction are corrected in real time, and optimized interference parameters are generated; and finally, a dynamic interference map is generated through overlapping frequency spectrum fusion of the dynamic interference parameters and the optimized interference parameters, accurate cooperative suppression is realized, full-band real-time accurate suppression of navigation, communication and power control signals of the target unmanned aerial vehicle is realized, and the countering efficiency in a complex scene is remarkably improved.

The invention belongs to the technical field of electromagnetic confrontation and intelligent decision making, and particularly relates to an electromagnetic situation intelligent confrontation decision making method and system based on deep learning. Threat level evaluation and resource adaptation degree analysis are carried out based on a multi-head self-attention mechanism, and a dynamic electromagnetic confrontation strategy knowledge graph is generated; a game confrontation engine driven by deep reinforcement learning is used for deducing an enemy countering path and a situation evolution result under different interference strategies of our party, interference parameters are optimized through a multi-agent collaborative learning model, weight parameters of a strategy network are updated in real time through online confrontation element learning, and historical confrontation efficiency data and current task constraints are combined to obtain a confrontation result. And dynamically generating a target interference decision sequence and an electromagnetic confrontation efficiency prediction report, and synchronously updating the electromagnetic confrontation strategy library. Therefore, the problems of serious disjunction of deduction results, low real-time antagonism and the like in the prior art are solved.

The invention relates to the field of electric power material warehouse management and control, in particular to an electric power material digital intelligent professional warehouse management and control method, which comprises the following steps: acquiring electromagnetic spectrum data, identifying a central frequency point energy distribution diagram and an electromagnetic interference intensity level, constructing a three-dimensional electromagnetic field intensity distribution model of a professional warehouse, and dynamically adjusting the working mode of an RFID reader-writer; the method comprises the following steps: deploying a multi-modal check node in a storage area of a professional warehouse, and generating a composite check result through an infrared contour matching method and a UWB coordinate verification method when the confidence coefficient of electric power material data read by an RFID reader-writer is lower than a preset confidence coefficient threshold value; updating an electric power material inventory database according to a composite verification result, and performing electric power material inventory state analysis in real time through an edge computing node; the problems that inventory data distortion and material positioning deviation are caused by reduction of the accuracy rate of RFID in a strong electromagnetic environment, and inventory accuracy management of an electric power material warehouse is seriously restricted are solved.

A vegetation monitoring device with at least one camera unit (28) for monitoring vegetation health in a garden (10), wherein the at least one camera unit (28) is configured to detect the garden area (30, 32, 34) in at least a first range of the electromagnetic spectrum, in particular in the visible light range, and in at least a second region of the electromagnetic spectrum, in particular in the infrared range, in order to determine at least one vegetation index of at least one garden area (30, 32, 34) of the garden (10), in particular in the region of visible light, and in at least one second region of the electromagnetic spectrum, in particular in the infrared range, wherein the camera unit (28) is provided for an arrangement at least substantially above ground level of the garden (10) and for an at least substantially stationary arrangement outside or in the vicinity of the garden (10), and a vegetation monitoring system is proposed.

The invention relates to the field of electromagnetic signal completion, and particularly discloses a low-rank autoregression electromagnetic spectrum tensor completion method and device. Comprising the following steps: acquiring incomplete electromagnetic spectrum data, constructing a three-dimensional tensor to represent the incomplete electromagnetic spectrum data, determining unknown pixel points, performing translation invariant wavelet transform, constructing a wavelet coefficient tensor by using a coefficient after wavelet transform, performing data replacement by using the wavelet coefficient tensor, and introducing local consistency constraint of time. And obtaining a low-rank autoregression tensor completion model, solving the low-rank autoregression tensor completion model by using an ADMM algorithm to obtain an updated auxiliary variable, an updated unknown variable and an updated recovery tensor, judging whether a convergence condition is satisfied or a preset number of iterations is reached, and if so, outputting the completed electromagnetic spectrum data. The objective of the invention is to solve the problem that effective completion of electromagnetic spectrum data can still be realized under the conditions of large data missing amount and large continuous time dimension missing amount.

The invention discloses a partial discharge online monitoring method based on an ultrahigh frequency original signal, and relates to the field of partial discharge monitoring, and the method comprises the steps: autonomously switching an ultrahigh frequency signal capturing frequency band according to the real-time electromagnetic spectrum distribution of a monitoring environment, and obtaining an original signal sequence covering a potential partial discharge feature; the method comprises the following steps: preprocessing an original signal sequence through multi-scale pulse contour extraction, and suppressing environmental noise interference to retain pulse characteristics of partial discharge signals; through real-time electromagnetic spectrum analysis, ultrahigh frequency capture frequency bands are automatically switched, environmental noise changes are dynamically adapted, original signal capture accuracy is improved, through multi-scale pulse contour extraction preprocessing, noise interference is effectively suppressed, key pulse features are reserved, and the accuracy of signal capture is improved. A multi-dimensional feature data set constructed based on the pulse peak gradient, the duration spectrum and the waveform similarity provides a comprehensive basis for partial discharge recognition.

The invention relates to the technical field of state monitoring, in particular to a non-intrusive equipment operation state monitoring method and system based on an electromagnetic spectrum. The method comprises the following steps: dynamic multi-dimensional spectrum fingerprint extraction; generating a prototype according to the extracted multi-dimensional spectrum fingerprints; respectively performing intra-prototype contrast learning and inter-prototype contrast learning based on the generated prototypes; constructing a prototype contrast loss function based on contrast learning; image modeling is carried out according to the extracted multi-dimensional spectrum fingerprints, and an image comparison loss function is constructed based on image modeling; performing function mixing based on a loss function mixing strategy to obtain an overall loss function; performing equipment state judgment based on the overall loss function; and outputting a discrimination result. The problem of wide-frequency-band electromagnetic signal aliasing in a petroleum equipment dense scene is effectively solved.

The invention provides an electromagnetic spectrum situation reinforcing method based on a K-means clustering algorithm, relates to the field of electromagnetic spectrum management and situation awareness, and aims to effectively complement electromagnetic spectrum data through unsupervised learning and overcome the problems of dependence on a large number of samples and sample imbalance and migration in a traditional method. The method comprises the following steps: firstly, carrying out grid division on a target area, and collecting electromagnetic spectrum data of each grid point through measuring equipment; then, an elbow method is used for determining the optimal clustering number of K-means clustering, clustering analysis is carried out on the collected data, and the clustering center of the frequency spectrum situation is obtained; and then, complementing missing spectrum data based on a clustering result to generate a complete spectrum situation map. And the accuracy of the complemented data is verified through similarity evaluation. According to the method, the calculation complexity is reduced, the accuracy and reliability of spectrum situation sensing are improved, and the method is suitable for real-time management and optimization of spectrum resources.

The invention discloses an electromagnetic spectrum prediction method and system based on fractional Fourier transform. The method comprises the following steps: constructing historical spectrum observation data in a target area; mapping the collected spectrum observation data to a corresponding fractional order Fourier domain by using adaptive fractional order Fourier transform, and adaptively adjusting fractional order parameters of a fractional order Fourier transform layer to output a fractional order Fourier domain containing predictable spectrum components; in the mapped fractional order Fourier domain, non-predictive noise in the converted data is filtered out; performing feature extraction and prediction on the frequency spectrum data of the fractional order Fourier domain after noise filtering by using a complex valued linear network to obtain a prediction result of the fractional order Fourier domain; and mapping the prediction result of the fractional Fourier domain back to the original time domain by adopting inverse fractional Fourier transform, and generating final frequency spectrum prediction output. According to the method, the accuracy of weak periodic spectrum prediction is effectively improved through fractional Fourier transform and a filtering strategy.

The invention relates to the technical field of radio, and particularly discloses a high-precision electromagnetic spectrum map construction method and system for a topographic relief environment, and the method comprises the steps: obtaining spectrum data in a to-be-constructed region, the spectrum data comprising signal intensity and geographic position; combining terrain digital elevation and topographic relief elevation information in a to-be-constructed region with the signal intensity, and matching with an observation point according to resolution to obtain topographic relief elevation information of the observation point; establishing a model for the topographic relief elevation information of the observation point based on a Gaussian regression process, and performing parameter optimization on the model by using hyper-parameters of a kernel function; and according to a posterior probability estimation formula of the Gaussian regression process, constructing a spectrum map of the to-be-constructed region by using the monitoring value of the spectrum data in the to-be-constructed region as input.

The invention discloses a lightweight long-term spectrum prediction method based on a multi-scale mixed structure, and relates to the technical field of electromagnetic spectrum prediction. The method comprises the following steps: acquiring spectrum time sequence data, preprocessing, and dividing into a training set and a test set; a spectrum prediction model is constructed, the spectrum prediction model is composed of a trend decomposition module, a multi-scale coding module and a hybrid decoding module, the macroscopic trend and short-term fluctuation characteristics of spectrum data are captured through trend decomposition, multi-scale feature representation is generated through multi-scale coding, and a prediction value is generated through hybrid decoding fusion; expanding the model to spatial data, constructing a spatial spectrum data set, preprocessing, and inputting into the model for prediction. The long-term spectrum use condition can be accurately and efficiently predicted, the prediction precision is improved, the computing resource consumption is reduced, the generalization ability is high, and important support is provided for spectrum management.

An automatic heat-sealing quality control system to automatically control a quality of a heat-sealing of a sealing strip to a longitudinal edge of a packaging web at a heat- sealing station in a packaging machine, where sealed packages containing pourable food products are continuously produced from a continuous vertical tube filled with the pourable food product and formed by longitudinally folding the packaging web to overlap longitudinal edges thereof and then heat-sealing the overlapping longitudinal edges. The system comprises a visible-light imaging camera designed to operate in the electromagnetic spectrum visible to human eye to capture and output visible-light digital images.

The invention belongs to the technical field of cognitive radio, and particularly relates to an open set modulation identification method based on time-frequency domain feature learning and fusion. The method comprises the following steps of: receiving a signal by adopting a receiving antenna through electromagnetic spectrum monitoring equipment, capturing a space electromagnetic radiation signal, converting the space electromagnetic radiation signal into an electric signal, amplifying the signal through a low-noise amplifier, performing frequency conversion and filtering through an analog receiver, and converting the received signal into an intermediate-frequency signal. Then, the signal is digitalized through AD sampling, and the intermediate frequency signal is converted into a baseband complex signal through frequency mixing and digital filtering in a digital receiver. Then performing time domain characterization calculation and frequency domain characterization calculation on the signal sampling data to form a plurality of time domain characterization vectors and a plurality of frequency domain characterization vectors; and then the vectors are sent to a trained deep neural network model, reasoning is carried out by using the network model, a reasoning result is post-processed, and a signal modulation identification result is finally obtained. According to the invention, the recognition accuracy of unknown signals is improved.

The invention discloses an obstacle avoidance tracking method based on deep reinforcement learning, and belongs to the field of path planning. The invention relates to a multi-unmanned aerial vehicle reconnaissance near-end strategy optimization method based on a distributed reinforcement learning framework, and the implementation method is as follows: through deep fusion of a pressure sensing mechanism and an improved SAC reinforcement learning method, precise avoidance of dynamic obstacles and efficient pursuit of abnormal radiation sources are realized. According to the pressure sensing module designed by the invention, the environmental pressure is quantized into a numerical index, and the conservative degree of a decision strategy is dynamically adjusted according to the numerical index. On this basis, decision optimization is carried out by using a double-Q network architecture of an improved SAC algorithm, and a conservative strategy and an aggressive strategy are adopted for parallel evaluation, so that the robustness of the strategies is improved while the exploration efficiency is ensured. The method operates in a continuous action space, and the smoothness and stability of the motion trail of the unmanned aerial vehicle are ensured. According to the invention, the unmanned aerial vehicle can intelligently track an abnormal radiation source in a high-voltage environment in complex urban electromagnetic spectrum combat.

Disclosed are systems, devices and methods for quantifying unique features of an object such as an artistic work to identify and authenticate the object and specific characteristics thereof using multi-spectral diagnostic characterization techniques and analytical algorithms. In some aspects, a method for creating an identification for an object includes acquiring image data of an object in two or more electromagnetic spectrums along a coordinated array of sample regions of the object; analyzing the acquired image data to produce a quantitative data set including specific characteristics of the object associated with the two or more electromagnetic spectrums for each sample region; generating a digital identification associated with a unique data fingerprint, based on the specific characteristics, in which the digital identification solely corresponds to the object; and storing the generated digital identification.

The invention belongs to the technical field of cognitive radio, and particularly relates to a broadband multi-signal detection method based on a large-size convolution kernel. The method mainly comprises the following steps of: receiving a signal by adopting a receiving antenna through electromagnetic spectrum monitoring equipment, capturing a space electromagnetic radiation signal and converting the space electromagnetic radiation signal into a baseband complex signal, carrying out large FFT point short-time Fourier transform on signal sampling data to form a high-resolution and large-size time-frequency matrix, and carrying out time-frequency spectrum detection along a frequency axis of the time-frequency matrix; a time-frequency matrix is cut into a plurality of time-frequency matrix fragments adapted to neural network processing, and parameters such as confidence probability, center frequency, bandwidth, arrival time, duration and the like of existence of signals in each time-frequency matrix fragment are automatically calculated and generated by using a specially designed and trained deep neural network model. The method has the advantages that the operation of monitoring equipment is simplified, the signal detection speed is high, the detection accuracy is high, the detection recall rate is high, and the time-frequency occupancy parameter estimation precision is high.

The invention provides a multi-agent cooperation method and device for electromagnetic spectrum monitoring and analysis, and belongs to the technical field of artificial intelligence. The method comprises the following steps: receiving a task demand, and obtaining interface information of an electromagnetic detection tool model; analyzing the task demand through the first agent to generate a preliminary task intention; generating a task planning book containing a plurality of subtasks based on the preliminary task intention and the interface information; calling an electromagnetic detection tool model to execute a subtask through a task scheduling module of the second agent, and obtaining intermediate result information and final label information; and based on the intermediate result information and the final label information, performing feature fusion and intention inference through a second agent, and generating an intention inference result of the electromagnetic signal and an evidence chain supporting the intention inference result. Based on the method, the invention also provides a multi-agent cooperation device for electromagnetic spectrum monitoring and analysis. According to the invention, the recall rate and robustness of spectral analysis are improved, and the detection rate of anomaly detection is improved.

Provided herein are photocurable compositions that provide a balance of fast cure characteristics under exposure to radiation in the electromagnetic spectrum with a deep impressive cure transmission depth.

The invention discloses a power inspection-oriented low-altitude electromagnetic abnormal trend early warning method and system, and the method comprises the steps: synchronously collecting electromagnetic spectrum data and flight state data of an unmanned plane when the unmanned plane executes a low-altitude power inspection task, and generating a space-time aligned electromagnetic-flight data sequence; performing feature extraction and matching on the electromagnetic-flight data sequence based on an electromagnetic interference feature library of a typical power interference source, and constructing a dynamic electromagnetic environment situation map by fusing spatial position information of the unmanned aerial vehicle; performing electromagnetic abnormal risk analysis on the dynamic electromagnetic environment situation map by adopting an analysis model trained by a historical power electromagnetic interference event to obtain a dynamic risk assessment coefficient; according to the dynamic risk assessment coefficient, predicting an electromagnetic anomaly trend index in a future preset time period; and comparing the pre-warning threshold value with the multi-level pre-warning threshold value conforming to the electric power safety regulation, and when the pre-warning threshold value exceeds the multi-level pre-warning threshold value, triggering a corresponding pre-warning level and generating a risk avoidance strategy adaptive to the low-altitude electric power inspection scene.

The invention discloses an unmanned aerial vehicle illegal intrusion countering system and method based on electromagnetic spectrum detection, and relates to the technical field of unmanned aerial vehicle countering, and the system comprises an electromagnetic spectrum detection module, a spectrum analysis processing module, an intelligent decision module and a countering execution module. By constructing a distributed electromagnetic spectrum detection network, the comprehensiveness and the accuracy of signal acquisition are improved, a spectrum analysis algorithm is combined, spectrum features are deeply mined, illegal intrusion signals of the unmanned aerial vehicle are accurately identified, high-precision detection and rapid early warning of illegal intrusion behaviors of the unmanned aerial vehicle are realized, and an intelligent self-adaptive countering strategy is utilized, so that the safety of the unmanned aerial vehicle is improved. According to the real-time state information of the unmanned aerial vehicle, the intensity, frequency and mode of countering measures are automatically adjusted, the countering effect is improved, the working frequency band of surrounding legal electronic equipment can be dynamically avoided in the countering process, interference to the electronic equipment is greatly reduced, stability of the electromagnetic environment in the area is guaranteed, and the safety of the unmanned aerial vehicle is improved. And the capability of preventing and coping with illegal invasion of the unmanned aerial vehicle is effectively improved.

A molecular-spin qubit includes a molecular color center having a ground state and an excited state. The ground state has non-zero spin with at least first and second sublevels. The molecular-spin qubit also includes a host matrix that is non-isostructural with the molecular color center. The molecular color center is diluted in the host matrix. An optical transition between the ground and excited states lies in the optical region of the electromagnetic spectrum. A spin transition between the first and second sublevels lies in the microwave or millimeter-wave regions of the electromagnetic spectrum. Each of the first and second sublevels is first-order insensitive to magnetic fields near zero magnetic. The molecular color center and host matrix may each be formed from strong-field ligands bound to a metal-atom center. One example of the molecular-spin qubit is Cr(IV)(o-toyl)4 diluted in a host matrix of Sn(IV)(4-fluoro-2-methylphenyl)4.

An approach to remotely and noninvasively detect and evaluate the response of a plant or plant population to a man-made or natural treatment regime (e.g., herbicide, fungicide or fertilizer treatment) via spectral imaging methods and systems comprising the capture of a plurality of spectral images for a common plant scene, each associated with a selected wavelength region of the electromagnetic spectrum, the formulation of an index function from the spectral information indicative of the plant response over time, and the assessment of mathematical parameters quantifying the time-varying plant response to the treatment regime. The plant response to a treatment regime may be quantified in illustrative embodiments in a fraction of the time previously required by many conventional approaches. Applying varying herbicide dosages to segments of the same plant population enables easy determination of a dose-response curve.

An automatic heat seal quality control system (12) for automatically controlling the quality of heat seals at the longitudinal ends of a packaging web (3) of a sealing strip (6) in a heat seal station (7) of a packaging machine (1). A sealed package (5) containing a cast food product is continuously manufactured from a continuous longitudinal tube (2) filled with the cast food product. The automatic heat seal quality control system (12) is formed by folding the packaging web (3) longitudinally and overlapping its longitudinal ends, and heat sealing the overlapping longitudinal ends. This system (12) includes a visible light camera (19) designed to operate in the electromagnetic spectrum visible to the human eye in order to capture and output a visible light digital image, and captures and outputs a visible light digital image.

Systems and methods include a computer-implemented method for analyzing rock samples. Rock and oil electromagnetic baselines are determined for rock samples in at least a section of an electromagnetic spectrum ranging from ultraviolet to long terahertz radiation. An aging process is conducted on each rock sample, initially starting with the rock and oil electromagnetic baselines. The aging process is repeated using spectrometry on the rock sample and measured wettabilities of the rock sample until changes in spectra are less than a predetermined threshold. Aging information including the spectra and wettabilities are stored in a machine learning database. Spectra are obtained from an unknown rock sample. The spectra are mapped to clusters in the machine learning database. Wettability ranges are determined for the unknown rock sample based on a mapping of the spectra of the unknown rock sample to clusters in the machine learning database.

A phototherapy face mask is provided for emitting therapeutic radiation towards the facial region of a user and aims to restore the youthful appearance of the user who is wearing the mask. The phototherapy mask comprises an outer surface and an inner surface, wherein the inner surface is aligned with the facial skin of the user. A flexible PCB layer is provided between the inner surface and the outer surface, on which a plurality of coherent light sources such as LEDs are mounted. The light sources are configured to emit radiation preferably in the visible and infrared range of the electromagnetic spectrum. The mask further includes a medicament chamber which is operably coupled to a piezoelectric pump that dispenses the medicament at regular intervals. The medicament is uniformly distributed all over the facial region of the user through a plurality of pores of the mask.

The invention discloses a complex electromagnetic environment spectrum sensing method and system based on a multi-dimensional feature domain, and the method comprises the steps: dividing an azimuth dimension 360-degree airspace into 24 regions, and respectively receiving electromagnetic wave signals in different directions; calculating time-frequency instantaneous energy of the electromagnetic wave signal of each area; effective signals in each airspace are detected, and the time-frequency amplitude is calculated; all signals with time-frequency amplitudes exceeding a preset threshold value are screened out, the instantaneous frequency of the signals is calculated by adopting an accumulated phase difference method, a spatial domain multi-angle time-frequency two-dimensional characteristic matrix is obtained, and an unoccupied spectrum range, namely a spectrum hole, is identified; and planning a communication link according to the number of the spectrum holes and the available spectrum range thereof. According to the method, the real-time performance and the accuracy of electromagnetic spectrum situation sensing of electromagnetic spectrum cognitive equipment can be effectively improved through multi-dimensional domain characteristic analysis of the spectrum in a complex electromagnetic environment, so that the frequency utilization strategy of frequency utilization equipment is guided.

The invention provides a sky-ground electromagnetic spectrum data fusion processing method and system and a storage medium, relates to the technical field of multi-source electromagnetic spectrum data fusion, and aims to solve the problems of different space-time references, data redundancy and stray in the sky-ground electromagnetic spectrum monitoring data fusion process. Comprising the following steps: step 1, carrying out time registration on sky-ground electromagnetic spectrum monitoring data; step 2, space registration is carried out on the sky-ground electromagnetic spectrum monitoring data, system equivalent errors are estimated, and system deviation of a base station measurement spectrum value is compensated; and step 3, carrying out space-time fusion of the multi-source spectrum data based on particle filtering of sequential importance sampling, including avoiding particle weight degradation through resampling, updating the confidence coefficient weight of the spectrum data of each base station in an elastic interactive manner based on the residual posterior probability, and finally generating fused sky-ground electromagnetic spectrum data in combination with the confidence coefficient weight.