Forestry resource collection method based on GIS technology

Abstract

The invention discloses a forestry resource acquisition method based on a GIS technology, relates to the technical field of data processing, and aims to solve the technical problem that an existing catalyst is generally poor in performance in a medium and low temperature flue gas environment. The method comprises the following steps: collecting a remote sensing image of a target forest region through remote sensing equipment; selecting a first target transmission strategy based on the remote sensing image, and transmitting the remote sensing image based on the first target transmission strategy; the method comprises the following steps: acquiring and obtaining sensor data through an Internet of Things sensor arranged in a target forest region; selecting a second target transmission strategy based on the sensor data, and transmitting the sensor data based on the second target transmission strategy; performing dimension reduction processing and integration processing on the obtained remote sensing image and the sensor data to obtain target forestry resource data; the target forestry resource data comprises original collection data and prediction result information generated based on the original collection data.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a forestry resource collection method based on GIS technology. Background Art

[0002] With the continuous development of modern science and technology, GIS (Geographic Information System) technology is increasingly used in various fields, especially in the management and monitoring of forestry resources. The collection, analysis and management of forestry resources have always been an important direction of forestry scientific research. Traditional forestry resource collection methods mostly rely on manual measurement and on-site investigation, which have problems such as low efficiency, poor accuracy and high time consumption. Therefore, how to efficiently and accurately obtain forestry resource information has become an important topic in current forestry management and research. Summary of the invention

[0003] The main purpose of the present application is to provide a porous multi-element doped sludge carbon-based low-temperature denitrification catalyst and a preparation method thereof, aiming to solve the technical problem that existing catalysts generally have poor performance in medium and low temperature flue gas environments.

[0004] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0005] In the first aspect, the embodiment of the present application provides a forestry resource collection method based on GIS technology, which is applied to a forestry resource collection system, wherein the forestry resource collection system includes a remote sensing image collection module, a sensor data collection module, a data transmission module and a terminal device; the remote sensing image collection module is used to collect images of target forest resources based on the remote sensing collection device, and transmit the images to the terminal device based on the data transmission module; the sensor data collection module refers to a group of Internet of Things sensors deployed in the target forest area, which is used to collect sensor data in real time and transmit the data to the terminal device based on the data transmission module; the data transmission module is used to transmit data and dynamically adjust the transmission strategy according to the data transmission environment; the terminal device includes a high-dimensional data dimensionality reduction module and a data integration module;

[0006] The forestry resource collection method based on GIS technology comprises the following steps:

[0007] Collecting a remote sensing image of a target forest area through a remote sensing device; selecting a first target transmission strategy based on the remote sensing image, and transmitting the remote sensing image based on the first target transmission strategy;

[0008] By using IoT sensors deployed in the target forest area, sensor data is collected and obtained; the sensor data includes real-time soil data, real-time moisture data and real-time temperature data; based on the sensor data, a second target transmission strategy is selected, and the sensor data is transmitted based on the second target transmission strategy;

[0009] The acquired remote sensing images and sensor data are subjected to dimensionality reduction and integration processing to obtain target forestry resource data; the target forestry resource data includes original collected data and prediction result information generated based on the original collected data.

[0010] As some optional implementations of the present application, the step of performing dimensionality reduction processing and integration processing on the acquired remote sensing image and the sensor data to obtain target forestry resource data includes:

[0011] Performing dimensionality reduction processing on the acquired remote sensing image and the sensor data and verifying whether the target information is retained after the dimensionality reduction processing, and if the verification is passed, obtaining low-dimensional data;

[0012] Integrate the acquired remote sensing image and the sensor data to obtain integrated data;

[0013] After the low-dimensional data and the integrated data are subjected to decision-level data fusion processing, they are input into a target prediction model to obtain target forestry resource data and prediction result information;

[0014] The decision-level data fusion process satisfies the following relationship: fused =w low *D low +w full *D full Among them, D fused represents the data after decision-level data fusion processing, w low and w full are the weights of low-dimensional data and integrated data, respectively, w low +w full＝ 1;D low represents low-dimensional data, D full Indicates integrated data;

[0015] The target prediction model satisfies the following relationship:

[0016] in, Indicates the prediction result information, D fused represents the data after decision-level data fusion processing, θ represents the model parameters, and f(·) represents the prediction function;

[0017] The loss function of the target prediction model is:

[0018] in, Represents the predicted value, y i represents the true value, n represents the number of samples, represents the squared error of each sample.

[0019] As some optional implementations of the present application, the step of performing dimensionality reduction processing on the acquired remote sensing image and the sensor data to obtain low-dimensional data includes:

[0020] The remote sensing image is subjected to feature extraction based on a convolutional neural network to obtain remote sensing feature data; the remote sensing feature data and the sensor data are preprocessed to obtain first multidimensional data; data centering is performed based on the first multidimensional data to obtain a centralized data matrix; based on the centralized data matrix, a covariance matrix of the centralized data is calculated to obtain; the covariance matrix of the centralized data is subjected to eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors; a preset number of target eigenvectors are selected to form a first target dimensionality reduction matrix; the first multidimensional data is projected onto the first target dimensionality reduction matrix to obtain first low-dimensional data;

[0021] Verifying whether the first low-dimensional data retains the target information based on the three-dimensional scatter plot; if the verification is successful, using the first low-dimensional data as the target low-dimensional data;

[0022] If the verification fails, performing a second dimensionality reduction process on the first multi-dimensional data based on a second dimensionality reduction strategy to obtain second low-dimensional data; the second dimensionality reduction process is a nonlinear dimensionality reduction process;

[0023] Similarly, the target information in the second low-dimensional data is verified based on the three-dimensional scatter plot, so that the second low-dimensional data that passes the verification is used as the target low-dimensional data.

[0024] As some optional implementations of the present application, the step of integrating the acquired remote sensing image and the sensor data to obtain integrated data includes:

[0025] After performing time synchronization processing on the acquired remote sensing image and the sensor data by using interpolation processing, a plurality of image data pairs are obtained; the remote sensing image and the sensor data in the image data pairs have the same time point;

[0026] The remote sensing image in each image data pair is converted into a feature vector based on a preset deep learning model, and the sensor data is converted into standard sensor data through Z-Score standardization processing to obtain a multidimensional data set;

[0027] The eigenvector satisfies the following relationship: I =f CNN (I); where f CNN represents the preset deep learning model; f I represents the feature vector; I represents the input remote sensing image, and the dimension of I is (image height, image width, number of image channels);

[0028] The standard sensor data satisfies the following relationship: Among them, μ S and σ S They are respectively represented as the mean and standard deviation of the sensor data set, Represented as the i-th standard sensor data, S i Represents the i-th sensor data;

[0029] The multidimensional data set is subjected to feature splicing processing to obtain integrated data.

[0030] As some optional implementation methods of the present application, the step of performing decision-level data fusion processing on the low-dimensional data and the integrated data to obtain target forestry resource data and prediction result information includes:

[0031] The low-dimensional data is input into a first preset prediction model to obtain coverage information of the target forest area; the first preset prediction model includes an input layer, a hidden layer and an output layer; the input layer is used to receive the low-dimensional data; the hidden layer includes a plurality of neurons, and a nonlinear activation function is used to learn the relationship between the low-dimensional data and the coverage information of the target forest area; the output layer is used to output the coverage information of the target forest area;

[0032] The integrated data is input into a second preset prediction model to obtain health status information, tree species distribution information and growth rate information of each tree species of the target forest area; the second preset prediction model includes an input layer, a hidden layer and an output layer; the input layer is used to receive the integrated data; the hidden layer includes 3 hidden sub-layers, and each hidden sub-layer extracts the features of the integrated data through a combination of weighted sum and activation function; the output layer includes 3 output sub-layers, which are respectively used to output health status information, tree species distribution information and growth rate information of each tree species of the target forest area.

[0033] As some optional implementation modes of the present application, after obtaining the target forestry resource data and the prediction result information, the following steps are further included:

[0034] Based on the coverage information, health information, tree species distribution information and growth rate information of each tree species in the target forest area, a forestry resource distribution map and a forestry resource prediction change map are generated to formulate forestry management target strategies.

[0035] As some optional implementations of the present application, the step of selecting a first target transmission strategy based on the remote sensing image and transmitting the remote sensing image based on the first target transmission strategy includes:

[0036] Performing feature recognition processing on the remote sensing image to obtain feature information of the remote sensing image;

[0037] Based on the size of the remote sensing image, a first constraint is selected; the first constraint is a first target compression ratio, a first target bandwidth value and a first target number of transmissions; based on the content of the remote sensing image, a second constraint is selected; the second constraint is a first target resolution and whether data compression is performed; based on the transmission environment, a third constraint is selected; the third constraint is the first target transmission path information; based on the transmission task of the remote sensing image, a fourth constraint is selected; the fourth constraint is the first target transmission protocol;

[0038] Based on the first restriction condition, the second restriction condition, the third restriction condition and the fourth restriction condition, a first target transmission strategy is obtained, and the remote sensing image is transmitted based on the first target transmission strategy.

[0039] As some optional implementation methods of the present application, the first constraint condition is selected based on the size of the remote sensing image; the first constraint condition is the compression ratio, bandwidth value and number of transmissions; the second constraint condition is selected based on the content of the remote sensing image; the second constraint condition is the resolution and whether to perform data compression; the third constraint condition is selected based on the transmission environment; the third constraint condition is the transmission path information; the fourth constraint condition is selected based on the transmission task of the remote sensing image; the fourth constraint condition is the steps of the transmission protocol, including:

[0040] If the remote sensing image is a low-resolution image or a single-band image, the first restriction condition is a low compression ratio and a low bandwidth value and a single data transmission; if the remote sensing image is a high-resolution multispectral image or a hyperspectral image, the first restriction condition is a high compression ratio and a high bandwidth value and a multiple data transmission;

[0041] If there are important geographical areas or abnormal areas in the remote sensing image, the second restriction condition is regional high-resolution transmission; if there are a large number of blank areas or repeated areas in the remote sensing image, redundant information in the image is used for data compression;

[0042] If the transmission path of the remote sensing image is long and the transmission network is congested, the third restriction condition is a multi-path transmission strategy; if the transmission path of the remote sensing image is short or the transmission network is not congested, the third restriction condition is a single-path transmission strategy;

[0043] If the transmission task of the remote sensing image is disaster monitoring, the transmission protocol is the User Datagram Protocol; if the transmission task of the remote sensing image is periodic and offline monitoring, the transmission protocol is the Transmission Control Protocol.

[0044] As some optional implementation manners of the present application, the step of selecting a second target transmission strategy based on the sensor data, and transmitting the sensor data based on the second target transmission strategy includes:

[0045] Outputting the sensor data to a preset data classification model for data classification processing, and obtaining data classification values ​​of the sensor data respectively; the data classification values ​​include a high priority value, a medium priority value, and a low priority value;

[0046] Based on the data classification value, selecting a second target transmission strategy;

[0047] The sensor data is transmitted based on the second target transmission strategy.

[0048] As some optional implementations of the present application, the step of selecting a second target transmission strategy based on the data classification value includes:

[0049] For data with a high priority value, the user datagram protocol is used for transmission;

[0050] For data with a data classification value of medium priority, the transmission control protocol is used for transmission;

[0051] For data with a data classification value of a low priority value, message queue telemetry transmission is used for transmission.

[0052] Traditional forestry resource collection methods mainly rely on manual surveys and ground measurements, which are inefficient and not very accurate. The forestry resource collection method based on GIS technology proposed in this application has obvious technological progress, which is specifically reflected in the following aspects:

[0053] Traditional forestry resource collection methods usually rely on a single data source, such as using only remote sensing images or only ground measurement data. However, this application can achieve all-round and multi-level resource collection from spatial information (remote sensing images) to environmental data (such as climate, soil, moisture, temperature, etc.) by comprehensively using remote sensing image acquisition modules and IoT sensor data acquisition modules. Remote sensing images provide spatial information of forest areas, while sensor data monitors the environmental status of forest areas in real time. The combination of the two greatly improves the accuracy and comprehensiveness of data collection.

[0054] In traditional forestry resource collection systems, data transmission is usually static and cannot adapt to different transmission environments. The data transmission module in this application has the ability to dynamically adjust the transmission strategy. According to the real-time situation of the collected data and the transmission environment, the system can intelligently select the optimal transmission strategy (such as selecting a suitable compression method, transmission path, protocol, etc.), effectively avoiding the problems of data loss and low transmission efficiency caused by transmission delays, insufficient bandwidth or signal interference in traditional methods. This dynamic adjustment mechanism ensures the stability and real-time performance of data transmission in complex forest environments, greatly improving the overall performance of the system.

[0055] Remote sensing images and data collected by sensors usually have high dimensions and large data volumes, which makes data processing and analysis very complex and resource-intensive. In traditional forestry resource management, the technology for processing these high-dimensional data is usually relatively crude, with problems such as slow analysis speed and inaccurate results. The present application scheme reduces the dimension and integrates remote sensing images and sensor data by introducing a high-dimensional data dimensionality reduction module and a data integration module in the terminal device. This method can effectively reduce the dimension of the data, remove redundant information, retain key information in the data, and significantly improve the efficiency of data processing. At the same time, the integrated data can provide a more accurate and valuable basis for subsequent analysis and decision-making.

[0056] Due to the combination of sensors and remote sensing technology, this application can achieve real-time monitoring of the target forest area and can obtain key environmental indicators such as climate, soil, moisture, temperature, etc. in a timely manner. Compared with traditional manual survey methods, this application greatly improves the timeliness and accuracy of monitoring. This enables forestry resource managers to respond quickly to changes in forest resources and take timely measures to deal with possible ecological risks or resource losses. For example, in the event of a forest fire or insect infestation, the latest information on the forest area can be obtained through sensor data and remote sensing images in the first place, problem areas can be quickly located, and corresponding emergency measures can be taken.

[0057] Existing traditional forestry resource collection methods often seem inadequate when faced with large-scale forest areas and are unable to quickly cover large areas of forest land. In contrast, the system architecture of the present application has good scalability and adaptability. As the demand for forestry resource management increases, the system can quickly expand the monitoring range and maintain efficient operation by adding remote sensing equipment, sensor nodes and other components. In addition, the data transmission module can flexibly adapt to various geographical conditions and climatic environments according to changes in different environments when dynamically adjusting the transmission strategy. It can not only work efficiently in open areas, but also operate stably in complex and remote forest environments.

[0058] Through the deep integration and dimensionality reduction of remote sensing images and sensor data, this application can provide intelligent decision-making support for forestry resource management. The system can not only monitor forest changes in real time, but also automatically generate early warning information such as climate change, pest warning, resource anomalies, etc. through data analysis and pattern recognition. This enables forestry managers to identify potential problems in advance and take timely countermeasures, thereby effectively reducing the loss of forest resources and environmental risks.

[0059] In summary, the forestry resource collection method based on GIS technology proposed in this application has shown significant technological progress in many aspects compared to the existing technology. It realizes an efficient, accurate and real-time forestry resource collection and monitoring system by integrating advanced technologies such as remote sensing image acquisition, IoT sensor data acquisition, dynamic transmission strategy, high-dimensional data dimensionality reduction and integrated processing. This solution not only improves the efficiency and accuracy of forestry resource collection, but also provides intelligent support for real-time monitoring, data analysis and decision-making of forest resources, and has broad application prospects and practical significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A schematic diagram of the structure of an electronic device of a hardware operating environment involved in an embodiment of the present application;

[0061] Figure 2 A schematic diagram of a module of a forestry resource collection system provided in an embodiment of the present application;

[0062] Figure 3 A schematic diagram of the process of the forestry resource collection method based on GIS technology provided in the embodiment of the present application;

[0063] Figure 4 A schematic diagram of a remote sensing image provided in an embodiment of the present application;

[0064] Markings in the figure: 101 - processor, 102 - communication bus, 103 - network interface, 104 - user interface, 105 - memory. DETAILED DESCRIPTION

[0065] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0067] Refer to the attached Figure 1 , attached Figure 1This is a schematic diagram of the structure of an electronic device of the hardware operating environment involved in the embodiment of the present application. The electronic device may include: a processor 101, such as a central processing unit (CPU), a communication bus 102, a user interface 104, a network interface 103, and a memory 105. Among them, the communication bus 102 is used to realize the connection and communication between these components. The user interface 104 may include a display screen (Display), an input unit such as a keyboard (Keyboard), and the user interface 104 may also include a standard wired interface and a wireless interface. The network interface 103 may optionally include a standard wired interface and a wireless interface (such as a wireless fidelity (WIreless-FIdelity, WI-FI) interface). The memory 105 may optionally be a storage device independent of the aforementioned processor 101. The memory 105 may be a high-speed random access memory (RAM) memory, or it may be a stable non-volatile memory (NVM), such as at least one disk storage; the processor 101 may be a general-purpose processor, including a central processing unit, a network processor, etc., or it may be a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component.

[0068] Those skilled in the art will appreciate that Figure 1 The structure shown in the figure does not constitute a limitation on the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0069] As attached Figure 1 As shown, the memory 105 as a storage medium may include an operating system, a network communication module, a user interface module and a forestry resource collection system.

[0070] In the attached Figure 1 In the electronic device shown, the network interface 103 is mainly used for data communication with the network server; the user interface 104 is mainly used for data interaction with the user; the processor 101 and the memory 105 in the present application can be set in the electronic device, and the electronic device calls the forestry resource collection system stored in the memory 105 through the processor 101, and executes the forestry resource collection method provided in the embodiment of the present application.

[0071] With the continuous development of modern science and technology, GIS (Geographic Information System) technology is increasingly used in various fields, especially in forestry resource management and monitoring. The collection, analysis and management of forestry resources have always been an important direction of forestry scientific research. Traditional forestry resource collection methods mostly rely on manual measurement and on-site investigation, which have problems such as low efficiency, poor accuracy and high time consumption. Therefore, how to efficiently and accurately obtain forestry resource information has become an important topic in current forestry management and research.

[0072] Traditional forestry resource collection methods mainly rely on manual surveys and ground measurements. Although these methods are effective for a certain period of time, with the increase in demand for forest resource protection and the expansion of resource scale, traditional methods have shown many shortcomings. Manual measurement is limited by time, space and personnel, and cannot cover a wide range of forest areas; in addition, traditional collection methods are also difficult to monitor changes in the forest ecological environment in real time, and the data processing efficiency is low, which cannot meet the needs of modern forestry management.

[0073] As an efficient resource monitoring method, remote sensing technology has been widely used in the collection and monitoring of forestry resources. Through satellite remote sensing, drone remote sensing and other equipment, large-scale, high-resolution forest image data can be obtained to monitor forest changes in real time, including forest coverage, tree species distribution, pest and disease invasion, etc. Remote sensing technology has the advantages of wide spatial coverage and strong timeliness, which can overcome the limitations of traditional collection methods. However, it also has problems such as large data volume, complex processing, and inaccurate information. Therefore, it is necessary to combine other technical means to further process and analyze the data.

[0074] The development of IoT technology, especially the advancement of sensor technology, has made it possible to monitor forest environmental data in real time. By deploying a group of IoT sensors in the target forest area, environmental data such as climate, soil, moisture, and temperature can be collected in real time to provide timely dynamic data for the monitoring of forest resources. These sensors can work continuously under different environmental conditions and provide high-frequency and high-precision data collection functions. Combined with the advantages of IoT technology, changes in the forest ecological environment can be captured in real time, improving the accuracy and timeliness of forest resource management.

[0075] The collection of forestry resources often involves a large amount of data, especially remote sensing images and sensor data, which need to be transmitted to the central processing system for analysis and storage after collection. In remote areas such as forests, due to geographical location, environmental interference and other reasons, data transmission often faces problems such as insufficient bandwidth, large transmission delays, and data loss. Therefore, how to dynamically adjust the data transmission strategy according to different transmission environments to ensure efficient data transmission and security has become a key technical issue in forestry resource collection systems.

[0076] In the process of forestry resource collection, remote sensing images and sensor data often have high-dimensional characteristics. If these data are used directly for analysis without processing, it is easy to lead to waste of computing resources and low processing efficiency. Dimensionality reduction of high-dimensional data can effectively reduce the dimension of the data, remove redundant information, and improve the efficiency and accuracy of data processing. The reduced-dimensional data will be more concise and useful, and can help decision makers quickly extract valuable forestry resource information. In addition, data integration and processing technology can unify data from different sources and formats to form comprehensive target forestry resource data, providing accurate information support for further analysis and decision-making.

[0077] As a powerful spatial data management and analysis tool, GIS technology can organically combine geographic spatial information with forestry resource data, and provide support for forestry resource management and decision-making through spatial analysis, geographic information visualization, etc. In the GIS-based forestry resource collection system, remote sensing images and sensor data can accurately depict the spatial distribution of forest resources through the integration and analysis of spatial information, thereby achieving accurate monitoring and dynamic management of the forest ecological environment.

[0078] In order to overcome the shortcomings of the above-mentioned traditional methods, modern forestry resource collection systems are gradually introducing the comprehensive application of remote sensing technology, Internet of Things technology, data transmission technology and GIS technology, and realizing comprehensive, real-time and efficient monitoring and management of forestry resources through accurate environmental data collection, real-time data transmission and efficient data processing.

[0079] The technical solution of this application proposes a forestry resource collection method based on GIS technology, which uses remote sensing image collection module, sensor data collection module, data transmission module and terminal equipment, combines remote sensing image data with IoT sensor data, and after dimensionality reduction processing and data integration, can provide accurate and real-time forestry resource data, improve the efficiency and accuracy of forestry resource management. Through this method, the ecological environment changes of the target forest area can be effectively monitored, forest resource data can be obtained in a timely manner, and scientific basis and decision-making support can be provided for forestry management departments.

[0080] In summary, with the advancement of remote sensing technology, Internet of Things technology, data transmission technology and GIS technology, forestry resource collection methods based on these technologies can overcome the defects of traditional methods and achieve more efficient and accurate forestry resource monitoring and management. Therefore, the technical solution of this application can not only improve the efficiency of resource collection, but also realize efficient transmission and processing of data, providing a more reliable and intelligent tool for modern forestry management.

[0081] Specifically, the forestry resource collection method based on GIS technology described in this application is applied to Figure 2The forestry resource acquisition system shown in the figure includes a remote sensing image acquisition module, a sensor data acquisition module, a data transmission module and a terminal device; the remote sensing image acquisition module is used to acquire images of target forest resources based on the remote sensing acquisition device, and transmit the images to the terminal device based on the data transmission module; the sensor data acquisition module refers to a group of Internet of Things sensors deployed in the target forest area, which is used to collect sensor data in real time and transmit the data to the terminal device based on the data transmission module; the data transmission module is used to transmit data and dynamically adjust the transmission strategy according to the data transmission environment; the terminal device includes a high-dimensional data dimensionality reduction module and a data integration module;

[0082] The forestry resource collection method based on GIS technology is as follows: Figure 3 As shown, the following steps are included:

[0083] Step S10: collecting a remote sensing image of the target forest area through a remote sensing device; selecting a first target transmission strategy based on the remote sensing image, and transmitting the remote sensing image based on the first target transmission strategy.

[0084] The remote sensing device may be a satellite remote sensor, an unmanned aerial vehicle (UAV) remote sensing device, an aerial remote sensing device or a ground remote sensing device; wherein the satellite remote sensor may include Landsat series satellites, Sentinel-1 and Sentinel-2 or MODIS. The unmanned aerial vehicle (UAV) remote sensing device may include an RGB (red, green and blue) camera, a multispectral camera, a laser radar (LiDAR) or an infrared thermal imager. The aerial remote sensing device may include a high-resolution digital camera, a laser radar (LiDAR) or an aerial photography measurement system. The ground remote sensing device may include a spectrometer, a global positioning system (GPS) device or a mobile sensor. The remote sensing images acquired by the acquisition are optical images, synthetic aperture radar (SAR) images, high-resolution images, laser radar (LiDAR) point cloud data, thermal infrared images, normalized difference vegetation index (NDVI) images, vegetation type and coverage images or change detection images. Figure 4 Shown is one of the remote sensing images obtained based on remote sensing equipment.

[0085] For example, the remote sensing images described in this application are obtained through the following steps: Select Landsat8 satellite images to ensure the timeliness (such as the latest image data) and spatial resolution (such as 30-meter resolution) of the images. Perform preprocessing such as radiation correction and geometric correction on the acquired satellite images to eliminate the influence of the atmosphere, sensors, and terrain. Use the classification tools in ArcGIS to extract vegetation types, use vegetation indices such as NDVI to classify forest vegetation, and identify different vegetation types.

[0086] In a further embodiment, step S10 comprises:

[0087] Step S11: performing feature recognition processing on the remote sensing image to obtain feature information of the remote sensing image.

[0088] Before performing feature recognition processing on the remote sensing image, the remote sensing image can also be preprocessed, such as using a filtering algorithm (such as median filtering, Gaussian filtering, etc.) to remove noise in the image and improve image quality; or by adjusting the contrast, brightness, or using methods such as histogram equalization, the details of the remote sensing image can be enhanced to make the features more obvious.

[0089] The feature recognition and extraction may include: using Canny, Sobel, Laplacian and other algorithms to detect edges in the image and extract the contour and boundary information of the remote sensing image; or, extracting texture features in the image through gray level co-occurrence matrix (GLCM), local binary pattern (LBP) and other methods; or, shape recognition: using morphological operations (such as corrosion, expansion, etc.) to extract shape features of the target area; or, using machine learning or deep learning algorithms (such as support vector machine SVM, convolutional neural network CNN, etc.) to classify different features in the remote sensing image and mark the targets in the image (such as buildings, roads, forests, etc.).

[0090] Step S12, based on the size of the remote sensing image, select the first constraint condition; the first constraint condition is the first target compression ratio, the first target bandwidth value and the first target transmission number; based on the content of the remote sensing image, select the second constraint condition; the second constraint condition is the first target resolution and whether to perform data compression; based on the transmission environment, select the third constraint condition; the third constraint condition is the first target transmission path information; based on the transmission task of the remote sensing image, select the fourth constraint condition; the fourth constraint condition is the first target transmission protocol.

[0091] To facilitate those skilled in the art to understand step S12, the above four restriction conditions are described in detail below in combination with actual conditions:

[0092] For the first constraint: For example, for large-size images, a higher compression ratio may be required to reduce the size of the transmitted file. For example, if the image is large, a transmission path with a higher bandwidth is selected to ensure that the transmission is completed within the specified time. For example, considering the size and bandwidth of the image, decide whether it needs to be transmitted in multiple times to avoid retransmission caused by a failed transmission.

[0093] For the second constraint: if the image content is relatively simple, a lower resolution can be selected; if high-precision analysis is required, a higher resolution can be selected. The resolution here can be selected by setting a standard or predefined value, and there is no special limitation here. For example, if the image content contains less details or redundancy, the image can be compressed to reduce the amount of data, but if the content is complex and the details are critical, compression may not be performed.

[0094] For the third constraint: For example, when the network condition is poor, redundant paths can be selected to ensure reliable data transmission, or a stronger error correction algorithm can be used. For example, the load of the current transmission path can be considered to avoid selecting a path that is running under high load.

[0095] For the fourth constraint, for example, when high reliability and accuracy are required, the TCP protocol may be selected; when low-latency real-time transmission is required, the UDP protocol may be selected. You can also choose the appropriate protocol and strategy to balance speed and data loss risk based on the image size and transmission delay requirements.

[0096] That is, if the remote sensing image is a low-resolution image or a single-band image, the first restriction condition is a low compression ratio and a low bandwidth value and a single data transmission; if the remote sensing image is a high-resolution multispectral image or a hyperspectral image, the first restriction condition is a high compression ratio and a high bandwidth value, and data transmission is performed in multiple times;

[0097] If there are important geographical areas or abnormal areas in the remote sensing image, the second restriction condition is regional high-resolution transmission; if there are a large number of blank areas or repeated areas in the remote sensing image, redundant information in the image is used for data compression;

[0098] If the transmission path of the remote sensing image is long and the transmission network is congested, the third restriction condition is a multi-path transmission strategy; if the transmission path of the remote sensing image is short or the transmission network is not congested, the third restriction condition is a single-path transmission strategy;

[0099] If the transmission task of the remote sensing image is disaster monitoring, the transmission protocol is the User Datagram Protocol; if the transmission task of the remote sensing image is periodic and offline monitoring, the transmission protocol is the Transmission Control Protocol.

[0100] Step S13: obtaining a first target transmission strategy based on the first restriction condition, the second restriction condition, the third restriction condition and the fourth restriction condition, and transmitting the remote sensing image based on the first target transmission strategy.

[0101] Specifically, the constraints selected in step S12 are integrated to analyze the mutual influence and optimal combination of various conditions (such as bandwidth, compression ratio, resolution, transmission path, transmission protocol, etc.). The conditions are weighed to determine the optimal transmission solution. For example, if the bandwidth is small, the compression ratio needs to be increased and a lower resolution needs to be selected; if the transmission environment is relatively stable, a high-resolution and uncompressed solution can be selected.

[0102] After determining the first target transmission strategy, start the transmission of remote sensing images. During the transmission process, ensure that the image files are processed according to the predetermined strategy (such as compression, segmentation, encryption, etc.) and transmitted through the selected path and protocol. During the transmission process, it is necessary to monitor the transmission status, ensure data integrity, and respond to possible transmission problems (such as packet loss, timeout, etc.) in a timely manner. After the transmission is completed, verify the received image to ensure that its integrity, resolution, etc. meet the predetermined requirements. If there is a problem with the transmission (such as image damage or loss), retransmit or adjust the strategy based on the feedback error information.

[0103] Step S20: Collect and obtain sensor data through Internet of Things sensors deployed in the target forest area; the sensor data includes real-time soil data, real-time moisture data and real-time temperature data; based on the sensor data, select a second target transmission strategy, and transmit the sensor data based on the second target transmission strategy.

[0104] The following example explains the layout of IoT sensors in detail:

[0105] Assume that there are three different areas in a forest: dense forest area, forest edge transition area and open area. Sensors can be deployed in these areas to ensure all-round monitoring:

[0106] In densely forested areas, the sensor types can be soil moisture sensors and temperature sensors. You can choose to deploy them in densely forested areas (such as places close to trees), where the soil moisture is usually higher and the temperature changes are relatively stable. You can choose to deploy sensors at the roots of several different types of trees.

[0107] In the forest edge transition zone, the sensor types can be soil moisture sensors and moisture real-time data sensors. You can choose to deploy them near the edge of the forest, where soil moisture usually changes greatly and is more significantly affected by the external climate. Sensors can be deployed here to monitor soil moisture changes and the impact of external temperature.

[0108] In open areas, the sensor type can be selected as temperature sensor or soil moisture sensor. When deploying, you can choose an open area without obstruction, such as a grassland or shrub area. The climatic conditions in these areas are usually more extreme, so strict monitoring of temperature and humidity is required.

[0109] The real-time soil data includes soil moisture value and soil temperature value. The soil moisture value refers to the percentage of soil water content, reflecting the dryness and wetness of the soil. Usually, the sensor obtains moisture data through resistance or capacitance changes. The soil temperature value refers to the temperature of the soil, which affects plant growth, microbial activity, etc. The sensor obtains temperature data through a thermistor or thermocouple.

[0110] The transmission of the real-time soil data, such as soil temperature, humidity, pH, etc., needs to take geographical distribution into consideration and may cover agricultural or environmental monitoring scenarios. The real-time moisture data generally refers to water sources, groundwater or soil moisture, and involves a dynamic response to environmental changes. As an important environmental parameter, the real-time temperature data requires real-time transmission and has significant timeliness. It can be seen that since the collected data is different, the priority during transmission must also be different. Therefore, the steps of selecting a second target transmission strategy based on the sensor data and transmitting the sensor data based on the second target transmission strategy described in this application include:

[0111] The sensor data is output to a preset data classification model for data classification processing to obtain data classification values ​​of the sensor data respectively; the data classification values ​​include high priority values, medium priority values ​​and low priority values; based on the data classification values, a second target transmission strategy is selected; based on the second target transmission strategy, the sensor data is transmitted.

[0112] Specifically, the step of selecting a second target transmission strategy based on the data classification value includes:

[0113] For data with high priority values, the User Datagram Protocol is used for transmission; for example, temperature, humidity and moisture data in climate data need to be transmitted in real time;

[0114] For data with medium priority, the transmission control protocol is used for transmission; for example, soil data and certain temperature data can be slightly delayed to adapt to lower bandwidth and stable connections;

[0115] For data with a data classification value of a low priority value, message queue telemetry transmission is used for transmission; for example, redundant auxiliary data or background information can be transmitted through a low priority queue.

[0116] Step S30, performing dimensionality reduction processing and integration processing on the acquired remote sensing image and the sensor data to obtain target forestry resource data; the target forestry resource data includes the original collected data and prediction result information generated based on the original collected data.

[0117] To ensure data security, the target forestry resource data is stored locally and in the cloud respectively, and the local and cloud data are synchronized regularly to ensure data consistency.

[0118] In a further embodiment, step S30 includes:

[0119] Step S31, performing dimensionality reduction processing on the acquired remote sensing image and the sensor data and verifying whether the target information is retained after the dimensionality reduction processing, and if the verification is passed, obtaining low-dimensional data;

[0120] Specifically, this step includes:

[0121] Extracting features of the acquired remote sensing image based on a convolutional neural network to obtain remote sensing feature data; preprocessing the remote sensing feature data and the sensor data to obtain first multidimensional data; and performing data centralization processing based on the first multidimensional data to obtain a centralized data matrix;

[0122] The centralized data matrix satisfies the following relationship:

[0123] X centerd =D multi -μ

[0124] In the formula, μ is the mean of each column, D multi is the first multidimensional data, X centerd is the centralized data matrix;

[0125] Based on the centralized data matrix, a covariance matrix of the centralized data is calculated; the covariance matrix of the centralized data is subjected to eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors; a preset number of target eigenvectors are selected to form a first target dimensionality reduction matrix; the first multidimensional data is projected onto the first target dimensionality reduction matrix to obtain first low-dimensional data;

[0126] Verifying whether the first low-dimensional data retains the target information based on the three-dimensional scatter plot; if the verification is successful, using the first low-dimensional data as the target low-dimensional data;

[0127] If the verification fails, the first multi-dimensional data is subjected to a second dimensionality reduction process based on a second dimensionality reduction strategy to obtain second low-dimensional data; the second dimensionality reduction process is a nonlinear dimensionality reduction process, such as t-SNE;

[0128] Similarly, the target information in the second low-dimensional data is verified based on the three-dimensional scatter plot, so that the second low-dimensional data that passes the verification is used as the target low-dimensional data.

[0129] Specifically, the verification of whether the first low-dimensional data retains the target information based on the three-dimensional scatter plot is to compare the data distribution before and after the dimensionality reduction. Generally speaking, the retention of the target information means that the clustering structure and classification structure of the data should be kept as unchanged as possible. For the verification stage, the similarity between the reduced-dimensional data and the original data in space can be calculated. Commonly used similarity metrics include cosine similarity and Euclidean distance.

[0130] The cosine similarity is used to measure the similarity between two vectors, and its value range is [-1,1][-1,1]. The larger the value, the higher the similarity. The Euclidean distance can calculate the direct distance between data points, and is suitable for verifying the change of data position in space.

[0131] Step S32, integrating the acquired remote sensing image and the sensor data to obtain integrated data;

[0132] Step S32 includes:

[0133] After performing time synchronization processing on the acquired remote sensing image and the sensor data by using interpolation processing, a plurality of image data pairs are obtained; the remote sensing image and the sensor data in the image data pairs have the same time point;

[0134] The remote sensing image in each image data pair is converted into a feature vector based on a preset deep learning model, and the sensor data is converted into standard sensor data through Z-Score standardization processing to obtain a multidimensional data set;

[0135] The eigenvector satisfies the following relationship: I =f CNN (I); where f CNN represents the preset deep learning model; f I represents the feature vector; I represents the input remote sensing image, and the dimension of I is (image height, image width, number of image channels);

[0136] The standard sensor data satisfies the following relationship: Among them, μ S and σ S They are respectively represented as the mean and standard deviation of the sensor data set, Represented as the i-th standard sensor data, S i Represents the i-th sensor data;

[0137] The multidimensional data set is subjected to feature splicing processing to obtain integrated data.

[0138] Step S33, after the low-dimensional data and the integrated data are subjected to decision-level data fusion processing, they are input into a target prediction model to obtain target forestry resource data and prediction result information;

[0139] The decision-level data fusion process satisfies the following relationship: fused =w low *D low +w full *D full Among them, D fused represents the data after decision-level data fusion processing, w low and w full are the weights of low-dimensional data and integrated data, respectively, w low +w full＝ 1;D low represents low-dimensional data, D full Indicates integrated data;

[0140] The target prediction model satisfies the following relationship:

[0141] in, Indicates the prediction result information, D fused represents the data after decision-level data fusion processing, θ represents the model parameters, and f(·) represents the prediction function;

[0142] The loss function of the target prediction model is:

[0143] in, Represents the predicted value, y i represents the true value, n represents the number of samples, represents the squared error of each sample.

[0144] The step of performing decision-level data fusion processing on the low-dimensional data and the integrated data to obtain target forestry resource data and prediction result information includes:

[0145] The low-dimensional data is input into a first preset prediction model to obtain coverage information of the target forest area; the first preset prediction model includes an input layer, a hidden layer and an output layer; the input layer is used to receive the low-dimensional data; the hidden layer includes a plurality of neurons, and a nonlinear activation function is used to learn the relationship between the low-dimensional data and the coverage information of the target forest area; the output layer is used to output the coverage information of the target forest area;

[0146] The integrated data is input into a second preset prediction model to obtain health status information, tree species distribution information and growth rate information of each tree species of the target forest area; the second preset prediction model includes an input layer, a hidden layer and an output layer; the input layer is used to receive the integrated data; the hidden layer includes 3 hidden sub-layers, and each hidden sub-layer extracts the features of the integrated data through a combination of weighted sum and activation function; the output layer includes 3 output sub-layers, which are respectively used to output health status information, tree species distribution information and growth rate information of each tree species of the target forest area.

[0147] After obtaining the target forestry resource data and prediction result information, the method further includes:

[0148] Based on the coverage information, health information, tree species distribution information and growth rate information of each tree species in the target forest area, a forestry resource distribution map and a forestry resource prediction change map are generated to formulate forestry management target strategies.

[0149] The training process of each model in the above technical solution can be carried out based on conventional methods and is not the focus of the technical solution of this application, so no further details will be given here.

[0150] In summary, the forestry resource collection method based on GIS technology proposed in this application has obvious technical progress, which is specifically reflected in the following aspects:

[0151] Traditional forestry resource collection methods usually rely on a single data source, such as using only remote sensing images or only ground measurement data. However, this application can achieve all-round and multi-level resource collection from spatial information (remote sensing images) to environmental data (such as climate, soil, moisture, temperature, etc.) by comprehensively using remote sensing image acquisition modules and IoT sensor data acquisition modules. Remote sensing images provide spatial information of forest areas, while sensor data monitors the environmental status of forest areas in real time. The combination of the two greatly improves the accuracy and comprehensiveness of data collection.

[0152] In traditional forestry resource collection systems, data transmission is usually static and cannot adapt to different transmission environments. The data transmission module in this application has the ability to dynamically adjust the transmission strategy. According to the real-time situation of the collected data and the transmission environment, the system can intelligently select the optimal transmission strategy (such as selecting a suitable compression method, transmission path, protocol, etc.), effectively avoiding the problems of data loss and low transmission efficiency caused by transmission delays, insufficient bandwidth or signal interference in traditional methods. This dynamic adjustment mechanism ensures the stability and real-time performance of data transmission in complex forest environments, greatly improving the overall performance of the system.

[0153] Remote sensing images and data collected by sensors usually have high dimensions and large data volumes, which makes data processing and analysis very complex and resource-intensive. In traditional forestry resource management, the technology for processing these high-dimensional data is usually relatively crude, with problems such as slow analysis speed and inaccurate results. The present application scheme reduces the dimension and integrates remote sensing images and sensor data by introducing a high-dimensional data dimensionality reduction module and a data integration module in the terminal device. This method can effectively reduce the dimension of the data, remove redundant information, retain key information in the data, and significantly improve the efficiency of data processing. At the same time, the integrated data can provide a more accurate and valuable basis for subsequent analysis and decision-making.

[0154] As a powerful spatial data analysis tool, GIS technology can effectively integrate and analyze multi-source data (such as remote sensing images, sensor data, etc.) with geographic spatial information. In traditional forestry resource collection systems, geographic spatial information is often not fully utilized, and the combination of resource data and spatial data is relatively weak. This application uses GIS-based integration processing to enable data from different sources to be correlated and analyzed based on geographic space, thereby achieving precise positioning and dynamic monitoring of forest resources. Through GIS technology, a comprehensive assessment of resource conditions, environmental changes, etc. in different forest areas can be conducted, providing decision makers with real-time and accurate support for forestry resource management.

[0155] Due to the combination of sensors and remote sensing technology, this application can achieve real-time monitoring of the target forest area and can obtain key environmental indicators such as climate, soil, moisture, temperature, etc. in a timely manner. Compared with traditional manual survey methods, this application greatly improves the timeliness and accuracy of monitoring. This enables forestry resource managers to respond quickly to changes in forest resources and take timely measures to deal with possible ecological risks or resource losses. For example, in the event of a forest fire or insect infestation, the latest information on the forest area can be obtained through sensor data and remote sensing images in the first place, problem areas can be quickly located, and corresponding emergency measures can be taken.

[0156] Existing traditional forestry resource collection methods often seem inadequate when faced with large-scale forest areas and are unable to quickly cover large areas of forest land. In contrast, the system architecture of the present application has good scalability and adaptability. As the demand for forestry resource management increases, the system can quickly expand the monitoring range and maintain efficient operation by adding remote sensing equipment, sensor nodes and other components. In addition, the data transmission module can flexibly adapt to various geographical conditions and climatic environments according to changes in different environments when dynamically adjusting the transmission strategy. It can not only work efficiently in open areas, but also operate stably in complex and remote forest environments.

[0157] Through the deep integration and dimensionality reduction of remote sensing images and sensor data, this application can provide intelligent decision-making support for forestry resource management. The system can not only monitor forest changes in real time, but also automatically generate early warning information such as climate change, pest warning, resource anomalies, etc. through data analysis and pattern recognition. This enables forestry managers to identify potential problems in advance and take timely countermeasures, thereby effectively reducing the loss of forest resources and environmental risks.

[0158] In summary, the forestry resource collection method based on GIS technology proposed in this application has shown significant technological progress in many aspects compared to the existing technology. It realizes an efficient, accurate and real-time forestry resource collection and monitoring system by integrating advanced technologies such as remote sensing image acquisition, IoT sensor data acquisition, dynamic transmission strategy, high-dimensional data dimensionality reduction and integrated processing. This solution not only improves the efficiency and accuracy of forestry resource collection, but also provides intelligent support for real-time monitoring, data analysis and decision-making of forest resources, and has broad application prospects and practical significance.

[0159] Based on the same inventive concept as in the aforementioned embodiments, an embodiment of the present application further provides a computer-readable storage medium storing a computer program. When the computer program is loaded and executed by a processor, a forestry resource collection method based on GIS technology as provided in an embodiment of the present application is implemented.

[0160] Based on the same inventive concept as in the above-mentioned embodiment, an embodiment of the present application further provides an electronic device, including a processor and a memory, wherein:

[0161] The memory is used to store computer programs;

[0162] The processor is used to load and execute a computer program so that the electronic device executes a forestry resource collection method based on GIS technology as provided in an embodiment of the present application.

[0163] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or various devices including one or any combination of the above memories. The computer may be various computing devices including intelligent terminals and servers.

[0164] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0165] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0166] By way of example, executable instructions may be deployed to be executed on one computing device, or on multiple computing devices located at one site, or on multiple computing devices distributed across multiple sites and interconnected by a communication network.

[0167] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0168] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0169] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as a read-only memory / random access memory, a magnetic disk, or an optical disk), and includes a number of instructions for enabling a multimedia terminal device (which can be a mobile phone, a computer, a television receiver, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0170] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A forestry resource collection method based on GIS technology, characterized in that: Applied to a forestry resource acquisition system, the forestry resource acquisition system includes a remote sensing image acquisition module, a sensor data acquisition module, a data transmission module and a terminal device; the remote sensing image acquisition module is used to acquire images of target forest resources based on the remote sensing acquisition device, and transmit the images to the terminal device based on the data transmission module; the sensor data acquisition module refers to a group of IoT sensors deployed in the target forest area, which is used to acquire sensor data in real time and transmit the data to the terminal device based on the data transmission module; the data transmission module is used to transmit data and dynamically adjust the transmission strategy according to the data transmission environment; the terminal device includes a high-dimensional data dimensionality reduction module and a data integration module; The forestry resource collection method based on GIS technology comprises the following steps: Collecting a remote sensing image of a target forest area through a remote sensing device; selecting a first target transmission strategy based on the remote sensing image, and transmitting the remote sensing image based on the first target transmission strategy; By deploying IoT sensors in the target forest area, sensor data is collected and obtained; the sensor data includes real-time soil data, real-time moisture data and real-time temperature data; based on the sensor data, a second target transmission strategy is selected, and the sensor data is transmitted based on the second target transmission strategy; The acquired remote sensing images and sensor data are subjected to dimensionality reduction and integration processing to obtain target forestry resource data; the target forestry resource data includes original collected data and prediction result information generated based on the original collected data.

2. The forestry resource collection method based on GIS technology according to claim 1 is characterized in that: The step of performing dimensionality reduction processing and integration processing on the acquired remote sensing image and the sensor data to obtain target forestry resource data includes: Performing dimensionality reduction processing on the acquired remote sensing image and the sensor data and verifying whether the target information is retained after the dimensionality reduction processing, and if the verification is passed, obtaining low-dimensional data; Integrate the acquired remote sensing image and the sensor data to obtain integrated data; After the low-dimensional data and the integrated data are subjected to decision-level data fusion processing, they are input into a target prediction model to obtain target forestry resource data and prediction result information; The decision-level data fusion process satisfies the following relationship: fused =w low *D low +w full *D full ; Among them, D fused represents the data after decision-level data fusion processing, w low and w full are the weights of low-dimensional data and integrated data, respectively, w low +w full＝ 1;D low represents low-dimensional data, D full Indicates integrated data; The target prediction model satisfies the following relationship: in, Indicates the prediction result information, D fused represents the data after decision-level data fusion processing, θ represents the model parameters, and f(·) represents the prediction function; The loss function of the target prediction model is: in, Represents the predicted value, y i represents the true value, n represents the number of samples, represents the squared error of each sample.

3. The forestry resource collection method based on GIS technology according to claim 2 is characterized in that: The step of performing dimensionality reduction processing on the acquired remote sensing image and the sensor data to obtain low-dimensional data comprises: The remote sensing image is subjected to feature extraction based on a convolutional neural network to obtain remote sensing feature data; the remote sensing feature data and the sensor data are preprocessed to obtain first multidimensional data; data centering is performed based on the first multidimensional data to obtain a centralized data matrix; based on the centralized data matrix, a covariance matrix of the centralized data is calculated to obtain; the covariance matrix of the centralized data is subjected to eigenvalue decomposition to obtain eigenvalues ​​and eigenvectors; a preset number of target eigenvectors are selected to form a first target dimensionality reduction matrix; the first multidimensional data is projected onto the first target dimensionality reduction matrix to obtain first low-dimensional data; Verifying whether the first low-dimensional data retains the target information based on the three-dimensional scatter plot; if the verification is successful, using the first low-dimensional data as the target low-dimensional data; If the verification fails, performing a second dimensionality reduction process on the first multi-dimensional data based on a second dimensionality reduction strategy to obtain second low-dimensional data; the second dimensionality reduction process is a nonlinear dimensionality reduction process; Similarly, the target information in the second low-dimensional data is verified based on the three-dimensional scatter plot, so that the second low-dimensional data that passes the verification is used as the target low-dimensional data.

4. The forestry resource collection method based on GIS technology according to claim 2 is characterized in that: The step of integrating the acquired remote sensing image and the sensor data to obtain integrated data comprises: After performing time synchronization processing on the acquired remote sensing image and the sensor data by using interpolation processing, a plurality of image data pairs are obtained; the remote sensing image and the sensor data in the image data pairs have the same time point; The remote sensing image in each image data pair is converted into a feature vector based on a preset deep learning model, and the sensor data is converted into standard sensor data through Z-Score standardization processing to obtain a multidimensional data set; The eigenvector satisfies the following relationship: I =f CNN (I); where f CNN represents the preset deep learning model; f I represents the feature vector; I represents the input remote sensing image, and the dimension of I is (image height, image width, number of image channels); The standard sensor data satisfies the following relationship: Among them, μ S and σ S are respectively represented as the mean and standard deviation of the sensor data set, Represented as the i-th standard sensor data, S i Represents the i-th sensor data; The multidimensional data set is subjected to feature splicing processing to obtain integrated data.

5. The forestry resource collection method based on GIS technology according to claim 2 is characterized in that: The step of performing decision-level data fusion processing on the low-dimensional data and the integrated data to obtain target forestry resource data and prediction result information includes: The low-dimensional data is input into a first preset prediction model to obtain coverage information of the target forest area; the first preset prediction model includes an input layer, a hidden layer and an output layer; the input layer is used to receive the low-dimensional data; the hidden layer includes a plurality of neurons, and a nonlinear activation function is used to learn the relationship between the low-dimensional data and the coverage information of the target forest area; the output layer is used to output the coverage information of the target forest area; The integrated data is input into a second preset prediction model to obtain health status information, tree species distribution information and growth rate information of each tree species of the target forest area; the second preset prediction model includes an input layer, a hidden layer and an output layer; the input layer is used to receive the integrated data; the hidden layer includes 3 hidden sub-layers, and each hidden sub-layer extracts the features of the integrated data through a combination of weighted sum and activation function; the output layer includes 3 output sub-layers, which are respectively used to output health status information, tree species distribution information and growth rate information of each tree species of the target forest area.

6. The forestry resource collection method based on GIS technology according to claim 2 is characterized in that: After obtaining the target forestry resource data and prediction result information, the method further includes: Based on the coverage information, health information, tree species distribution information and growth rate information of each tree species in the target forest area, a forestry resource distribution map and a forestry resource prediction change map are generated to formulate forestry management target strategies.

7. The forestry resource collection method based on GIS technology according to claim 1 is characterized in that: The step of selecting a first target transmission strategy based on the remote sensing image and transmitting the remote sensing image based on the first target transmission strategy includes: Performing feature recognition processing on the remote sensing image to obtain feature information of the remote sensing image; Based on the size of the remote sensing image, a first constraint is selected; the first constraint is a first target compression ratio, a first target bandwidth value and a first target number of transmissions; based on the content of the remote sensing image, a second constraint is selected; the second constraint is a first target resolution and whether data compression is performed; based on the transmission environment, a third constraint is selected; the third constraint is the first target transmission path information; based on the transmission task of the remote sensing image, a fourth constraint is selected; the fourth constraint is the first target transmission protocol; Based on the first restriction condition, the second restriction condition, the third restriction condition and the fourth restriction condition, a first target transmission strategy is obtained, and the remote sensing image is transmitted based on the first target transmission strategy.

8. The forestry resource collection method based on GIS technology according to claim 7 is characterized in that: The first limiting condition is selected based on the size of the remote sensing image; the first limiting condition is a compression ratio, a bandwidth value and a number of transmission times; Based on the content of the remote sensing image, a second restriction condition is selected; the second restriction condition is resolution and whether data compression is performed; based on the transmission environment, a third restriction condition is selected; the third restriction condition is transmission path information; Based on the transmission task of the remote sensing image, a fourth restriction condition is selected; the fourth restriction condition is a step of the transmission protocol, including: If the remote sensing image is a low-resolution image or a single-band image, the first restriction condition is a low compression ratio and a low bandwidth value and a single data transmission; if the remote sensing image is a high-resolution multispectral image or a hyperspectral image, the first restriction condition is a high compression ratio and a high bandwidth value and a multiple data transmission; If there are important geographical areas or abnormal areas in the remote sensing image, the second restriction condition is regional high-resolution transmission; if there are a large number of blank areas or repeated areas in the remote sensing image, redundant information in the image is used to perform data compression; If the transmission path of the remote sensing image is long and the transmission network is congested, the third restriction condition is a multi-path transmission strategy; if the transmission path of the remote sensing image is short or the transmission network is not congested, the third restriction condition is a single-path transmission strategy; If the transmission task of the remote sensing image is disaster monitoring, the transmission protocol is the User Datagram Protocol; if the transmission task of the remote sensing image is periodic and offline monitoring, the transmission protocol is the Transmission Control Protocol.

9. The forestry resource collection method based on GIS technology according to claim 1 is characterized in that: The step of selecting a second target transmission strategy based on the sensor data and transmitting the sensor data based on the second target transmission strategy includes: Outputting the sensor data to a preset data classification model for data classification processing, and obtaining data classification values ​​of the sensor data respectively; the data classification values ​​include a high priority value, a medium priority value, and a low priority value; Based on the data classification value, selecting a second target transmission strategy; The sensor data is transmitted based on the second target transmission strategy.

10. The forestry resource collection method based on GIS technology according to claim 9 is characterized in that: The step of selecting a second target transmission strategy based on the data classification value comprises: For data with a high priority value, the user datagram protocol is used for transmission; For data with a data classification value of medium priority, the transmission control protocol is used for transmission; For data with a data classification value of a low priority value, message queue telemetry transmission is used for transmission.

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