429 results about "Coverage data" patented technology

A user equipment (UE) may access a public land mobile network (PLMN) via a communication satellite. The UE may receive satellite coverage data from the serving PLMN via the communication satellite indicating at which locations and / or at which times satellite coverage is available. The UE may determine, based on the satellite coverage data, a first time of satellite unavailability for a location of the UE and a second time of satellite availability. The UE may enter a no coverage state following the first time. The UE may inhibit mobile originating requests and / or reduce a frequency of satellite cell searching while in the no coverage state. The UE may leave the no coverage state at the second time. The satellite coverage data may comprise a coverage map for a grid of locations. Extra coverage data may be provided for satellite availability for other PLMNs and / or for terrestrial cells.

The invention relates to the technical field of landscape evaluation, and discloses an urban green land landscape evaluation method based on a large model, and the method comprises the steps: carrying out the real-time analysis and standardization processing of a streetscape image, IoT environment data, satellite vegetation coverage data and RTK high-precision positioning information of a target city district, extracting core landscape elements, building cross-scene semantic mapping, and carrying out the calculation of the cross-scene semantic mapping. Transmitting the standardized data to a central database and deploying edge nodes; extracting a multi-dimensional landscape index by adopting an improved semantic segmentation model, dynamically adjusting an index weight in combination with regional features and seasonal changes, and classifying and correcting deviation data by edge nodes; a visual evaluation result is generated based on a cooperative computing architecture and a digital twinborn model, a landscape space to be promoted is identified, and optimization suggestions are generated; and obtaining planner feedback information, updating the model, the weight rule and the suggestion generation strategy, and forming a closed loop iteration mechanism. According to the method, the dynamic and practical evaluation requirements of the current urban green land landscape can be met.

The invention relates to the technical field of intelligent operation inspection of power transmission lines, in particular to an intelligent detection method of a power grid frame and a satellite image processing system.The method comprises the steps that the power grid frame is detected according to collected multi-source monitoring information, information of a plurality of power grid structures is obtained, and a power grid topology model is established; performing connection analysis on the power grid topology model to obtain a power grid topology analysis result, and performing spatial distribution analysis on the power grid frame by collecting coordinate information of the power grid frame and the power grid topology analysis result to obtain power grid coverage data. And performing data visualization on the plurality of pieces of power grid structure information, the power grid topology analysis result and the power grid coverage data to generate a power grid frame detection result. According to the invention, the method effectively solves a problem that the weak edge region is liable to leak detection in the recognition of the power grid frame in a large-range and high-complexity scene, remarkably improves the recognition efficiency and accuracy of the power grid frame, and reduces the cost and time of manual inspection.

Disclosed are a three-dimensional laser point cloud-based method and system for in-depth vegetation management in transmission corridors. The method comprises: collecting multi-source data by means of an unmanned aerial vehicle- and helicopter-mounted multi-sensor system, and preprocessing the multi-source data; using the processed multi-source data to create a digitalized power grid corridor and construct a vegetation management analysis model; on the basis of data analyzed by the vegetation management analysis model and geofencing technology, designing a hazard vegetation clearing-cost-compensation prediction model; and developing a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows. The described method achieves high-precision and high-coverage data collection, precisely identifies transmission line digital twins and hazard vegetation, standardizes and automates the management of hazard vegetation clearing and related compensation, and develops a mobile application for analyzing hazard vegetation data flows and the closed-loop management of vegetation management service flows, thereby achieving the end-to-end digital and mobile management of vegetation management. The organic combination of steps comprehensively improves the intelligence and efficiency of transmission line vegetation management.

The invention discloses a depth space-time super-resolution mapping method fusing historical high-resolution land coverage data, and the method comprises the steps: constructing a depth space-time super-resolution mapping model structure which is formed by connecting a super-resolution adversarial network SRGAN structure and a MUNet network structure in series; mapping from low-resolution images to high-resolution land coverage category probabilities in combination with super-resolution reconstruction and semantic segmentation is carried out, a historical high-resolution land coverage category map of a target area is used as auxiliary information, and changed categories and unchanged categories are distinguished by comparing category information of the low-resolution images in a historical period and a target period; and a change detection-based sub-pixel category allocation optimization model fusing a historical high-resolution land cover classification map and a predicted high-resolution category probability map is constructed, so that the depth space-time super-resolution mapping of the low-resolution remote sensing image is completed. The method has the advantages of high simulation precision, stability and the like, and is suitable for earth surface information extraction work such as classification, land cover / utilization, change detection and the like when remote sensing images are insufficient.

The invention relates to the technical field of data mining, in particular to a customer behavior analysis method and system based on retail data, and the method comprises the following steps: extracting a numerical set of customer purchase frequency, single consumption amount and shopping duration based on a customer group behavior feature matrix, and calling matrix operation to analyze a multi-feature distribution condition item by item. According to the invention, through extracting the purchase frequency, the consumption amount and the shopping duration in the customer behaviors, combining the analysis of the offset value and the item-by-item comparison of the distribution interval, accurately identifying the abnormal characteristics, and analyzing the offset value in the multi-period frequency change trend, the period characteristics of the behavior change are clearer, and the frequency intersection analysis of the commodity combination purchase behaviors is more accurate. Reconstruction accuracy of feature distribution and hierarchical marking of regional customer coverage data are improved, the assessment ability of regional feature distribution and an offset ratio is refined, and the precision and practicability of the operations in feature distribution capture, multi-period behavior analysis and regional data comparison are improved.

The invention discloses a forest grass vegetation coverage refined inversion method and system based on multi-scale remote sensing, and the method comprises the steps: firstly providing end member types and end member abundance information through the field observation data of an unmanned plane, inputting the prior knowledge into a linear mixed pixel decomposition model, and carrying out the back calculation to obtain end member spectrum information of the corresponding end member types; by taking unmanned aerial vehicle over-resolution observation data as priori knowledge, the end member spectrum extraction precision is improved, and a technical support is provided for improving the inversion precision of the total coverage of forest and grass vegetation; the textural features of high-resolution satellite observation data are fully utilized, the spectral features of medium resolution and the textural features of high resolution are combined, and the optimal response relation between different remote sensing feature combinations and woody vegetation coverage measured data is constructed by utilizing a random forest model. Therefore, high-precision inversion of the woody vegetation coverage in a large-area range is realized, and data support is provided for further estimation of the herbaceous vegetation coverage.

The invention provides a soil erosion evaluation and prediction method based on a public data set, and belongs to the field of soil erosion. An improved RUSLE model is constructed by obtaining public land utilization data, rainfall data, vegetation coverage data, soil texture data and DEM data of a target area, and the soil erosion condition of the target area is evaluated; four prediction scenes including a natural development scene, a city extension suppression scene, an ecological priority scene and a cultivated land protection scene are put forward according to policies, adaptive atlas under the four scenes are made, existing land utilization data and the adaptive atlas of all the scenes are input into a CA-Markov model, and land utilization conditions under all the scenes in the future are obtained; and on the basis of the predicted land utilization data, a water and soil conservation measure factor under each scene is obtained in combination with an assignment method, and then an improved RUSLE model is constructed in the grid, so that the soil erosion condition of the target area under multiple scenes in the future can be obtained.

The invention discloses a flash store digital site selection management system and method based on multi-channel site selection, relates to the field of flash store site selection management, and provides a mapping tool for subsequent mapping classification of site selection influence factor data of a plurality of to-be-selected points of a flash store by constructing a final category mapping equation; through mapping, flash store to-be-selected points with annual net profit data not meeting requirements are eliminated; a 0-1 integer programming model is constructed according to the construction cost data, the coverage data and the annual net profit data corresponding to each quick-flash store primary selection point, so as to solve the quick-flash store site selection points where quick-flash stores are constructed. The construction cost is smaller than the preset construction cost threshold value, and the corresponding total coverage range data is larger than or equal to the preset coverage range threshold value, so that the screened quick-flash store site selection point can meet various requirements at the same time, and the net profit maximization is achieved.

The invention discloses a vegetation coverage prediction method and system based on multi-source data fusion, and relates to the technical field of vegetation coverage prediction, and the method comprises the following steps: obtaining historical vegetation coverage data and land utilization type data, and obtaining a matching time sequence of land utilization transformation and vegetation recovery lag; frequency band extraction is carried out on the matching time sequence based on Fourier transform, and the initial change frequency of the matching degree is obtained; performing random disturbance on the initial change frequency of the matching degree based on a preset first constraint condition to obtain a plurality of different matching degree change frequencies; obtaining vegetation coverage degradation influence data and ecological restoration potential data corresponding to different matching degree change frequencies, and constructing an evaluation model; and according to the data output by the evaluation model, the vegetation coverage prediction value of the preset first prediction model is corrected. The problem that the vegetation coverage prediction is inaccurate due to the fact that the relation fluctuation between the land utilization type conversion speed and the vegetation recovery lag degree is not considered is solved.

The invention relates to the field of industrial automation testing, discloses a multifunctional testing tool and method for an EUROMAP interface of an injection molding machine, and achieves the crossing from single function verification to system-level compliance evaluation. A test network is constructed through a controller, and deep compliance verification, coverage data interaction, exception handling and cross-layer response consistency are realized. The test tool integrates the functions of protocol analysis, signal simulation, fault injection and the like, supports dynamic generation of boundary value test scenes and abnormal injection of signal delay, CRC errors and the like, and verifies the time sequence / logic consistency of a protocol instruction and an execution mechanism response through a hardware-level timestamp alignment technology. According to the method, an evaluation report including compliance rating, fault diagnosis and optimization suggestions is automatically generated, the problems of single coverage dimension, insufficient abnormal simulation, cross-layer verification deficiency and the like of a traditional test are solved, the intelligence and standardization level of the injection molding machine interface test is remarkably improved, and the equipment interconnection and intercommunication performance is guaranteed.

The invention discloses an optimal signal selection intelligent network switching management method based on a super SIM multi-network, and relates to the field of intelligent network switching management, and the main scheme is as follows: analyzing and calculating operation state data to obtain a network delay score, a downloading rate score and a network stability score in a current network environment, and carrying out comprehensive analysis to obtain an optimal signal selection score; obtaining a current network state score; judging whether to execute a network switching instruction or not by evaluating the current network state score; the coverage degree data, the network state data and the switching cost data are analyzed and calculated to respectively obtain coverage matching degree indexes, network state indexes and switching cost-benefit ratios of different operators; switching priority indexes of different operators are obtained through comprehensive analysis, the switching priority is determined, and the problem that switching decisions and requirements are disjointed due to the fact that switching is triggered only based on signal strength or a single rate threshold and core indexes such as network delay and stability which affect user experience are not fused is solved.

The invention provides an individual credit risk assessment method, system and device based on federal learning, and belongs to the technical field of data processing. The method comprises the following steps: in response to a credit evaluation request from a credit evaluation node, determining one or more evaluation participation nodes of a credit evaluation object; generating a corresponding local credit evaluation instruction and sending the local credit evaluation instruction to each evaluation participation node; wherein the local credit assessment instruction comprises a desensitization identifier of the credit assessment object. And determining a global credit score of the credit assessment object based on the local credit score fed back by each assessment participation node and the weight set corresponding to the credit assessment request, and sending the global credit score to the credit assessment node. Through the method, the credit assessment coverage data is improved, and the credit assessment is carried out in time, so that the personal credit risk assessment is accurately completed.

The invention discloses an urban block green land planning method and device based on thermal environment big data, and the method comprises the steps: obtaining the thermal environment data, vegetation coverage data and a vegetation database of a target block; constructing a thermal environment map of the target block based on the thermal environment data, and obtaining a thermal environment feature vector of each sub-block according to the thermal environment map; acquiring a microclimate influence value of each vegetation type; performing correlation analysis on the thermal environment feature vector and the microclimate influence value of each sub-block to obtain the microclimate adjustment contribution degree of each vegetation type to each sub-block; and according to the microclimate adjustment contribution degree, determining a target vegetation type to be planted in each sub-block and a planting proportion of each target vegetation type, and obtaining a vegetation configuration scheme of the target block. The microclimate adjusting capability of the urban green land can be improved.

The invention provides a wind power data determination method and device based on geographical constraint attention. The wind power data determination method comprises the steps of obtaining topographic data, surface vegetation coverage data and wind power data; for the wind power data, combining the topographic data and the surface vegetation data, performing feature fusion through an adaptive attention mechanism to obtain a fusion feature containing multi-source information; performing shallow feature extraction, deep feature extraction and image up-sampling on the fusion features in sequence, wherein the deep feature extraction realizes multi-scale feature extraction and geographic constraint fusion through a multi-stage residual fusion module RFSTB, a wind field frequency domain decoupling module CFB and a geographic perception global attention module GAB; verifying the effectiveness of the model by using a peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM); and performing super-resolution reconstruction on the wind power data based on the trained model to generate wind power data with predetermined precision. The method provides support for fine evaluation and efficient development of wind energy resources in complex terrain areas.

The invention discloses a code coverage rate data exporting method and device, equipment and a medium, relates to the technical field of software testing, is applied to NVMe equipment, and comprises the steps that when a data exporting command sent by a target host is obtained, the data exporting command is analyzed to obtain address information of a memory buffer area pre-applied in the target host and the size of the buffer area; transmitting and writing pre-collected code coverage rate data into a memory buffer area; the code coverage rate data is data which is obtained by the NVMe equipment after establishing a one-way linked list for a target data structure used for storing code coverage rate information in each source file and counting executed code segments by using a preset instrumentation code and is added to the one-way linked list; and after writing of the code coverage rate data in the one-way linked list is completed, sending a target command used for representing that the execution of the writing operation is completed to the target host, so that the target host generates a coverage rate data file for the obtained code coverage rate data by using a local file system.

The invention discloses a method for reducing line loss and improving power supply reliability based on the Internet of Things, and the method comprises the steps: carrying out the full-dimension load and line state monitoring: deploying an Internet of Things sensor, constructing an end-to-end monitoring network, and collecting key parameters in real time; a low-delay and wide-coverage data channel is constructed; a layered communication architecture is adopted, and efficient data transmission is realized; the real-time analysis and intelligent decision engine is used for constructing a load-line-equipment three-dimensional data model based on a cloud end and an edge platform, and realizing line loss real-time diagnosis, load prediction, situation awareness and rapid fault positioning; dynamic optimization and self-healing control: based on a data layer analysis result, source-network-load coordinated regulation and control are realized through an Internet of Things terminal, line loss is reduced, and stable power supply is guaranteed; user interaction and global optimization: realizing power grid-user interaction and peak load shifting through a user-side Internet of Things terminal to reduce the pressure of a power system; the system has the advantages of being capable of reducing line loss, guaranteeing power supply reliability, having various functions and being good in comprehensive application effect.

The invention relates to a landscaping coverage rate data statistical method. Comprising the steps of generating independent greening coverage rate data of each layer based on vegetation multi-dimensional stratified analysis of ecological characteristics, and obtaining an overall greening coverage rate through a superposition algorithm; calculating a dynamic coverage rate based on plant spectral characteristics, then calculating a green coverage rate actually participating in an ecological function based on an evaluation result, and adjusting coverage rate data in real time according to a plant health state to obtain the dynamic coverage rate; correcting coverage rate data by analyzing soil humidity and air temperature environmental factors in different time periods based on ecological influence weights of spatial and temporal changes; the ecological functional indexes are associated with the greening coverage rate; according to the method, the vegetation coverage rate and real-time environment data are coupled to construct a statistical model, and then adjusted data are combined with multispectral remote sensing data to form coverage rate evaluation; by introducing an ecological compensation mechanism, the dynamic process of landscaping change is simulated.

The invention relates to the technical field of geological engineering, in particular to a comprehensive evaluation method and system for a mine geological environment, and the method comprises the following steps: collecting mine surface geological data based on a target mine region, collecting vegetation types and vegetation coverage area data, evaluating the occurrence probability of landslide and land subsidence of a plurality of regions of a mine, and obtaining a comprehensive evaluation result; and obtaining a surface risk assessment result. According to the method, the accuracy and the reliability of risk assessment are improved by integrating the surface geological data and the vegetation coverage data and combining meteorological information and topographic analysis, the judgment for geological stability is enhanced by combining fixed-point assessment and stratigraphic analysis, so that early warning for a hidden danger area is more timely and specific, and the risk assessment accuracy and the risk assessment reliability are improved. By monitoring surface deformation in real time and combining long-term data analysis, a more dynamic and continuous geological environment change trend is provided, mining strategies and disaster prevention and reduction measures can be adjusted in time, and the environmental influence of mining activities is reduced.

The invention relates to a lake remote sensing image algae bloom coverage degree inversion method based on machine learning. Compared with the prior art, the defect that algae bloom coverage degree inversion is difficult to carry out on lake remote sensing images is overcome. The method comprises the following steps: acquiring and preprocessing a remote sensing data source; carrying out algae bloom extraction and processing on the Sentinel-2 multispectral image to obtain a coverage sample set; performing feature selection and combination on the MODIS multispectral image to obtain a feature index data set; constructing a lake algae bloom coverage degree data set; constructing a model and training based on machine learning; and obtaining an inversion result of the algae bloom coverage. According to the method, the algae bloom coverage degree of the MODIS pixel scale can be obtained by utilizing Sentinel-2 data with high spatial resolution, and the quantitative estimation model is constructed by combining MODIS spectral characteristics, so that high-temporal-spatial-precision monitoring and change analysis of the lake algae bloom are realized.

The invention provides a discrete cosine transform-based atmospheric pollutant full-coverage data fusion method, which comprises the following steps of: 1, carrying out data preparation and preprocessing, namely adjusting model data and satellite observation data to the same precision by using an area weighted aggregation method, ensuring the consistency of the model data and the satellite observation data in geographic space, and carrying out data fusion on the model data and the satellite observation data; subsequent data fusion processing is facilitated; step 2, performing data fusion processing: performing filling and smoothing operation on three-dimensional space-time proportion parameters of the satellite observation data and the model data based on a space-time discrete cosine transform (DCT) smoothing principle; and step 3, performing correlation analysis on the atmospheric pollutant seamless full-coverage data and the ground station observation data obtained after fusion, and performing advantage and disadvantage judgment on a correlation analysis result according to a set judgment threshold. According to the invention, the model data can be effectively calibrated and the deficiency of satellite observation can be supplemented.

The invention belongs to the field of remote sensing technology and environment monitoring, and particularly relates to a three gorges reservoir area ground surface settlement monitoring method, system and device based on multi-source remote sensing data, and the method comprises the steps: obtaining a plurality of single-view complex images and corresponding image surface coverage data in a period of time, and carrying out the preprocessing of the image data; carrying out SBAS-InSAR processing on the preprocessed image data, wherein the SBAS-InSAR processing comprises carrying out interference, inversion and geocoding processing on the image; fusing the coded data by adopting a multi-source remote sensing data fusion technology; inputting the fused image into a pre-trained machine learning model to obtain a three gorges reservoir area ground surface settlement monitoring result; by introducing multi-source remote sensing data fusion and an advanced machine learning model, the problems of single data source, difference in temporal-spatial resolution, poor model adaptability and the like in the prior art are solved, and the precision, the stability and the robustness of ground surface settlement monitoring and evaluation are remarkably improved.

The invention relates to the technical field of bridge detection and data processing, in particular to a bridge appearance detection method and system based on cooperation of an unmanned aerial vehicle and a workstation. The technical problems are as follows: the modern bridge detection technology is complex in bridge environment, most of the existing unmanned aerial vehicle detection schemes depend on manual control flight, the flight difficulty is high, and high-definition pictures are difficult to shoot, so that a large amount of rework is caused; according to the technical scheme, the bridge appearance detection system based on cooperation of the unmanned aerial vehicle and the work station comprises an unmanned aerial vehicle platform and a ground work station; real-time data acquisition is performed on the bridge and the surrounding environment thereof through the unmanned aerial vehicle, a bridge digital map is produced by integrating a multi-sensor fusion technology and a three-dimensional modeling and deep learning technology, and a fine acquisition path is planned; and automatic, high-precision and high-coverage data acquisition of the bridge appearance in a complex environment is completed, so that the precision and efficiency of disease detection are greatly improved.

The invention relates to the field of code instrumentation, and discloses a method for analyzing front-end page coverage rate based on code instrumentation, which is used for solving the problem that part of coverage rate data cannot be acquired when code analysis is performed through code instrumentation, and comprises the following steps: adding an instrumentation configuration plug-in in a front-end project, the method comprises the following steps: deploying a front-end project containing an instrumentation code into a test environment, executing a function test of a front-end page in the test environment, recording each executed code path, regularly reporting collected coverage rate data, receiving and processing the coverage rate data from the front end, and storing the processed data in a database. According to the method, the coverage rate report is generated and checked, test case optimization is carried out for uncovered code paths, the test is executed again, the optimized coverage rate data is reported again, and a new coverage rate report is formed through data processing and report generation, so that the accurate test range is effectively narrowed, and the difficulty in analyzing the coverage rate data is reduced.

A user equipment (UE) accesses a public land mobile network (PLMN) and obtains an address and authorization data of a server. The UE transmits a request for coverage data to the server based on the address and includes the authorization data. Upon verifying the authorization data, the server transmits the coverage data to the UE, the coverage data enabling the UE to determine whether access to the PLMN is possible at one or more locations of the UE, at one or more future times, and using one or more radio access technologies (RATs), the one or more radio access technologies (RATs) include a non-terrestrial network (NTN) RAT and / or a terrestrial network (TN) RAT. The authorization data may include a temporary identifier of the UE, which may be encrypted and may enable verification of the authorization data. The request for coverage data may indicate the signal level required for the UE to access the NTN and TN RAT as well as the location and the future time.

The invention relates to the technical field of carbon sink data processing, in particular to a tamarix chinensis forest wetland ecosystem carbon sink data analysis and processing system and method. The system comprises a carbon sink data calculation module, a data proofreading information acquisition platform and a data analysis proofreading module. Wherein the carbon sink data calculation module determines carbon sink resource data corresponding to a tamarix chinensis forest in a target area based on tamarix chinensis coverage data and pre-input matched carbon sink resource parameters of tamarix chinensis in different types of land; according to the method, an affected area of a tamarix chinensis forest is identified, the affected area is divided into a plurality of areas, the areas are matched with various environments in a city, an abnormal damage database is constructed by utilizing a portable monitoring environment of the city, and the short plate of which a broken branch is difficult to identify through remote sensing monitoring is supplemented through damage information recorded in the abnormal damage database. Therefore, proofreading of the carbon sink resource data is completed, and accuracy of remote sensing monitoring is improved.

The invention discloses an SAR (Synthetic Aperture Radar) image overlay detection method, device and equipment and a storage medium. The method comprises the following steps: acquiring SAR time sequence image data, external DEM (Digital Elevation Model) data and external earth surface classification coverage data of a current detection area; obtaining a region amplitude diagram of the current detection region according to the SAR time sequence image data, and carrying out normalization processing on the region amplitude diagram; obtaining a corresponding interferometric phase diagram according to the SAR time sequence image data, and calculating a coherence coefficient; performing normalization processing on the external DEM data, performing wave band splicing on the normalized region amplitude diagram, interference phase diagram and coherence coefficient, and the normalized external DEM data and external earth surface classification coverage data to generate a corresponding data block, and inputting the data block into a preset overlay identification model, and obtaining an overlay identification result of the current detection area. The SAR overlay recognition method can improve the overlay recognition precision of the SAR.

The invention relates to the technical field of electronic design automation, in particular to a coverage rate-based index database management method, coverage rate-based index database management equipment and a storage medium. Meanwhile, extracting design level information in the coverage rate database to establish an index database consisting of a plurality of index tables; when the visual query window initiates a query request, the index database queries the identifier of the target object according to the query request, and processes and updates the target window of the coverage rate visual system according to the identifier in the coverage rate data structure, so that the search efficiency is greatly improved, and the search time is shortened. And the purpose of real-time searching is achieved.

The invention discloses a cloud management and intelligent distribution method for classroom teaching resources, and relates to the technical field of cloud computing software. The classroom teaching resource cloud management and intelligent distribution method comprises the steps that S1, multi-source teaching resource data and auxiliary distribution data are acquired and preprocessed, and a knowledge content database is constructed; s2, performing semantic fusion analysis through text semantic association, structural consistency and professional term coverage data; s3, carrying out dependency evaluation by fusing pre-association, knowledge co-occurrence, sequence association and semantic fusion data; s4, performing resource adaptability distribution analysis through the maximum knowledge dependence value in combination with the learning state data and the historical feedback data; and S5, evaluating the teaching resource quality by collecting classroom feedback, teacher evaluation and resource use data, and implementing content optimization. The problems of semantic distortion, path breakage, distribution mismatching and resource updating inconsistency in the teaching resource data fusion and intelligent resource distribution process are solved.

The embodiment of the invention provides a coal field fire area gas and particulate matter monitoring method, device and equipment and a storage medium, and the method comprises the steps: carrying out the temperature inversion of thermal infrared remote sensing image data based on a preset temperature inversion algorithm, and obtaining a temperature abnormal region; based on a pre-constructed hyperspectral detection model, performing inversion on the soil moisture data, the chlorophyll content data and the vegetation coverage data to obtain a surface vegetation growth condition; based on the temperature abnormal area and the earth surface vegetation generation condition, a preset judgment model is adopted for judgment, and the fire combustion stage is determined; based on the acquired atmospheric monitoring data, determining a two-dimensional distribution condition of gas and particulate matters in each fire combustion stage; and based on the two-dimensional distribution condition, performing overlay analysis on the atmospheric monitoring data at different heights in the temperature abnormal region to obtain the three-dimensional distribution condition of the gas and the particulate matters. Therefore, the accurate judgment of the combustion condition of the fire area is improved, and the accuracy and reliability of gas and particulate matter monitoring are improved.