9659 results about "Wavelength band" patented technology

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

The invention relates to the technical field of defect detection, and discloses an integrated circuit mask plate defect multispectral cooperative detection method, which comprises the following steps: carrying out preliminary scanning on a mask plate by using a multispectral camera, selecting an optimal wave band combination, switching to a target wave band through a liquid crystal adjustable optical filter, and obtaining a high-confidence multispectral image; the optical path offset is calculated through the linear relation, the deflection angle of the micro-mirror is dynamically adjusted, and images are collected for the second time after imaging offset is compensated; image filtering and registration are completed in the FPGA, and a high-contrast fusion image is generated; if the confidence coefficient is insufficient, triggering a wave band reselection mechanism to recheck; dynamically optimizing a defect type-spectrum mapping table based on real-time detection data; when the environment fluctuation exceeds the limit, an anti-interference mode is automatically switched, and micro-mirror calibration is triggered; and monitoring performance degradation of the optical assembly in real time, switching redundant optical paths, cleaning damaged parts, triggering manual reinspection when conflicts occur, and finally generating a visual report. According to the invention, the detection accuracy of the integrated circuit mask plate can be improved.

The invention belongs to the technical field of industrial measurement, and discloses a pump machine metal shell size detection method based on image analysis, which comprises the following steps: synchronously acquiring image data of a pump machine metal shell in a visible light wave band and a near-infrared wave band; the contrast ratio of image data is optimized through a dynamic exposure control technology, and a high-dynamic-range multispectral image is obtained; three-dimensional geometric information of the pump machine metal shell is obtained, registration fusion is carried out on the three-dimensional geometric information and the high-dynamic-range multispectral image, a dense three-dimensional point cloud model with multispectral textures is constructed, and a multi-frequency heterodyne algorithm is adopted to process a high-reflection area in the dense three-dimensional point cloud model; the method comprises the following steps: collecting a multi-angle reflection image of a pump machine metal shell by rotating a linear polarizer, calculating a Stokes vector to extract a diffuse reflection component, carrying out diffuse reflection component constraint and brightness suppression on a high-reflection area based on a CIE-Lab color space, and outputting a dense three-dimensional point cloud model after texture enhancement; and the size detection precision of the pump machine metal shell is improved.

Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

The invention discloses a PCB three-proofing coating quality detection method and system. The method comprises the steps that a visible light reflection image of the surface of a PCB and scattering spectrum data of a near-infrared band are acquired; performing spatial filtering processing on the visible light reflection image to extract interface area pixels, calculating coating layer thickness gradient distribution based on Mie scattering characteristics in scattering spectrum data, and fusing to generate an edge gradient distribution map; and according to the gradient amplitude change rate in the edge gradient distribution map, segmenting the effective coverage area of the coating layer by adopting a self-adaptive dynamic threshold algorithm, and extracting curvature extreme point density and boundary fractal dimension parameters at the segmentation boundary. According to the invention, three technical barriers of difficult microdefect capture, unmeasurable interface performance and delayed failure risk in traditional detection are solved, and two-dimensional accurate diagnosis of the structural compactness and interface reliability of the coating layer is realized.

Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

The invention relates to a multi-source remote sensing image classification method based on a key band retrieval attention mechanism, and belongs to the technical field of remote sensing image processing. The method comprises the steps of firstly performing preprocessing and data set division on multi-source remote sensing data, then constructing a key band retrieval attention network, performing training and evaluation on a model by utilizing a training set and a verification set after division, and finally performing visual analysis on a model result by utilizing a test set. According to the method, the features of the hyperspectral image and the laser radar / synthetic aperture radar image are effectively extracted and fused, redundant information interference is effectively reduced, the retention rate of hyperspectral key information is improved, the complementary expression ability among multi-source data is enhanced, and the remote sensing image classification precision and calculation efficiency are remarkably improved. The method is suitable for application scenes of multi-source remote sensing data fusion and classification, can meet efficient intelligent processing requirements of complex earth surface information, and provides an accurate and efficient remote sensing image classification solution.

Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

Systems and methods are provided for a digital beamformed phased array feed. The system may include a radome configured to allow electromagnetic waves to propagate; a multi-band software defined antenna array tile; a power and clock management subsystem configured to manage power and time of operation; a thermal management subsystem configured to dissipate heat generated by the multi-band software defined antenna array tile; and an enclosure assembly. The multi-band software defined antenna array tile may include a plurality of coupled dipole array antenna elements; a plurality of frequency converters; and a plurality of digital beamformers.

The invention relates to the technical field of semiconductors, and particularly discloses a multiband LED chip and a preparation method thereof, and a white light LED light source, the LED chip comprises a light-emitting layer, and the light-emitting layer comprises a first cyan light multi-quantum well layer, a second long-wave blue light multi-quantum well layer, a third short-wave blue light multi-quantum well layer and a fourth purple light multi-quantum well layer which are sequentially stacked in the epitaxial direction; the first cyan light multi-quantum well layer, the second long-wave blue light multi-quantum well layer, the third short-wave blue light multi-quantum well layer and the fourth purple light multi-quantum well layer are each of a periodic structure formed by alternately stacking well layers and barrier layers, and the light-emitting wavelengths of the first cyan light multi-quantum well layer, the second long-wave blue light multi-quantum well layer, the third short-wave blue light multi-quantum well layer and the fourth purple light multi-quantum well layer are sequentially decreased. And the forbidden band widths of the barrier layers are sequentially decreased. According to the LED chip, the overall excitation efficiency can be effectively improved, and the output full-spectrum white light LED light source is higher in color rendering index, better in stability, less in damage to human eyes and high in light emitting efficiency.

The invention discloses a high-temperature airflow two-dimensional temperature field and velocity field synchronous measurement system and method based on laser-induced phosphorescence, and the system comprises phosphorescence particles, a low-frequency double-pulse laser, an image collection device, a synchronous controller, and an image processing device. The image processing device analyzes the gray intensity ratio of different wave band images acquired by the two PIV cameras at the same time, and combines a temperature response function calibrated by an experiment to realize inversion of a temperature field; meanwhile, a double-frame time-resolved image acquired by any PIV camera is subjected to cross-correlation calculation, particle displacement is extracted, and then a velocity field is reconstructed. According to the invention, synchronous acquisition of temperature and speed based on the same data source is realized, and the system has the remarkable advantages of simple structure, high measurement precision and wide application environment.

The invention discloses an epilepsy electroencephalogram signal recognition method, system and device and a storage medium, and the method comprises the steps: obtaining an electroencephalogram signal, and calculating phase synchronization information between different channels according to the electroencephalogram signal as an edge feature; filtering the electroencephalogram signals, respectively calculating and extracting time domain features, dynamic features and phase amplitude coupling features corresponding to different wavebands of each channel, and fusing the features into node features; and integrating the obtained edge features and node features into a graph, performing classification by using a graph attention network model, and performing judgment and recognition on the electroencephalogram signals according to a classification result. The invention provides a classification method which is high in performance, has priori interpretability and is used for epilepsy signal identification and prediction, not only considers an interaction relationship among different channels, but also pays attention to and extracts deep features of time domain, dynamics, cross-frequency coupling and the like of nodes, and is more accurate in classification of electroencephalogram signals in different periods.

The invention relates to the technical field of image processing, and discloses a precast concrete T-beam bubble detection method and system. The method comprises the following steps: carrying out multiband reflection data acquisition and calibration on the surface of a T-beam, and obtaining optimized data through light intensity compensation and band screening; performing spatial registration and noise reduction processing on the data, and fusing the data into an image; edge detection and image segmentation are carried out, and pore morphological features are extracted; calculating depth data and performing spatial reconstruction to obtain pore distribution data; performing statistical analysis on the distribution data to obtain a defect evaluation result; and finally generating a detection report. According to the method, accurate positioning and quantitative evaluation of bubble defects are realized through multi-source data fusion and deep analysis, a complete early warning and evaluation mechanism is established, and the reliability and practicability of a detection result are improved.

The invention provides an intelligent real-time detection method and system for PCCP welding quality, and relates to the technical field of online detection and intelligent evaluation of pipeline welding quality through machine learning. Light energy data and multi-light-source images of a spiral weld pool are collected, exposure parameters are dynamically adjusted through the energy difference of visible light near-infrared bands, and the real-time detection of the PCCP welding quality is achieved. Inhibiting strong light interference and generating a weld surface image; a stress concentration area is positioned by scanning a welding seam thermal deformation area and combining speckle pattern change, sound frequency change and the elastic characteristic of the thin-wall steel cylinder; inputting the surface image and the deformation data into a space-time convolutional neural network, fusing light energy change, image details and spatial features to construct a weld joint space structure diagram, and adaptively correcting the position of a sensor; and comparing the sinking depth of the three-dimensional point cloud reconstruction, analyzing the correlation between the sinking degree and the stress, and generating a probability thermodynamic diagram to output the pressure-bearing failure risk level, so that the probabilistic early warning of the pressure-bearing failure risk can be realized.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

The invention provides a multi-layer optical film detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a polarization phase difference signal of transmission light and reflected light through multi-angle incidence irradiation of a multi-band programmable laser source, and recording the polarization ellipticity parameter change; then combining confocal microscopic interference and polarization analysis to determine a space coordinate and a stress distribution diagram of a stress abnormal region; continuously irradiating in a photo-thermal coupling environment through a broadband light source, and monitoring interference signals, polarization states and temperature changes to obtain dynamic evolution characteristics; and finally, performing a simulation test on the multi-layer optical film in a system environment with preset optical axis arrangement and light beam energy distribution, obtaining an in-situ polarization state and interference pattern data, and evaluating the optical performance of the film layer. According to the invention, potential defects can be positioned under high resolution, and the reliability and imaging quality of the film layer under complex application conditions can be accurately evaluated.

The invention relates to the technical field of chemical material detection, and discloses an AI-based chemical material detection information library retrieval and standard spectrum comparison system. The system comprises a data acquisition module, a spectrum preprocessing module, a feature extraction module, a retrieval matching module and a dynamic comparison module. The data acquisition module uses a multispectral sensor to acquire real-time spectral data covering ultraviolet, visible light and infrared bands, and the real-time spectral data is denoised by the spectrum preprocessing module. The feature extraction module adopts a deep residual convolutional network to extract spectral fingerprint feature vectors. The retrieval matching module inputs the feature vectors into a graph structure retrieval model to screen candidate standard spectrums. And the dynamic comparison module compares the candidate standard spectrums in multiple dimensions based on an improved quantum particle swarm optimization algorithm to obtain an optimal result. The system can accurately acquire and process spectral data, quickly retrieve and accurately compare standard spectrums, improve the efficiency and accuracy of chemical material detection, and have wide application prospects in the fields of chemical material research, production quality control and the like.

The invention relates to the technical field of photovoltaic power station operation and maintenance, and discloses a photovoltaic power station intelligent operation and maintenance and fault early warning system. A multi-band image fusion algorithm and a temperature gradient enhancement algorithm are applied to identify composite defects; establishing a defect interaction influence model to analyze the synergistic effect of different defects; constructing a defect evolution prediction algorithm to predict a defect development trend; and developing a defect-performance mapping model and a processing priority algorithm to optimize a maintenance decision. According to the method, the defect detection sensitivity and accuracy are improved, accurate identification and evolution prediction of composite defects are realized, a prospective maintenance decision is effectively supported, the operation and maintenance efficiency of a photovoltaic power station is remarkably improved, the service life of the power station is prolonged, and the safety risk is reduced.

The invention provides a water body monitoring method and equipment based on satellite remote sensing data and a storage medium, and relates to the technical field of water body monitoring, the method comprises the following steps: firstly, acquiring remote sensing image data and water quality measured data (including water quality parameters and geographic positions) of a target area at least covering two spectral bands; performing radiation correction on the remote sensing image data, and extracting target image data corresponding to the water body by using the image segmentation model; matching the water quality parameters with the target remote sensing image data according to the geographic position information, and constructing a training set of time-space alignment; using a multilayer model fusion strategy to train a preset water quality inversion model based on the training set to obtain a target model; and finally, inputting an image to be detected to the target model to obtain water quality parameters, and mapping the water quality parameters to a geographic space to generate a water quality two-dimensional distribution diagram. According to the invention, the water body monitoring precision and monitoring details based on satellite remote sensing data can be effectively improved.

The invention provides an urban green land coverage rate dynamic monitoring method and device. The method comprises the following steps: acquiring multispectral satellite remote sensing data, and preprocessing the multispectral satellite remote sensing data; carrying out cloud detection and shadow detection on the preprocessed remote sensing image, and calculating an effective pixel proportion index, a spectrum quality index, a time phase matching degree index and a spatial integrity index; based on the four indexes and the corresponding weights, constructing a fuzzy comprehensive evaluation matrix, and selecting an optimal data combination; calculating a normalized difference vegetation index (NDVI) based on the near infrared band and the red light band in the optimal data combination, and performing illumination angle correction and time sequence smoothing processing on the NDVI to obtain an optimized NDVI; performing green space identification on the optimized NDVI by adopting a first deep learning model, and outputting a green space distribution map; and calculating a green land area change rate, an NDVI difference value and connectivity index change based on the green land distribution map. According to the invention, high-precision dynamic monitoring of the green land coverage rate is realized, and the monitoring efficiency and accuracy are improved.

The invention relates to a method for improving satellite data inversion soil salinity by using hyperspectral data of an unmanned aerial vehicle. The method comprises the following steps: collecting a soil sample in a test area, and calculating soil salt content; acquiring and preprocessing satellite remote sensing image data of a test area; storing data, outputting a single-band satellite image, and calculating a spectral index; obtaining and preprocessing hyperspectral image data of the unmanned aerial vehicle in the test area; reconstructing and upscaling unmanned aerial vehicle image data pixels to a spatial resolution of 10m; reconstructing the narrow-band spectral data of the unmanned aerial vehicle into broadband spectral data consistent with a satellite image, storing and outputting a single band, and calculating a spectral index; screening a soil salt content characteristic variable, and constructing a soil salt content inversion model by taking the screened characteristic variable as an independent variable and the actually measured soil salt content as a dependent variable; and fitting a function relationship between the upscaling unmanned aerial vehicle soil salt content inversion model and the satellite inversion model by adopting a numerical regression function method to realize correction of the satellite inversion model.

The invention relates to the technical field of image processing, in particular to a road area intelligent extraction system for unmanned aerial vehicle aerial images, and the system comprises an image noise filtering module which analyzes and inputs frequency domain information of the unmanned aerial vehicle aerial images, recognizes a target noise mode, and filters the unmanned aerial vehicle aerial images. According to the method, frequency domain noise analysis and notch filtering are combined, the sharpness of the road edge is reserved, meanwhile, high-frequency noise and periodic interference are restrained, and the visibility of a low-contrast road area is improved; local pixel spectrum dispersion calculation is introduced and is dynamically fused with morphological parameters, and direction consistency and length-width ratio constraints are combined, so that the connectivity perception capability of the fragmented non-paved road is enhanced, and the fracture misjudgment under the form-changeable terrain is reduced; a normalized vegetation index and multi-band reflectivity are combined for modeling, water reflection interference is eliminated through a blue light band standard deviation, and a space mask of a non-road surface feature interference area is constructed.

The invention discloses a method and system for improving satellite remote sensing inversion precision by fusing hyperspectral proximity, and belongs to the technical field of water environment and water ecology monitoring. Acquiring a water body hyperspectral near-sensing reflectivity spectral data set, a satellite remote sensing surface reflectance data set and a water environment actual measurement data set, and performing preprocessing; a synchronous data set containing # imgabs0 # and water quality and a synchronous data set containing # imgabs1 # and water quality are constructed; based on the synchronous data of the # imgabs2 # and the water quality, synchronous data of the # imgabs3 # and the water quality are generated, and synchronous data of the # imgabs4 # and the # imgabs5 # are obtained; the # imgabs6 # is normalized to obtain # imgabs7 #, and the quantitative conversion relation between the # imgabs7 # and the # imgabs8 # is constructed band by band to eliminate errors; generating a fused satellite-ground data set, and constructing a training water quality parameter inversion model; and updating corresponding methods and data sets in real time by repeating specified steps, and optimizing the model. According to the method, the inversion result is displayed and downloaded according to the query instruction, the satellite spectrum precision and the satellite-ground synchronous data set scale are improved, and the method is used for high-precision water environment monitoring.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor defect detection method and system based on deep learning. The method comprises the following steps: step 1, multiband cooperative imaging and dynamic scanning control; 2, performing multi-scale feature fusion and defect identification; 3, dynamic threshold judgment and multi-scale feature fusion network optimization: dynamically adjusting a judgment threshold according to probability distribution characteristics of a current batch defect thermodynamic diagram, applying an offset to the threshold in combination with a wafer process type, and determining the offset according to a balance relationship between a false drop rate and an omission rate in historical data; critical samples and misjudgment samples with classification confidence close to a threshold value in historical detection are periodically screened, incremental learning is performed on the last layer of the multi-scale feature fusion network, and upstream network weight is frozen to prevent feature drift. Through fine image processing, multi-band information fusion, dynamic threshold adjustment, incremental learning and other mechanisms, the defect detection efficiency and accuracy in the semiconductor manufacturing process can be effectively improved, and the detection performance is continuously optimized.

The invention provides a space-time decoupling feature fusion method based on space-time bidirectional Vision Mama, and the method comprises the steps: S1, extracting multi-scale space detail features from an input time sequence image through employing a plurality of cascaded 2D CNN blocks, and obtaining a feature graph; s2, a bidirectional space-time Mama structure is applied to the feature maps of the low and middle layers so as to compensate for loss of time information in the 2D CNN block; wherein for the feature map obtained in the step S1, time sequence features are extracted by applying time bidirectional Vision Mama, and then space features are extracted by using space bidirectional Vision Mama. The invention further provides a space-time decoupling feature fusion system based on the space-time bidirectional Vision Mama. According to the method, efficient and accurate feature extraction and information fusion are performed on a special time sequence multi-band multi-channel remote sensing image, and efficient and high-precision semantic segmentation of crops on a large-area scale remote sensing image is realized.

The invention provides a simultaneous multiband superheterodyne receiver based on Rydberg atoms, and relates to the technical field of microwave electric field measurement, probe light provided by a probe light laser and coupling light provided by a coupling light laser oppositely enter an alkali metal atom gas chamber; the microwave signal sources of the multiple wave bands generate two paths of signals, one path of microwave signal is emitted to the space through the local oscillator microwave electric field emission horns of the wave bands, and the microwave electric field receiving horns of the wave bands receive corresponding echo signals. After frequency mixing of each wave band, the microwave electric field emission horn irradiates the alkali metal atom gas chamber at the same time, microwave fields of multiple wave bands are sequentially coupled with multiple pairs of continuous adjacent Rydberg states, and superheterodyne receiving is carried out on microwaves of multiple wave bands at the same time; and superheterodyne and high-sensitivity detection of microwave signals of multiple wave bands can be realized.

The invention relates to the technical field of fire safety monitoring, in particular to a firework identification system and method based on multi-modal fusion perception, and the core of the scheme is a visible light, multispectral and temperature three-modal framework: feature extraction optimization of each modal, improved YOLOv8s for visible light branches, dynamic background modeling and flame color screening, and multi-modal fusion perception. False positive is rejected by a multispectral branch depending on a waveband ratio and an index, an error compensation algorithm is introduced into a thermopile branch, and finally, a final smoke and fire area and the confidence coefficient thereof are determined by associating a three-mode area through collaborative decision. According to the scheme, the complex environment adaptability and the recognition reliability can be improved, the false alarm risk is reduced, the extremely-early smoke and fire detection capability is enhanced, good real-time performance and deployment flexibility are achieved, and the method is suitable for various types of fire safety monitoring scenes.