91 results about "Time spectrum" patented technology

The invention discloses a scanning high-resolution double-optical-comb spectrum system based on Raman gain modulation. The method is suitable for real-time spectrum detection with a wide spectrum range and high spectral resolution. The system comprises a single-frequency laser and driver, an optical coupler, a mode-locked laser, a rare earth doped amplifier, a wavelength division multiplexer, a Raman gain fiber, a sample to be tested, a photoelectric detector, a data processor and a wavemeter. The single-frequency laser couples part of light to the wavemeter, and the remaining light is divided into two paths to be coupled with the two amplified mode-locked lasers to enter the Raman gain optical fiber for Raman gain modulation and is converted into high-mutual-dryness double optical combs. The double combs pass through a to-be-measured sample in a combined manner, and interference signals are measured by the photoelectric detector and received by the data processor. And meanwhile, the processor sends a tuning signal to the single-frequency laser driver, so that the double combs scan in a wide spectrum range to obtain high-resolution spectrum information. Electrical feedback locking is not needed, and high-precision spectrum acquisition with megahertz resolution in a terahertz range can be completed within a few minutes.

The present invention comprises a method for jointly processing data from the nuclear magnetic resonant (NMR) response of oil and rock samples in in natura condition, and its main product is the delimitation of the oil saturation (So) present in the rock samples, i.e., the determination of the range of admissible values of the volumetric fraction of the porous volume of the rock occupied by such fluid. The method requires a small volume of oil, originating from or of a composition similar to the oil present in the rock being tested, since it is based on both the measurement of the NMR response of the rock sample and the response of the fluid sample. These two sets of data feed a statistical inference scheme that (1) determines a range of admissible values for the So of the sample, delimited by the minimum and maximum oil saturation values compatible with the two sets of data presented; (2) produces, for each of these extreme values of admissible So, a decomposition of the rock relaxation time spectrum into two complementary spectra, the first highlighting the signatures relating to oil and the second comprising the distribution of the rock relaxometric signatures that cannot correspond to such fluid under the prescribed saturation condition.

The invention discloses a multi-channel integrated radio frequency system based on double-threshold interference detection and dynamic spectrum aggregation. A radio frequency front-end unit is used for realizing broadband spectrum access and performing analog-to-digital conversion; the digital channel processing unit is used for realizing channelized segmentation through up-conversion and down-conversion, carrying out band elimination filtering to shield unavailable channels and outputting filtering parameters to support dynamic channel recombination; the digital signal parallel processing unit performs parallel access by using the divided channels; the spectrum sensing unit is used for carrying out double-threshold channel interference detection and real-time spectrum analysis according to the parallel access signals; and the frequency spectrum decision-making unit is used for judging the channel state and fusion detection judgment according to the interference detection result, judging the availability of a specific channel in the data link network and carrying out cooperative dynamic frequency spectrum aggregation. According to the method, a consistent spectrum access decision is realized by adopting a spectrum monitoring, sensing convergence and fusion decision mechanism, and consistent understanding of an available frequency set and high-reliability network domain dynamic spectrum aggregation are guaranteed.

The invention belongs to the technical field of electromagnetic spectrum management, and particularly relates to an intelligent planning method for an electromagnetic spectrum time sequence. Comprising the following steps: collecting real-time spectrum use data through a sensor network or wireless communication equipment; carrying out preprocessing operation on the collected data; constructing an electromagnetic spectrum time sequence intelligent planning model based on the preprocessed data; formulating a dynamic spectrum allocation strategy according to a prediction result of the intelligent planning model; and the allocation strategy is continuously optimized and adjusted in the spectrum allocation process. According to the algorithm provided by the invention, efficient and reasonable time sequence planning is carried out on electromagnetic spectrum resources in an intelligent mode, so that the increasing wireless communication requirements are met, and the utilization rate of the spectrum resources and the overall performance of a communication system are improved.

The invention relates to a method for realizing full-time frequency spectrum monitoring based on a nest unmanned aerial vehicle and a fixed monitoring station. The method comprises the following steps: issuing a full-time frequency spectrum monitoring task; continuously scanning the monitoring target frequency band; extracting a signal frequency, and performing parameter measurement; signal parameters which are not matched with the known signal library are stored, and the equipment state is recovered; setting a time interval, and generating a direction finding task list; the unmanned aerial vehicle advances according to a preset track and performs a monitoring direction finding task; the unmanned aerial vehicle returns to enter the nest, and charging and heat dissipation reconditioning are automatically completed; and updating the signal positioning data. By adopting the method, the system, the device, the processor and the computer readable storage medium for realizing full-time frequency spectrum monitoring based on the nest unmanned aerial vehicle and the fixed monitoring station, a reliable positioning result is given through a multi-point direction finding result of the unmanned aerial vehicle on the whole track, high-frequency weak signals are easy to capture, the monitoring utilization rate of the unmanned aerial vehicle is greatly improved, and the real-time performance of the unmanned aerial vehicle is improved. The total element information of the signal is collected, and the management is scientific and accurate.

1. The name of the design product: graphical user interface for portable spectrum analyzer (ZP128). 2. The use of the design product: the design product is used for basic spectrum and real-time spectrum analysis, interference analysis and positioning, demodulation analysis, and 4G, 5G base station measurement. 3. The design points of the design product: the graphical user interface display content of the portable spectrum analyzer. 4. The picture or photo that best indicates the design points: interface change state diagram 1. 5. Belongs to the usual design, omit other views. 6. The use of the graphical user interface: for the operation, running management of the portable spectrum analyzer. 7. The area of the graphical user interface in the product: the graphical user interface of the design product is located on the front of the portable spectrum analyzer, which is embodied in the front view. 8. The human-computer interaction mode of the graphical user interface: the front view is the product carrier diagram with graphical user interface; the front interface enlargement is the screen enlargement in the front view, which is the startup interface automatically displayed after the product is started; interface change state diagram 1 is automatically entered after the display of the front interface enlargement is completed, which is the interface at the last shutdown; interface change state diagram 2 is the optional interface entered by clicking the "four-square icon" in the upper left corner of interface change state diagram 1; interface change state diagram 3 is the 5G measurement interface entered by clicking "5G-NR analysis" in interface change state diagram 2; interface change state diagram 4 is the setting interface entered by clicking "setting" in interface change state diagram 3; interface change state diagram 5 is the threshold editing interface entered by clicking "SINR threshold" in interface change state diagram 4.

The present application relates to a kind of communication system howling detection method and device, it is related to howling detection technique, the method is based on peak power spectrum stability measure detects whether there is first type howling frequency point index in the short time spectrum to be detected at t frame, if not, it is based on peak-average amplitude ratio stability measure and peak-harmonic power ratio detects whether there is second type howling frequency point index in the short time spectrum to be detected at t frame, first type howling frequency point index is the local peak frequency point index of peak power spectrum stability measure greater than preset peak power spectrum stability measure threshold, second type howling frequency point index is the local peak frequency point index corresponding to peak-average amplitude stability measure greater than preset peak-average amplitude stability measure threshold and peak-harmonic power ratio greater than preset peak-harmonic power ratio threshold, the scheme is combined with judgment using two kinds of howling frequency point index, effectively improve howling detection accuracy.

The described systems and related test methods provide a significant improvement in test accuracy, as well as a reduction in test time for system-level electromagnetic environmental effects (E3) testing. The systems may include a real-time spectrum analyzer, a network analyzer, and a switching / filtering / coupling network, controlled by an information processing device such as a general-purpose computer. These systems can scan all frequencies at each receiver.

The application provides a remote sensing image cloud removal method and system fusing gradient fidelity and time spectrum consistency, relates to the technical field of remote sensing image processing and tensor modeling, and comprises the following steps: firstly, acquiring multi-temporal remote sensing images and cloud mask data, and generating a fault mask through active fault identification; then, calculating a guided gradient tensor and performing low-rank approximation processing to obtain spatial feature factors and time spectrum feature factors; based on this, a multi-objective optimization model of a gradient domain fidelity term, a pixel domain fidelity term and a time spectrum consistency constraint term is constructed; a proximal alternating minimization algorithm of an embedded alternating direction multiplier method is used for efficient solution, and the reconstruction result is modified in combination with the cloud mask and the active fault mask, and a high-quality cloud-removed image sequence is output. The application can effectively maintain the image edge structure, texture details and space-time consistency under a complex cloud coverage scene, and significantly improves the availability and analysis value of remote sensing data.

The invention discloses a multi-client real-time spectrum monitoring system and method based on a GPU, and belongs to the technical field of spectrum analysis, and the method comprises the steps: obtaining a spectrum state, behavior information, a host state and a business service quality demand of a client; calculating a client interference intensity coefficient, a behavior state coefficient, a host state coefficient and a service quality coefficient through a corresponding model; dynamically distributing the calculation power of each client by using a power distribution model based on the coefficients; and finally, outputting a target refresh rate of each client through a refresh rate optimization model in combination with the distributed power and the service quality coefficient. Through multi-dimensional state sensing and modeling, the problems that in the prior art, resource distribution is rigid, and differentiation requirements of multiple clients cannot be met are solved, intelligent, dynamic and efficient distribution of system computing resources is achieved, and the overall energy efficiency and stability of the system are improved while the multi-business service quality is guaranteed.

This invention provides a deep learning-based method for calculating the relaxation time distribution of nuclear magnetic resonance (NMR) signals. Based on the characteristics of Laplace NMR signals, a mathematical model of an exponentially decaying signal with noise and an ideal relaxation time spectrum are constructed. The ideal relaxation time spectrum serves as a label, and the position of the spectral peak represents the exact value of either the T1 or T2 relaxation time. The full width at half maximum (FWHM) of the spectral peak corresponds to the uncertainty of the result. Simulated signals are generated from the mathematical model, and training and test sets are constructed. A network model is built, and the training data is input into the network model for training. The test data is then input into the network model for testing. The method provided by this invention relies on a mathematical model of the exponentially decaying signal during the relaxation process to construct a dataset for training. This eliminates the need to collect large amounts of real data and allows for NMR relaxation time spectrum calculations to be completed within seconds. Furthermore, it exhibits high robustness to test data with different signal-to-noise ratios.

The invention discloses a radio partial discharge detection positioning method, device and equipment, and relates to the technical field of radio monitoring, and the method comprises the following steps: obtaining the real-time spectrum data of an electromagnetic environment, carrying out the feature learning through a deep reinforcement learning algorithm, and constructing an electromagnetic environment feature fingerprint database; filtering the real-time spectrum data, extracting a target pulse characteristic quantity, inputting the target pulse characteristic quantity into a deep twin network, and screening out an effective pulse signal; performing signal decomposition on the effective pulse clusters by applying an improved variational mode decomposition algorithm to obtain each discharge source signal; and carrying out TDOA time difference correction calculation on each discharge source signal, carrying out dynamic error correction in combination with an environment adaptation coefficient of the electromagnetic environment fingerprint database, and outputting a three-dimensional positioning result. According to the invention, high-precision detection and positioning of multi-source discharge signals can be realized in a complex electromagnetic environment, the sensitivity and reliability of partial discharge monitoring are significantly improved, and meanwhile, accurate data support is provided for fault early warning and maintenance of power equipment.

The invention provides a wireless communication positioning method and device, electronic equipment and a storage medium, and belongs to the technical field of communication. The method comprises the following steps: acquiring a received signal; performing channel estimation on the received signal to obtain a channel estimation value and a signal-to-noise ratio of each antenna array element; based on the channel estimation value, an arrival time spectrum and an arrival direction spectrum are respectively constructed, the arrival time spectrum comprises arrival time spectrum data on each antenna array element, and the arrival direction spectrum comprises arrival direction spectrum data on each observation frequency point; based on the signal-to-noise ratio and the arrival time spectrum of each antenna array element, calculating to obtain an observation time spectrum corresponding to each transmission delay; obtaining a signal transmission time delay based on the maximum value in the observation time spectrum corresponding to each transmission time delay; determining a signal arrival direction angle based on the arrival direction spectrum; and obtaining positioning information based on the signal transmission time delay and the signal arrival direction angle. The influence of noise on the search result is suppressed, and the positioning precision is improved.

This invention discloses a hardware-software co-processing method for real-time spectral signal processing in Raman microscopy, belonging to the field of spectral signal processing technology. The method includes: establishing strict temporal constraints between physical displacement and cloud inference, and initiating asynchronous communication using the physical gaps in stage movement; simultaneously, deploying a deep learning model (F2P) pre-trained with a self-supervised masking strategy in the cloud. This pre-trained model utilizes the structural correlation between the Raman broadband background and narrowband characteristic peaks to directly perform real-time forward inference on the input single-frame noisy spectrum, achieving millisecond-level high-fidelity reconstruction of a single frame without any complex parameter tuning or the need for a clean control spectrum. This invention completely eliminates the physical lag in multi-frame processing and the general technical bias of supervised learning algorithms, achieving true "synchronization between acquisition and display," and significantly improving the real-time processing performance and cross-device automated deployment capabilities of in-situ Raman imaging.

The present application relates to a kind of discrete shared frequency band spectrum sensing system and method, belong to wireless communication technical field.The system includes base station, edge computing device and several sensing terminals, multiple sensing terminals collect spectrum data and carry out spectrum sensing, and sensing result is sent to base station, and base station forwards sensing result to edge computing device and carries out multiple sensing result fusion processing, and edge computing device is fused after multiple sensing results by a pre-trained binary prediction model Prediction of subsequent multiple time steps of spectrum occupation, and spectrum prediction result is sent to base station, and base station filters idle spectrum frequency point according to spectrum prediction result and carries out communication with sensing terminal.The method is based on the system and carries out discrete shared frequency band sensing, can adapt to the signal coverage range of discrete frequency band, unknown communication system exists and needs real-time spectrum analysis.

The application provides a process state real-time correction method based on acoustic characteristics, and relates to the technical field of process control, and comprises the following steps: collecting acoustic signals generated by interaction between a processing medium and a processing unit when the processing unit is executing a processing task; performing real-time spectrum analysis on the acoustic signals to extract characteristic frequencies and energy distribution characteristics thereof which are related to the current physical state of the processing medium; determining whether the current physical state of the processing medium is in an abnormal critical region in real time based on changes of the characteristic frequencies and the energy distribution characteristics thereof relative to a preset reference; the abnormal critical region comprises a first precursor state in which the processing medium tends to be excessively dried or a second precursor state in which the processing medium tends to be excessively accumulated; and if it is determined that the processing medium is in the abnormal critical region, the physical state of the processing medium being currently processed is corrected in real time by adjusting at least one execution parameter which influences the physical state of the processing medium before the processing medium being currently processed reaches a target region.

The invention relates to the technical field of petroleum well logging, in particular to a data acquisition and time sequence synchronous processing method and system for a neutron generator, and aims to solve the technical problems of synchronous lock loss in a high-temperature and quick-change environment and difficulty in realizing high-precision time sequence correction at a low counting rate. The method comprises the following steps: S1, acquiring a neutron trigger signal, gamma time spectrum data and environment state data of a downhole instrument; s2, calculating a time sequence gravity center shift index by using an energy gravity center method; s3, constructing a thermal shock confidence coefficient; and S4, calculating a final time sequence compensation step length, and adjusting the trigger delay of the acquisition circuit or the gamma time spectrum data in the cache by using the time sequence compensation step length. According to the method, the signal-to-noise ratio constraint is introduced, so that the confidence coefficient is automatically reduced when the signal quality is poor, misadjustment caused by random noise is inhibited, and the data stability under a low counting rate is ensured.

The invention discloses a time spectrometer system and method based on a microchannel plate photomultiplier (MCP-PMT). The constant proportion discriminator is triggered by adopting an attenuation branch / delay branch differential zero crossing point, and performs latch width limiting and level conversion on a trigger pulse; fPGA-based multi-level carry chain time TDC is adopted, and parallel tap sampling, bubble suppression and code density calibration table look-up correction are carried out; a processing flow of time window histogram accumulation, a first effective hit strategy, a background adaptive threshold and multi-peak positioning and fitting is adopted. According to the invention, the picosecond-level timing precision is obtained at a low clock rate, the sensitivity and precision of weak light signal flight time distance measurement are significantly improved, and the method is suitable for laser radar imaging and high time sequence of time correlation single photon counting, can realize sub-hundred picosecond-level time resolution, and is suitable for flight time distance measurement under a weak light condition.

The application provides a self-supervised abnormal sound detection method based on spectrum and time information combination, comprising the following steps: collecting original audio clips of each target device in advance, and storing the original audio clips in association with machine ID as an identifier; using a preset mel filter to extract a mel spectrum graph of the original audio clips; using a preset time feature extraction network to extract a time spectrum feature graph of the original audio clips; performing feature fusion on the mel spectrum graph and the time spectrum feature graph of each target device respectively; inputting the mel and time fusion features of each target device into a classifier, taking the machine ID of each target device as a category label, and training to obtain a target classification model; in the test, inputting the to-be-tested sound information into the obtained target classification model to obtain real-time feature sound indicators of each target device, and judging whether the target device is in an abnormal state according to the comparison result of the real-time feature sound indicators and a critical value.

The application discloses a GPU-based multi-client real-time spectrum monitoring system and a method thereof, and belongs to the technical field of spectrum analysis.The method comprises the following steps: acquiring the spectrum state, behavior information, host state and service quality requirement of a client; calculating the interference intensity coefficient, behavior state coefficient, host state coefficient and service quality coefficient of the client through a corresponding model; dynamically allocating the computing power of each client by using a power distribution model based on the above coefficients; and finally outputting the target refresh rate of each client by a refresh rate optimization model in combination with the allocated power and service quality coefficient.The application solves the problem that the resource allocation in the prior art is rigid and cannot adapt to the differentiated requirements of multi-clients by multi-dimensional state sensing and modeling, realizes intelligent, dynamic and efficient allocation of system computing resources, guarantees the service quality of multi-services, and improves the overall energy efficiency and stability of the system.

This application relates to the technical field of partial discharge (PD) detection, and in particular to a PD signal denoising method and system based on time-frequency domain coordination. The method includes: acquiring a prediction model built on an encoder-decoder framework and training it using a composite loss function; acquiring the PD signal and performing a time-frequency transformation to obtain the time spectrum of the noisy signal, the time spectrum of the noisy signal including the clean PD signal and noise components in the time spectrum; inputting the time spectrum of the noisy signal into the trained prediction model and outputting a corresponding prediction signal mask matrix, and multiplying the prediction signal mask matrix element-wise with the time spectrum of the noisy signal to generate the time spectrum of the denoised signal; and performing an inverse time-frequency transformation on the time spectrum of the denoised signal to obtain the denoised time-domain signal. This application aims to improve the noise suppression effect of PD signals in noisy environments.

The invention relates to a method and system for identifying submillimeter-level defects of a composite material laminated plate, and the method comprises the steps: carrying out the ultrasonic scanning of the composite material laminated plate, and collecting a reflection signal at each position on a scanning path; according to the defect identification target, selecting a corresponding reflection signal to carry out synchronous extrusion continuous wavelet transform, and obtaining a time-frequency spectrum of the reflection signal; performing double integration of time and frequency on components corresponding to each frequency at each moment from the defect echo front part to the bottom echo front part in the time-frequency spectrum of the reflected signal, and performing normalization operation to obtain normalized time-frequency energy; and identifying different types of defects of the composite laminated plate according to the characteristics of the normalized time-frequency energy. Compared with the prior art, the method has the advantages that the normalized time-frequency energy characteristics of the ultrasonic reflection signals are adopted, and detection and identification of different types of manufacturing defects in the composite material structure are completed.

This invention relates to the field of oil and gas geophysical exploration technology, and more specifically, to a time-domain compressed seismic spectral decomposition method based on matching pursuit. This method involves sparse decomposition of the seismic signal, specifically searching for the optimal matching wavelet, further deriving the reflection wavelet, and then performing Fourier transform and time-compression transform on the reflection wavelet to obtain its time-domain compressed spectrum. Finally, the time-domain compressed spectra of the reflection wavelet are superimposed and summed to improve the temporal resolution of the seismic record's time spectrum, avoid spurious spectral anomalies, and ensure the accuracy of the output results. Utilizing time-domain compression methods to improve the time-frequency resolution of data can highlight subtle features in the signal and their time-varying characteristics, improving the accuracy and reliability of signal processing. Applying this method to fields beyond seismic exploration can enhance signal feature recognition and improve signal detection capabilities.

The embodiment of the invention discloses a laser state monitoring method and system, and relates to the technical field of laser. The method comprises the following steps that an output optical signal of a laser is collected, a real-time spectrum curve of the optical signal is obtained, and the real-time spectrum curve comprises multiple pieces of characteristic spectrum information; comparing the characteristic spectrum information of the real-time spectrum curve with corresponding reference spectrum information in a pre-stored standard spectrum information base; according to the comparison result and in combination with a preset judgment condition, obtaining a state evaluation result of each to-be-detected module; and when the state evaluation result is abnormal, triggering early warning. Therefore, the spectral information is obtained by monitoring the optical signal in real time and is compared with the corresponding reference spectral information in the pre-stored standard spectral information path, and the health state and trend of each module to be detected in the laser can be found and early warning can be performed in time according to the change of the spectral peak intensity, the spectral peak wave band and the spectral peak bandwidth.

The invention belongs to the technical field of spectrum analysis instruments, and particularly relates to a method and device for rapidly processing spectrum data based on a real-time spectrum analyzer, and the method comprises the steps: carrying out the fast Fourier transform processing of IQ data, and obtaining serial spectrum data; converting the serial spectrum data into n segments of parallel spectrum data, wherein n is an integer greater than 1; performing parallel spectrum processing on the n segments of parallel spectrum data at the same time to obtain n segments of processed spectrum data; and outputting the processed spectrum data. According to the method, the post-processing process of the spectrum data can be accelerated on the premise that the length of single Fourier transform is not reduced and a system clock is not increased, so that the real-time spectrum analyzer can process more spectrum data in the same time.

This invention discloses a high-frequency signal monitoring method, device, and medium based on real-time spectrum analysis, relating to the field of signal quality control technology. The method includes: acquiring high-frequency signal data and performing time base correction and continuous sequential arrangement to generate high-frequency time-series data; using a filter topology reconstruction algorithm to reorganize the filter structure and reconfigure parameters of the spectral gradient data to obtain a filtered signal, and performing spectrum-driven filtering processing on the filtered signal to generate filtered monitoring data; extracting the target signal from the filtered monitoring data, combining the target signal with the filtered signal, and performing difference separation to generate decomposed signal data; continuously organizing and determining the status of anomaly identification data to obtain anomaly determination data, performing correlation analysis on the anomaly determination data, and generating a high-frequency signal monitoring report. This invention improves the reliability of signal separation and identification.

This invention discloses an integrated method and system for noise reduction and identification of ship targets under marine environmental noise interference. The method includes: performing a short-time Fourier transform on the radiated noise signal of the ship to be identified, which is affected by marine environmental noise, to obtain the corresponding time spectrum, and inputting it into a trained integrated ship target noise reduction and classification model to obtain the ship classification. The model includes: a deep noise reduction network module, which integrates an MP-SENet parallel decoder structure into DBSA-Net to reconstruct the amplitude spectrum and phase spectrum of the ship signal separately in the time and frequency domains; an inverse Fourier transform module, which performs an inverse Fourier transform on the amplitude spectrum and phase spectrum of the ship signal to obtain the noise-reduced ship signal to be identified in the time domain; and a multi-scale identification module, which performs multi-scale feature extraction, channel-aware compression-excitation, and hierarchical feature fusion on the noise-reduced ship signal to achieve ship classification.

The application discloses a multi-source heterogeneous data fusion management system based on a spectrum sensing detection device, a device anchoring unit configured to anchor a spectrum sensing device in a region to be sensed; a real-time feature acquisition unit configured to acquire real-time spectrum features of the spectrum sensing device; a real-time feature input unit configured to input the real-time spectrum features to a pre-constructed spectrum state recognition model; a state sequence output unit configured to output a spectrum state time sequence of each spectrum sensing device in the region to be sensed according to the spectrum state recognition model; and a spectrum device management unit configured to uniformly manage state data of multiple heterogeneous spectrum sensing devices according to the spectrum state time sequence of each spectrum sensing device. The application realizes uniform structural expression of devices with uneven spatial distribution and inconsistent frequency bands by combining geographical coordinate clustering and directionality edge building, so that the system has good heterogeneous adaptability and state fusion capability.

The invention relates to a method suitable for a real-time spectrum analyzer to realize time length flexible setting before and after triggering. The method comprises the following steps: waiting for Pre Trigger time length before entering a triggering state; the upper computer enters a receiving trigger state; after triggering, recording an address of triggering moment data in the DDR, and storing the address in a register of the FPGA; continuing to wait for the time length of the Post Trigger; and reading the DDR address at the triggering moment in the FPGA register, and obtaining data stored after triggering. The invention also relates to a device suitable for the real-time spectrum analyzer to realize the time length flexible setting before and after triggering, a processor and a computer readable storage medium thereof. By adopting the method and the device suitable for the real-time spectrum analyzer to realize the flexible setting of the duration before and after triggering, the processor and the computer readable storage medium thereof, the space size of the data stored during triggering can be flexibly set according to requirements, and the storage coverage of the triggering data can be flexibly carried out; and the stored data can be calculated and obtained more conveniently for data display.

The application discloses a parameter self-adaptive real-time spectrum analysis method and system, and belongs to the technical field of digital signal processing.The method comprises the following steps: performing down-sampling filtering on an IQ signal by using a cascaded FIR filter based on a down-sampling coefficient to obtain a down-sampled IQ signal; dividing the down-sampled IQ signal into a non-over-sampling rate signal or an over-sampling rate signal according to an actual sampling rate, performing overlapping segmentation processing to obtain a serial or parallel segmented time domain signal, applying a window function with an actual FFT length and performing FFT conversion to obtain a plurality of groups of frequency domain signals, calculating the amplitudes of the plurality of groups of frequency domain signals and performing average processing to obtain a frequency-synthesized signal; performing logarithmic conversion on the frequency-synthesized signal by using a segmented look-up table method to obtain a logarithm-processed frequency domain signal, and performing video filtering on the logarithm-processed frequency domain signal to obtain a real-time spectrum signal.The application solves the problems of small bandwidth, long data processing time and complex structure existing in the prior art real-time spectrum analysis technology.