Satellite and sensor data integrated acquisition and analysis method and system

Abstract

The application discloses a satellite and sensor data integrated acquisition and analysis method and system, which is applied to the technical field of data processing and has the method which comprises the following steps: acquiring comprehensive data, wherein the comprehensive data comprises satellite remote sensing data, sensor data, land use data and digital elevation model data; respectively pre-processing the satellite remote sensing data and the sensor data to obtain daily average values of land surface temperature and sensor land surface temperature; respectively extracting features of the land surface temperature, the land use data and the digital elevation model data, and then performing feature integration to generate a feature matrix; taking the daily average values of the sensor land surface temperature as true value labels, screening the feature matrix to obtain an effective data set; constructing an integrated learning model according to the effective data set; acquiring a target feature matrix; inputting the target feature matrix into the integrated learning model to perform prediction, outputting a land surface temperature prediction value of a target region, and completing monitoring of the satellite and sensor data of the target region.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a method and system for integrated acquisition and analysis of satellite and sensor data. Background Technology

[0002] Satellite monitoring is the process of remote sensing and data acquisition using satellites. Satellites typically carry various sensors, such as optical sensors, radar, and infrared sensors. These sensors collect data from space and can be used to monitor weather, oceans, geology, environmental changes, vegetation cover, and more. Sensor monitoring, on the other hand, uses sensor devices to capture various data from the environment. Sensors can be embedded in various devices and systems to monitor environmental factors such as weather conditions, water quality, air quality, and traffic flow. Sensor data can be transmitted in real time via internet-connected devices for real-time monitoring and decision support. Sensor monitoring has wide applications in smart cities, health monitoring, and traffic management. These monitoring methods play a crucial role in scientific research, resource management, and decision-making.

[0003] While individual satellite remote sensing technology or sensor network technology each have their own advantages and limitations, the fusion of large-scale monitoring data and high-resolution data, as well as the fusion of coarse-grained data and fine-grained data, to improve the accuracy and resolution of monitoring data is particularly important for environmental protection and governance, health and safety assurance, environmental policy formulation, natural disaster early warning, resource management and sustainable development.

[0004] To overcome these shortcomings, this application proposes a method and system for integrated acquisition and analysis of satellite and sensor data. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for integrated acquisition and analysis of satellite and sensor data, which aims to solve the above-mentioned problems.

[0006] To achieve the above objectives, this application provides the following technical solution:

[0007] In a first aspect, this application provides an integrated acquisition and analysis method for satellite and sensor data, including:

[0008] Acquire comprehensive data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data;

[0009] The satellite remote sensing data and the sensor data are preprocessed to obtain the daily average values ​​of land surface temperature and sensor land surface temperature.

[0010] After extracting features from the surface temperature, the land use data, and the digital elevation model data respectively, feature integration is performed to generate a feature matrix;

[0011] Using the daily average surface temperature value of the sensor as the true value label, the feature matrix is ​​filtered to obtain a valid dataset;

[0012] An ensemble learning model is constructed based on the effective dataset; wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model;

[0013] The target feature matrix is ​​obtained, and the target feature matrix is ​​input into the ensemble learning model for prediction. The predicted surface temperature value of the target area is output, thus completing the monitoring of satellite and sensor data of the target area.

[0014] Secondly, this application provides an integrated satellite and sensor data acquisition and analysis system, comprising:

[0015] Acquisition module: Acquires comprehensive data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data;

[0016] Preprocessing module: preprocesses the satellite remote sensing data and the sensor data respectively to obtain the daily average values ​​of land surface temperature and sensor land surface temperature;

[0017] Feature processing module: Extracts features from the surface temperature, land use data, and digital elevation model data respectively, integrates the features, and generates a feature matrix; uses the daily average surface temperature of the sensor as the true value label, filters the feature matrix, and obtains a valid dataset;

[0018] Model building module: Constructs an ensemble learning model based on the effective dataset; wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model;

[0019] Prediction module: acquires the target feature matrix, inputs the target feature matrix into the ensemble learning model for prediction, outputs the predicted surface temperature value of the target area, and completes the monitoring of satellite and sensor data of the target area.

[0020] Thirdly, this application provides an apparatus comprising a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing an integrated satellite and sensor data acquisition and analysis method; the processor is configured to execute the program instructions stored in the memory to implement an integrated satellite and sensor data acquisition and analysis method.

[0021] Fourthly, this application provides a storage medium storing processor-executable program instructions for executing an integrated satellite and sensor data acquisition and analysis method.

[0022] This application provides a method and system for integrated acquisition and analysis of satellite and sensor data, which has the following advantages:

[0023] (1) By integrating multi-source data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data, the monitoring scope has been expanded and the comprehensiveness and accuracy of the data have been improved.

[0024] (2) By performing steps such as data cleaning, resampling, and feature extraction, the quality and usability of the data are effectively improved, providing a solid foundation for subsequent analysis and modeling;

[0025] (3) The method of this application adopts a stacked ensemble machine learning model, which integrates the advantages of different models and improves the prediction accuracy and generalization ability. At the same time, the final dataset is generated, realizing comprehensive and accurate monitoring of the target area, and providing strong data support for smart city construction, ecological environment monitoring and other fields. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating an integrated satellite and sensor data acquisition and analysis method according to Embodiment 1 of this application.

[0027] Figure 2 This is a flowchart of an integrated satellite and sensor data acquisition and analysis method according to Embodiment 1 of this application;

[0028] Figure 3 This is a schematic diagram of the structure of an integrated satellite and sensor data acquisition and analysis system according to Embodiment 2 of this application;

[0029] Figure 4 This is a schematic diagram of the device structure in Embodiment 3 of this application;

[0030] Figure 5 This is a schematic diagram of the storage medium structure of Embodiment 4 of this application. Detailed Implementation

[0031] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0032] The following analysis, based on relevant technologies, examines existing solutions.

[0033] Satellite remote sensing monitoring has limited resolution; some environmental monitoring satellites have limited resolution and cannot provide high-resolution data, which restricts the monitoring of some small-scale environmental changes. At the same time, it is susceptible to weather and cloud cover, which can prevent satellite sensors from acquiring surface data, especially under adverse weather conditions. This leads to data discontinuity and data delays, as satellite data transmission and processing may take time, preventing real-time data acquisition.

[0034] However, sensors have limited spatial coverage, and sensor networks typically have limited coverage areas, requiring the deployment of numerous sensors to cover large areas. Data quality and accuracy are also affected; sensors are susceptible to environmental interference, leading to a decline in data quality and accuracy. Furthermore, differences may exist between sensors, necessitating calibration and correction. In summary, both satellite and sensor-based environmental monitoring present technological limitations and challenges. Therefore, integrating satellite and sensor data acquisition and analysis can effectively fuse large-scale and high-resolution monitoring data, as well as coarse-grained and fine-grained data, thereby improving the accuracy and resolution of monitoring data.

[0035] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0036] Example 1

[0037] Please see Figure 1 This is a flowchart illustrating an integrated satellite and sensor data acquisition and analysis method according to Embodiment 1 of this application; the steps include:

[0038] S1: Obtain comprehensive data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data.

[0039] In this embodiment, Landsat8 OLI_TIRS satellite remote sensing data is downloaded from the AI ​​Earth platform, surface temperature sensor data is downloaded from the self-developed sensor management platform, 30-meter spatial resolution land use data is downloaded from the China Land Cover Dataset website, and 30-meter resolution digital elevation model data is downloaded from the USGS Earth Explorer platform.

[0040] S2: Preprocess the satellite remote sensing data and the sensor data respectively to obtain the surface temperature and the daily average value of the sensor surface temperature.

[0041] In this embodiment, the satellite remote sensing data is preprocessed, specifically including:

[0042] Based on the accuracy of the acquired satellite remote sensing data, and to obtain standardized spatial resolution for subsequent analysis and comparison, the satellite remote sensing data was resampled to fit into a unified grid system with a 1-kilometer resolution. Considering issues such as calibration bias, inversion accuracy, and universality of the infrared thermal sensor bands, the land surface temperature was retrieved using a single-window algorithm. The calculation formula is as follows:

[0043]

[0044] Among them, T LS T represents the surface temperature. a T is the average atmospheric operating temperature. Sensor represents the brightness temperature observed by the satellite's remote sensor; a and b are empirical constants related to the Planck equation; C and D are intermediate variables, calculated using the following formula:

[0045] C=ετ

[0046] D = (1-ε)[1+(1-ε)τ]

[0047] Where ε is the surface emissivity; τ is the atmospheric transmittance from the ground to the sensor.

[0048] Preprocessing of sensor data specifically includes:

[0049] To ensure the validity of relevant factor data, the study area was divided into a unified grid system with a resolution of 30 meters. Sensor data were integrated, and daily averages were calculated for the data within each grid. When calculating the daily average temperature monitored by the sensors, it was necessary to determine the validity of the sensor data for each grid.

[0050] Specifically, a parameter 'n' is introduced to determine the validity of sensor monitoring data within a grid cell. For example, setting n to 8 means that the daily average temperature of a grid cell is considered valid only if the sensor recording period for a given day is greater than or equal to 8 hours. If a grid cell records less than n hours, the sensor data for that day is discarded.

[0051] The daily average value of sensor data within the effective grid is calculated using the following formula:

[0052]

[0053] Where i is the grid number; T avg (i) represents the daily average temperature of the grid, i.e., the daily average surface temperature of the sensor; N is the number of grid records; t represents different times; T i,t This represents the monitoring data of grid i at time t.

[0054] S3: After extracting features from the surface temperature, land use data, and digital elevation model data respectively, feature integration is performed to generate a feature matrix.

[0055] In this embodiment, various features are extracted and statistically analyzed in a 30-meter resolution grid to provide input for subsequent models, thereby improving the accuracy of surface temperature inversion.

[0056] Feature extraction of surface temperature: The surface temperature is resampled to a uniform grid system with a resolution of 30 meters, and the temperature value T of each 30-meter grid is extracted. rs As basic input features;

[0057] Feature extraction of land use data: Based on the land use data, different land cover types are one-hot encoded according to the land use type in the unified grid system with a resolution of 30 meters, and the encoded feature L is used as the basic input feature to control different land cover conditions;

[0058] Feature extraction of digital elevation model data: The digital elevation model data is resampled to a unified grid system with a resolution of 30 meters. As an important feature reflecting terrain changes, the elevation value H of each 30-meter grid is extracted as the basic input feature.

[0059] The above basic input features T rs L and H are integrated into the feature matrix X = (T) rs ,H,L).

[0060] S4: Using the daily average surface temperature value of the sensor as the true value label, filter the feature matrix to obtain a valid dataset.

[0061] In this embodiment, the effective grid daily average temperature of the sensor's surface temperature is extracted from a 30-meter resolution grid. This feature T avg As the true value label, the feature matrix X is integrated with the true value label y; where X includes the temperature value T. rs Encoding feature L, elevation value H, and true value label y = T avg This is the measured surface temperature.

[0062] By selecting grids with non-empty y-values, an effective dataset (X) with spatiotemporal consistency is constructed. valid ,y); specifically including:

[0063] (X valid ,y)=(X i,day ,y i,day )|n i,day ≠ null, i∈full grid set

[0064] Among them, (X)valid ,y) is the effective grid feature matrix X valid The true value label is y; i is the grid number; day is the time corresponding to the feature.

[0065] S5: Construct an ensemble learning model based on the effective dataset; wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model.

[0066] In this embodiment, an ensemble learning model is used to achieve integrated analysis of satellite remote sensing data and sensor data, fusing the outputs of different machine learning models. To construct the ensemble learning model M... ensemble Based on the effective dataset (X) valid The feature matrix X (x, y) is modeled using three machine learning models: Random Forest, Extreme Gradient Boosting (XGBoost), and Lightweight Gradient Boosting (LightGBM). The outputs of these three models are then fused using a linear regression model to obtain a high-precision land surface temperature prediction. The specific steps are as follows:

[0067] S51: Establish training and test sets based on the effective dataset, set the ratio of training set to test set to 7:3, and divide the training set and test set;

[0068] S52: Construct a random forest model M based on the training set and the test set respectively. RF XGBoost model M XGB LightGBM model M LGBM ;

[0069] S53: Use grid search to select the optimal parameters for the random forest model, the XGBoost model, and the LightGBM model;

[0070] S54: Train the random forest model, XGBoost model, and LightGBM model respectively according to the optimization parameters;

[0071] S55: Obtain the output y of the random forest model RF The output y of the XGBoost model XGB The output y of the LightGBM model LGBM ;

[0072] S56: Construct a linear regression model, y RF y XGB y LGBM As input to the linear regression model, the true value label y is used as the label to obtain the predicted value. The calculation formula is:

[0073]

[0074] Where a, b, c, and d are coefficients, optimized using the least squares method;

[0075] S57: Obtain the trained Random Forest model, XGBoost model, and LightGBM model, i.e., the ensemble learning model M. ensemble .

[0076] S6: Obtain the target feature matrix, input the target feature matrix into the ensemble learning model for prediction, output the predicted surface temperature value of the target area, and complete the monitoring of satellite and sensor data of the target area.

[0077] In this embodiment, based on the ensemble learning model trained in step S5, for a 30-meter resolution grid system of the target area, the target feature matrix of all grids is input into the ensemble learning model, and the predicted surface temperature value of each 30-meter grid is output. Thus, full-time, full-grid surface temperature data is supplemented, constructing an integrated dataset of satellite and sensor data, enabling integrated monitoring of satellite and sensor data for the target area.

[0078] In summary, Embodiment 1 of this application acquires satellite remote sensing data, sensor data, land use data, and digital elevation model data from multiple platforms, and performs preprocessing to improve data quality. Then, it extracts, integrates, and verifies the validity of the data to obtain a valid feature dataset. A stacked ensemble machine learning model is used for modeling, fusing the outputs of different models to enhance predictive capabilities. The acquired target feature matrix is ​​then predicted using the ensemble learning model to output a predicted land surface temperature value, achieving integrated monitoring of satellite and sensor data for the target area. This application effectively fuses large-scale monitoring data with high-resolution data, improving the accuracy and scope of monitoring data, and providing strong support for smart city construction, ecological environment monitoring, and other fields.

[0079] Example 2

[0080] Please see Figure 3 This is a schematic diagram of the structure of an integrated satellite and sensor data acquisition and analysis system according to Embodiment 2 of this application; the specific content includes:

[0081] Acquisition module: Acquires comprehensive data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data;

[0082] Preprocessing module: preprocesses the satellite remote sensing data and the sensor data respectively to obtain the daily average values ​​of land surface temperature and sensor land surface temperature;

[0083] Feature processing module: Extracts features from the surface temperature, land use data, and digital elevation model data respectively, integrates the features, and generates a feature matrix; uses the daily average surface temperature of the sensor as the true value label, filters the feature matrix, and obtains a valid dataset;

[0084] Model building module: Constructs an ensemble learning model based on the effective dataset; wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model;

[0085] Prediction module: acquires the target feature matrix, inputs the target feature matrix into the ensemble learning model for prediction, outputs the predicted surface temperature value of the target area, and completes the monitoring of satellite and sensor data of the target area.

[0086] Example 3

[0087] Please see Figure 4 This is a schematic diagram of the device structure in Embodiment 3 of this application. The device 50 includes a processor 51 and a memory 52 coupled to the processor 51.

[0088] The memory 52 stores program instructions for implementing the above-described method for integrated acquisition and analysis of satellite and sensor data.

[0089] The processor 51 is used to execute program instructions stored in the memory 52 to realize an integrated acquisition and analysis of satellite and sensor data.

[0090] The processor 51 can also be referred to as a CPU (Central Processing Unit).

[0091] Processor 51 may be an integrated circuit chip with signal processing capabilities. Processor 51 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0092] Example 4

[0093] Please see Figure 5This is a schematic diagram of the storage medium in Embodiment 4 of this application. The storage medium in this embodiment stores a program file 61 capable of implementing all the above methods. This program file 61 can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or devices such as computers, servers, mobile phones, and tablets.

[0094] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0095] The above description is only a preferred embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural changes made based on the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

[0096] Although embodiments of this application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of this application, the scope of which is defined by the appended claims and their equivalents.

[0097] Of course, the present invention may have many other embodiments. Based on this embodiment, other embodiments obtained by those skilled in the art without any creative effort are all within the scope of protection of the present invention.

Claims

1. A method for integrated acquisition and analysis of satellite and sensor data, characterized in that, include: Acquire comprehensive data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data; The satellite remote sensing data and the sensor data are preprocessed to obtain the daily average values ​​of land surface temperature and sensor land surface temperature. After extracting features from the surface temperature, the land use data, and the digital elevation model data respectively, feature integration is performed to generate a feature matrix; Using the daily average surface temperature value of the sensor as the true value label, the feature matrix is ​​filtered to obtain a valid dataset; An ensemble learning model is constructed based on the effective dataset; wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model; The target feature matrix is ​​obtained, and the target feature matrix is ​​input into the ensemble learning model for prediction. The predicted value of the land surface temperature of the target area is output, thus completing the monitoring of satellite and sensor data of the target area. The step of integrating features after extracting features from the surface temperature, land use data, and digital elevation model data to generate a feature matrix specifically includes the following steps: The surface temperature is resampled into the grid, and the temperature value T of the grid is extracted. rs ; Based on the land use data, different land cover types are individually coded according to the land use type in the grid to obtain the coding feature L; The digital elevation model data is resampled into a grid, and the elevation value H of the grid is extracted. Temperature value T rs The encoding feature L and elevation value H are integrated into the feature matrix X = (T rs ,H,L); The step of using the daily average surface temperature value of the sensor as the true value label and filtering the feature matrix to obtain a valid dataset specifically includes the following steps: The feature matrix X is integrated with the true value label y; where X includes the temperature value T. rs Encoding feature L, elevation value H, and true value label y = T avg This refers to the measured surface temperature. By filtering out grids with non-empty y-values, an effective dataset (X) is constructed. valid ,y); specifically including: (X valid ,y)=(X i,day ,y i,day )|y i,day ≠ null, i∈full grid set Among them, (X) valid ,y) is the effective grid feature matrix X valid The true value label is y; i is the grid number; day is the time corresponding to the feature.

2. The integrated satellite and sensor data acquisition and analysis method according to claim 1, characterized in that, The step of preprocessing the satellite remote sensing data and the sensor data to obtain the daily average values ​​of land surface temperature and sensor land surface temperature specifically includes the following steps: The surface temperature is retrieved using a single-window algorithm, and the calculation formula is as follows: Among them, T LS T represents the surface temperature. a T is the average atmospheric operating temperature. Sensor represents the brightness temperature observed by the satellite's remote sensor; a and b are empirical constants related to the Planck equation; C and D are intermediate variables, calculated using the following formula: C=ετ D = (1-ε)[1+(1-ε)τ] Where ε is the surface emissivity; τ is the atmospheric transmittance from the ground to the sensor; The area where sensor data is acquired is divided into grids. The validity of sensor data within each grid cell is determined, and the daily average value of sensor data within the valid grid cells is calculated using the following formula: Where i is the grid number; T avg (i) represents the average daily temperature of the grid; N represents the number of grid records; t represents different times; T i,t This represents the monitoring data of grid i at time t.

3. The integrated satellite and sensor data acquisition and analysis method according to claim 2, characterized in that, The determination of the validity of sensor data in a grid cell includes: introducing a parameter n; if the sensor recording period is greater than or equal to n hours, the sensor data of that grid is determined to be valid, i.e., it is a valid grid; if the sensor recording period is less than n hours, the recorded sensor data is invalid.

4. The integrated satellite and sensor data acquisition and analysis method according to claim 1, characterized in that, The step of constructing an ensemble learning model based on the effective dataset, wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model, specifically includes the following steps: Establish training and testing sets based on the aforementioned valid dataset; Based on the training set and the test set, a random forest model, an XGBoost model, and a LightGBM model are constructed respectively. Grid search is used to select the optimal parameters of the random forest model, the XGBoost model, and the LightGBM model. The Random Forest model, XGBoost model, and LightGBM model were trained according to the aforementioned optimization parameters to obtain the output y of the Random Forest model. RF The output y of the XGBoost model XGB The output y of the LightGBM model LGBM ; y RF y XGB y LGBM The true value label y is used as input to a pre-built linear regression model to obtain the predicted value. The calculation formula is: Where a, b, c, and d are coefficients, optimized using the least squares method; this yields the trained Random Forest model, XGBoost model, and LightGBM model, i.e., the ensemble learning model M. ensemble .

5. The integrated satellite and sensor data acquisition and analysis method according to claim 1, characterized in that, The steps of acquiring the target feature matrix, inputting the target feature matrix into the ensemble learning model for prediction, and outputting the predicted surface temperature value of the target area to complete the monitoring of satellite and sensor data of the target area specifically include the following steps: The target feature matrix is ​​input into the ensemble learning model to obtain the predicted surface temperature value; By supplementing the full-time grid surface temperature data with the predicted surface temperature values, a dataset of satellite and sensor data is constructed to complete the monitoring of satellite and sensor data in the target area.

6. A system for an integrated satellite and sensor data acquisition and analysis method according to claim 1, characterized in that, include: Acquisition module: Acquires comprehensive data, including satellite remote sensing data, sensor data, land use data, and digital elevation model data; Preprocessing module: preprocesses the satellite remote sensing data and the sensor data respectively to obtain the daily average values ​​of land surface temperature and sensor land surface temperature; Feature processing module: Extracts features from the surface temperature, land use data, and digital elevation model data respectively, then integrates these features to generate a feature matrix; Using the daily average surface temperature value of the sensor as the true value label, the feature matrix is ​​filtered to obtain a valid dataset; Model building module: Constructs an ensemble learning model based on the effective dataset; wherein the ensemble learning model consists of a trained random forest model, an XGBoost model, and a LightGBM model; Prediction module: acquires the target feature matrix, inputs the target feature matrix into the ensemble learning model for prediction, outputs the predicted surface temperature value of the target area, and completes the monitoring of satellite and sensor data of the target area.

7. A device, characterized in that, The device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions for implementing the integrated satellite and sensor data acquisition and analysis method according to any one of claims 1-5; the processor is used to execute the program instructions stored in the memory to implement the integrated satellite and sensor data acquisition and analysis.

8. A storage medium, characterized in that, The system stores processor-executable program instructions for performing an integrated satellite and sensor data acquisition and analysis method as described in any one of claims 1-5.

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