Forest region visual management system based on Internet of Things technology

Abstract

The invention discloses a forest region visualization management system based on the Internet of Things technology. The forest region visualization management system comprises an Internet of Things data acquisition module, a data transmission and compression module, an edge calculation and preprocessing module, a central processing system, a WebGIS visualization module and a load balancing and network security module. The Internet of Things data acquisition module is used for acquiring forest region information in real time; the data transmission and compression module is used for transmitting the data to the central processing system; the edge calculation and preprocessing module is used for screening and preprocessing the data; the central processing system is used for receiving, storing and processing data and generating visual management information; the WebGIS visualization module is used for receiving and displaying the visualization management information; the load balancing and network security module optimizes the network performance through a load balancing mechanism, and enhances the network security protection. According to the invention, accurate and efficient acquisition of forest region data can be realized, three-dimensional visualization and dynamic monitoring of forest region resources are provided, and the use experience and operation convenience of a user are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of forest management, and in particular to a forest visualization management system based on Internet of Things technology. Background Art

[0002] In traditional forest management, forest resource information is mainly obtained through on-site inspections. Although this method can ensure the accuracy of information, it is not only time-consuming and labor-intensive, but also highly dangerous in the face of a wide range of forests with complex terrain. In addition, traditional inspection methods often make it difficult to grasp the dynamic changes in forests in a timely manner, resulting in a lack of timeliness and scientificity in management decisions.

[0003] With the rapid development of IoT technology, more and more fields are trying to apply it to actual management. In the field of forest management, IoT technology can collect various data of forest areas in real time through sensors and other equipment, such as temperature, humidity, soil conditions, and tree growth. These data provide rich information support for forest management and help make more scientific and reasonable management decisions.

[0004] As a powerful spatial information processing tool, Geographic Information System (GIS) also plays an important role in forest management. Through GIS technology, the topography, vegetation distribution and other information of forest areas can be digitized to form intuitive maps and images. However, traditional GIS systems often have problems such as untimely data updates and poor visualization effects, which are difficult to meet the needs of modern forest management.

[0005] In order to overcome the shortcomings of traditional forest management, the present invention proposes a forest visualization management system based on the Internet of Things technology and GIS technology, combining the advantages of the Internet of Things technology and GIS technology. The system collects forest data in real time through Internet of Things devices, and uses GIS technology for data processing and visualization, so as to realize comprehensive monitoring and management of forest resources. Summary of the invention

[0006] The present invention provides a forest area visualization management system based on the Internet of Things technology to solve the technical problems of insufficient timeliness and accuracy of existing information acquisition and low management efficiency.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a forest area visualization management system based on Internet of Things technology, comprising: Internet of Things data acquisition module, data transmission and compression module, edge computing and preprocessing module, central processing system, WebGIS visualization module, load balancing and network security module; The IoT data collection module is used to collect environmental parameters and forest resource information of the forest area in real time through sensor devices arranged in the forest area; The data transmission and compression module is used to transmit the collected and processed data to the central processing system, and reduce the amount of data required for transmission through data compression and coding optimization technology; The edge computing and preprocessing module is arranged at a key location in the forest area and is used to perform preliminary screening and preprocessing of data transmitted by the data transmission and compression module; The central processing system includes a data receiving unit, a data storage unit and a data processing unit, which are used to receive, store and process data from the data transmission and compression module to generate visual management information of the forest area; The WebGIS visualization module is connected to the central processing system and is used to receive and display the visualization management information generated by the central processing system; The load balancing and network security module optimizes network performance through a load balancing mechanism, strengthens network security protection, and ensures the security of data transmission and storage.

[0008] The beneficial effects brought about by the technical solution provided by the present invention include at least: The forest area visualization management system based on the Internet of Things technology of the present invention comprises an Internet of Things data acquisition module, a data transmission and compression module, an edge computing and preprocessing module, a central processing system, a WebGIS visualization module, and a load balancing and network security module; the Internet of Things data acquisition module is used for collecting environmental parameters and forest resource information of the forest area in real time through sensor equipment arranged in the forest area; the data transmission and compression module is used for transmitting the collected and processed data to the central processing system, and reducing the amount of data required for transmission through data compression and coding optimization technology; the edge computing and preprocessing module is arranged at a key position in the forest area, and is used for preliminary screening and preprocessing of the data transmitted by the data transmission and compression module; the central processing system comprises a data receiving unit, a data storage unit and a data processing unit, and is used for receiving, storing and processing data from the data transmission and compression module, and generating visualization management information of the forest area; the WebGIS visualization module is connected to the central processing system, and is used for receiving and displaying the visualization management information generated by the central processing system; the load balancing and network security module optimizes network performance through a load balancing mechanism, strengthens network security protection, and ensures the security of data transmission and storage.

[0009] The present invention can realize the accurate and efficient collection of real-time data in the forest area through the Internet of Things data collection module, provide comprehensive and detailed data support for forest area management, and ensure the timeliness and accuracy of information.

[0010] The present invention can realize fast and low-loss transmission of collected data through the data transmission and compression module, effectively reduce the data transmission volume, improve transmission efficiency, and reduce network load and transmission cost.

[0011] Through edge computing and preprocessing modules, the present invention can realize local processing of forest area data. The edge device can analyze the monitoring data in real time, promptly detect abnormal situations and trigger early warnings, reducing the delay in uploading data to the central processing system and improving the response speed and accuracy of the system.

[0012] The present invention can process massive amounts of data efficiently through a central processing system, mine the correlation and regularity between data, and provide a scientific basis and decision-making support for forest management.

[0013] The present invention can realize three-dimensional visualization and dynamic monitoring of forest resources through the WebGIS visualization module, provide an intuitive and convenient visualization tool for the planning, protection and utilization of forest areas, and improve the user experience and operational convenience.

[0014] The present invention uses load balancing and network security modules to ensure that the system can still operate stably under high concurrency conditions, effectively prevent security risks such as network attacks and data leakage, and provide strong guarantees for the safe transmission and storage of forest data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 It is a schematic diagram of the system module structure provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0018] First Embodiment This embodiment provides a forest area visualization management system based on the Internet of Things technology, including: an Internet of Things data acquisition module, a data transmission and compression module, an edge computing and preprocessing module, a central processing system, a WebGIS visualization module, and a load balancing and network security module; The IoT data collection module is used to collect the environmental parameters and forest resource information of the forest area in real time through the sensor equipment arranged in the forest area; The data transmission and compression module is used to transmit the collected and processed data to the central processing system, and reduce the amount of data required for transmission through data compression and coding optimization technology; The edge computing and preprocessing modules are arranged at key locations in the forest area to perform preliminary screening and preprocessing of data transmitted by the data transmission and compression modules; The central processing system includes a data receiving unit, a data storage unit and a data processing unit, which are used to receive, store and process data from the data transmission and compression module to generate visual management information of the forest area; The WebGIS visualization module is connected to the central processing system and is used to receive and display the visualization management information generated by the central processing system; The load balancing and network security module optimizes network performance through load balancing mechanism, strengthens network security protection, and ensures the security of data transmission and storage.

[0019] Please refer to Figure 1 It is a schematic diagram of the system module structure provided by an embodiment of the present invention.

[0020] For the forest area visualization management system based on the Internet of Things technology in this embodiment, its hardware deployment should include an Internet of Things data acquisition module, a data transmission and compression module, an edge computing and preprocessing module, a central processing system, a WebGIS visualization module, and a load balancing and network security module, among which: The data transmission and compression module, central processing system, WebGIS visualization module, load balancing and network security module are deployed on the server side; IoT data collection module, edge computing and preprocessing module are deployed on the terminal; The central processing system is the core module, and the IoT data acquisition module, data transmission and compression module, edge computing and preprocessing module, WebGIS visualization module, and load balancing and network security module together constitute other auxiliary modules; 1. IoT data collection module: The IoT data collection module deploys a variety of sensor devices in the forest area to collect forest area data in real time. The sensors include temperature sensors, humidity sensors, light intensity sensors, soil moisture sensors, tree growth monitoring sensors, and animal activity monitoring sensors. The IoT data collection module is also equipped with a weather station, a camera, and a GPS positioning device to assist in collecting environmental parameters and geographic location information of the forest area; The IoT data collection module can collect the environmental parameters of the forest area in real time, including temperature and humidity, light intensity, soil conditions, and meteorological information in the forest area obtained through the weather station; The IoT data collection module can collect forest resource data in real time, including tree species, quantity, height, breast diameter and crown width; The IoT data acquisition module includes image and video acquisition functions. Through cameras installed in the forest area, it captures dynamic images of the forest area in real time, providing intuitive information for forest safety monitoring, wildlife protection and forest fire warning.

[0021] It should be noted that temperature sensors are used to monitor the temperature conditions in forest areas, which helps to understand the climate conditions and plant growth environment in forest areas. Temperature changes have a direct impact on the physiological processes of tree growth, flowering, and fruiting.

[0022] Humidity sensors are used to measure air humidity in forest areas. Humidity is an important factor affecting plant growth. Too high humidity may lead to the breeding of pests and diseases, while too low humidity may affect the water absorption and transpiration of plants.

[0023] Light intensity sensors are used to monitor light intensity in forest areas. Light is an important condition for plants to carry out photosynthesis. Changes in light intensity will affect the growth rate and health of plants.

[0024] Soil moisture sensors are used to measure the moisture content in the soil, which helps to understand the soil moisture conditions and the water supply to the plant roots. The adequacy of soil moisture directly affects the growth and yield of plants.

[0025] Tree growth monitoring sensors are used to monitor the growth of trees and help assess the growth and ecological health of forest areas.

[0026] Animal activity monitoring sensors are used to monitor the activities of animals in forest areas, including the species, number, and activity tracks of animals. They are of great significance for wildlife protection and ecological balance maintenance.

[0027] The weather station can provide meteorological information in the forest area, including temperature, humidity, air pressure, wind speed and wind direction, which helps to understand climate change in the forest area and issue early warnings for meteorological disasters.

[0028] The camera is used to capture dynamic images of the forest area in real time, providing intuitive information for forest safety monitoring, wildlife protection and forest fire warning. The camera can be installed in key locations, including forest entrances and important vegetation areas.

[0029] The GPS positioning device is used to determine the geographical location information within the forest area, which helps to accurately monitor and record the location of sensors and collected data.

[0030] 2. Data transmission and compression module: The data compression algorithm used by the data transmission and compression module can identify and remove redundant information in the data. For continuous and slightly changing environmental parameters, only the data points with significant changes are recorded, thereby reducing the volume of data and saving network bandwidth. The data transmission and compression module applies coding optimization technology. Based on information entropy theory, it further reduces the data size by reducing the average length of data representation, ensuring that more valid data can be transmitted under limited transmission resources. The specific formula is:

[0031] in, is the information entropy, is the probability that the random variable X takes a particular value x; The data transmission and compression module takes security measures during the transmission process, including data encryption and integrity verification, to ensure the confidentiality, integrity and availability of the data and prevent the data from being unauthorized access, tampering or loss during the transmission process.

[0032] It should be noted that the determination of significantly changed data points is usually based on a certain threshold and a change range is set. When the change of a data point exceeds this range, it is considered to be a significantly changed point and needs to be recorded.

[0033] Information entropy is an indicator to measure information uncertainty. In information theory, the larger the entropy, the higher the uncertainty of the information; conversely, the smaller the entropy, the lower the uncertainty of the information. Coding optimization technology based on information entropy theory can further reduce the data size by reducing the average length of data representation. For data with a higher probability of occurrence, a shorter code is used to represent it; and for data with a lower probability of occurrence, a longer code is used to represent it.

[0034] Data encryption is an important means to ensure data confidentiality. By encrypting the data, it can be ensured that even if the data is intercepted during transmission, it cannot be decrypted and read by unauthorized users. The data encryption algorithm selects an asymmetric encryption algorithm (RSA).

[0035] Integrity verification is used to ensure that data has not been tampered with or damaged during transmission. The integrity verification methods include hash verification and digital signature. Hash verification verifies the integrity of data by calculating the hash value of the data. If the data is tampered with during transmission, its hash value will also change, which can be detected. Digital signature signs data with a private key and verifies it with a public key. Only users with private keys can generate valid digital signatures to ensure the source and integrity of the data.

[0036] 3. Edge computing and preprocessing module: The edge computing and preprocessing module has real-time data analysis capabilities and can monitor and analyze environmental parameters in the forest area in real time. When abnormal data is detected, the early warning mechanism is immediately triggered and an emergency notification is sent to the central management system. Abnormal data includes fire hazards and outbreaks of pests and diseases. The edge computing and preprocessing modules perform data cleaning and denoising operations, filter out invalid data, duplicate data, and environmental noise data through algorithms, and can perform preliminary data formatting to ensure the quality of data transmitted to the central management system.

[0037] It should be noted that the core advantage of the edge computing and preprocessing module lies in its ability to process environmental parameter data within the forest area in real time. Once the data is generated, it can be captured and analyzed immediately, thereby greatly shortening the response time.

[0038] Fire hazards are identified quickly by analyzing environmental parameters, including sharp temperature rises and increased smoke concentrations, and potential fire hazards can be quickly identified and immediate action can be taken.

[0039] Pest and disease outbreaks can be predicted and detected by monitoring the health of forest vegetation, including leaf color changes and slowed growth, providing valuable time for timely prevention and control.

[0040] Identifying and filtering out outliers caused by equipment failure or data transmission errors can improve data quality and ensure that data transmitted to the central management system is more accurate and reliable; deduplication processing is to identify and remove duplicate data records to avoid data redundancy and repeated analysis; noise reduction processing is for environmental noise data, including data fluctuations caused by electromagnetic interference, weather changes, etc., which will be effectively removed by the module, thereby improving data purity and analysis accuracy.

[0041] The edge computing and preprocessing modules perform preliminary formatting on the data, including data format conversion and timestamp alignment, to ensure the consistency and readability of the data during transmission and storage. The data that has undergone preliminary formatting will be easier to be received and analyzed by the central management system, thereby improving the efficiency and accuracy of the overall system.

[0042] 4. Central processing system: The data receiving unit of the central processing system has high concurrent processing capabilities, can simultaneously receive data streams from multiple data transmission and compression modules, and ensure the integrity of data reception; The data storage unit of the central processing system adopts a distributed storage architecture, which stores data in multiple storage nodes to improve data access speed and support data backup and recovery functions; The data processing unit of the central processing system analyzes the received data to obtain the changes in forest resources and generate visual management information of the forest area. The visual management information includes forest resource distribution maps, growth trend forecasts, and pest and disease warnings.

[0043] The steps for the central processing system to analyze forest resource changes include: S1: retrieve remote sensing images and ground observation data of forest areas at different time periods to ensure data integrity and time continuity; S2: Perform geometric correction, terrain correction, radiation calibration, atmospheric correction, denoising and enhancement on remote sensing images and ground observation data; S3: Extract spectral features and texture features. Texture features include energy, entropy, and contrast. The specific formula is:

[0044] in: Represents energy, represents the entropy value, represents contrast, Represents the probability of a pixel with gray level i being away from another pixel with gray level j, i,j=(1,2,...,L), i,j,L represents the gray level of the image.

[0045] S4: Use convolutional neural network to build a classifier to identify the type of forest land use. The specific formula is:

[0046] in, Represents the output layer output; represents the first The remote sensing image features of neurons; N and M represent the number of neurons in the input layer and the number of neurons in the hidden layer respectively; , and , They represent the connection weights and thresholds between the input layer neuron b and the hidden layer neuron k, and between the hidden layer neuron k and the output layer neuron a, respectively; f( ) is the activation function;

[0047] S5: Classify and identify remote sensing images of different time periods to obtain the land use type distribution of each time period, compare the land use type distribution of different time periods, and detect the change type, change area and change rate of the forest area.

[0048] It should be noted that high concurrent processing capability refers to the system's ability to handle a large number of requests or data streams at the same time. Multi-threading technology allows the system to process multiple tasks at the same time without affecting the execution of other tasks due to the blocking of a certain task.

[0049] Distributed storage architecture is a storage method that stores data in a dispersed manner on multiple physical nodes. The nodes are interconnected through a network to form a logical whole, which improves the access speed of data because data can be read from multiple nodes in parallel. Since data is stored in a dispersed manner, the fault tolerance and scalability of the system are also improved.

[0050] Data backup refers to copying data to other storage media or locations to prevent data loss; data recovery refers to the process of restoring data from a backup when the data is lost or damaged. Data backup is achieved through regular automatic backup, manual backup, and off-site backup. Data recovery is usually accomplished by restoring the backup file.

[0051] Geometric correction: ensure the consistency of the image with the actual geographic coordinates; terrain correction: reduce the interference of terrain on image interpretation; radiation calibration: convert image grayscale or brightness into physical radiation intensity; atmospheric correction: eliminate the influence of atmospheric conditions on image clarity; denoising: improve the signal-to-noise ratio of the image and reduce noise interference; enhancement processing: enhance the contrast and brightness characteristics of the image.

[0052] Spectral features include reflectance or radiation intensity in different bands; texture features are extracted, including energy, entropy, and contrast, to describe the grayscale distribution and complexity of the image.

[0053] Visual management information refers to information that displays the results of data analysis in the form of intuitive graphics, charts, and maps, allowing forestry managers to more intuitively understand the distribution and status of forest resources and make more accurate decisions.

[0054] The forest resource distribution map can clearly show the quantity and type of forest resources in different regions; the growth trend forecast map can predict the growth of forest resources in the future; the pest and disease warning map can timely detect and deal with potential pest and disease problems.

[0055] 5. WebGIS visualization module: The WebGIS visualization module uses the mapping tool VirtualGIS Viewer, combined with remote sensing images and visual management information of forest areas, to draw a three-dimensional map of the forest area through a three-dimensional modeling algorithm, thus realizing a three-dimensional visualization display of forest resource information in the forest area; When executing 3D map drawing, the WebGIS visualization module first retrieves the elevation value of the forest area from the data storage unit, calculates the terrain grid spacing according to the maximum and minimum height values, and then retrieves the 2D map for elevation interpolation calculation, accurately inserts the elevation data into the 2D map to form a continuous terrain surface, and realizes the construction of 3D terrain by constructing a triangular mesh model; The WebGIS visualization module has a texture rendering function, which can extract texture features from the acquired remote sensing images, apply texture information to the 3D terrain model using the Scene Properties tool in WebGIS, and adjust the lighting and shadows according to the characteristics of the forest area and display requirements to enhance the realism and visual effects of the 3D map; The formula for calculating terrain grid spacing in the WebGIS visualization module is:

[0056] Where s is the terrain grid spacing, W represents the forest area, is the maximum height value, is the minimum height value; The formula for the WebGIS visualization module to retrieve the two-dimensional map for elevation interpolation calculation is:

[0057] in, is the elevation value of the point o to be inserted, is the elevation value of the u-th reference point, is the weight of the u-th reference point, e is the number of reference points;

[0058] The WebGIS visualization module enables users to intuitively view the resource distribution, growth status and environmental change information of the forest area through a visualization interface, and provides interactive functions that allow users to perform zooming, rotating and panning operations.

[0059] It should be noted that VirtualGIS Viewer is a specially designed mapping tool that integrates multiple GIS functions, including map rendering, data analysis and visualization. In the WebGIS visualization module, it is responsible for integrating and displaying remote sensing images and forest management information.

[0060] Elevation interpolation is a method of inferring the elevation value of an unknown point through the elevation value of a known point. Commonly used interpolation methods include linear interpolation, bilinear interpolation, and nearest neighbor interpolation.

[0061] Texture extraction is the extraction of texture features from remote sensing images, using image processing technology to identify and extract useful texture information; in order to enhance the realism and visual effects of three-dimensional maps, it is necessary to adjust the lighting and shadow effects, which is achieved by adjusting the position, intensity and color of the light source. The generation and rendering of shadows can increase the three-dimensionality and layering of the map.

[0062] The WebGIS visualization module provides rich user interaction functions through the visualization interface, and can further optimize the design and operation process of the interactive interface, including increasing the zoom level of the map, providing multiple view modes (bird's eye view, top view), and adding map marking and annotation functions.

[0063] 6. Load balancing and network security module: The load balancing and network security module can monitor the current load and processing capacity of V servers in the system in real time, and calculate the load balancing index of each server as the basis for dynamically allocating new data requests. The specific formula is:

[0064] in, is the current load of the fth server, is the processing capacity of the fth server, is the load balancing index of the fth server; When receiving a new data request, the load balancing and network security module will automatically select the server with the smallest LBI to process the request, ensuring that the load on each server is even, improving the overall processing speed and response time; The load balancing and network security modules integrate network security protection measures, including firewall technology, intrusion detection system and data encryption technology, to ensure the security of data transmission and storage, and prevent data leakage and illegal access.

[0065] It should be noted that the load balancing module can monitor the current load and processing capacity of each server in the system in real time. It is achieved by collecting the server's performance indicators (CPU utilization, memory usage, disk I / O). Through load balancing, the system can more effectively utilize server resources, improve overall processing speed and response time, and help improve user experience.

[0066] The load balancing module calculates the load balancing index (LBI) of each server. This index is a comprehensive indicator that reflects the current load of the server relative to its processing capacity. When a new data request arrives, the load balancing module automatically selects the most suitable server to handle the request based on the LBI. This ensures that the load is evenly distributed among the servers and avoids the situation where some servers are overloaded while other servers are idle.

[0067] Firewalls are the first line of defense for network security. They are used to monitor and control data flows in and out of the network. They can prevent unauthorized access and data leakage, and protect internal networks from external threats. Firewall technology includes deep packet inspection and behavioral analysis functions, which can accurately identify and block malicious traffic.

[0068] Intrusion detection systems (IDS) can monitor network activities in real time, detect and report suspicious behaviors, help organizations detect and respond to security incidents in a timely manner, and prevent potential damage from occurring. They can detect intrusion behaviors based on signatures (known attack patterns) or anomalies (non-conformance to normal behavior patterns).

[0069] Data encryption is an important means to protect the security of data transmission and storage. By encrypting sensitive data, it can ensure that even if the data is intercepted during transmission or illegally accessed during storage, it cannot be easily decrypted and read.

[0070] The forest area visualization management system based on the Internet of Things technology in this embodiment realizes comprehensive, real-time, visual monitoring and management of forest area resources through the collaborative work of the Internet of Things data acquisition module, edge computing and preprocessing module, data transmission module, central processing system, WebGIS visualization module and user interface module.

[0071] In addition, it should be noted that the present invention can be provided as a method, an apparatus or a computer program product. Therefore, the embodiments of the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0072] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0073] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable terminal device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0074] It should also be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. The terms "include", "comprises" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device 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 terminal device. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0075] Finally, it should be noted that the above is a preferred embodiment of the present invention. It should be pointed out that although the preferred embodiment of the present invention has been described, for those skilled in the art, once the basic creative concept of the present invention is known, several improvements and modifications can be made without departing from the principles of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention. Therefore, the attached claims are intended to be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the embodiments of the present invention.

Claims

1. A visual management system for forest areas based on Internet of Things technology, characterized in that: include: Internet of Things data acquisition module, data transmission and compression module, edge computing and preprocessing module, central processing system, WebGIS visualization module, load balancing and network security module; The IoT data collection module is used to collect environmental parameters and forest resource information of the forest area in real time through sensor devices arranged in the forest area; The data transmission and compression module is used to transmit the collected and processed data to the central processing system, and reduce the amount of data required for transmission through data compression and coding optimization technology; The edge computing and preprocessing module is arranged at a key location in the forest area and is used to perform preliminary screening and preprocessing of data transmitted by the data transmission and compression module; The central processing system includes a data receiving unit, a data storage unit and a data processing unit, which are used to receive, store and process data from the data transmission and compression module to generate visual management information of the forest area; The WebGIS visualization module is connected to the central processing system and is used to receive and display the visualization management information generated by the central processing system; The load balancing and network security module optimizes network performance through a load balancing mechanism, strengthens network security protection, and ensures the security of data transmission and storage.

2. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The Internet of Things data acquisition module, data transmission and compression module, edge computing and preprocessing module, central processing system, WebGIS visualization module, load balancing and network security module, among which: The data transmission and compression module, central processing system, WebGIS visualization module, load balancing and network security module are deployed on the server side; The IoT data acquisition module, edge computing and preprocessing module are deployed in the terminal; The central processing system is the core module, and the Internet of Things data acquisition module, data transmission and compression module, edge computing and preprocessing module, WebGIS visualization module and load balancing and network security module together constitute other auxiliary modules.

3. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The IoT data collection module is used to collect forest resource data of the forest area in real time through sensor devices arranged in the forest area, wherein: The IoT data collection module arranges a variety of sensor devices in the forest area to collect forest area data in real time. The sensors include temperature sensors, humidity sensors, light intensity sensors, soil moisture sensors, tree growth monitoring sensors, and animal activity monitoring sensors. The IoT data collection module is also equipped with a weather station, a camera and a GPS positioning device to assist in collecting environmental parameters and geographical location information of the forest area; The IoT data collection module can collect environmental parameters of the forest area in real time, including temperature and humidity, light intensity, soil conditions, and meteorological information in the forest area obtained through a weather station; The IoT data collection module can collect forest resource data of the forest area in real time, and the forest resource data includes the type, quantity, height, breast diameter and crown width of trees; The IoT data acquisition module includes image and video acquisition functions, which captures dynamic images of the forest area in real time through cameras installed in the forest area, providing intuitive information for forest area safety monitoring, wildlife protection and forest fire warning.

4. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The data transmission and compression module is used to transmit the collected and processed data to the central processing system, and reduce the amount of data required for transmission through data compression and coding optimization technology, wherein: The data compression algorithm used by the data transmission and compression module can identify and remove redundant information in the data. For continuous and slightly changing environmental parameters, only the data points with significant changes are recorded, thereby reducing the volume of data and saving network bandwidth. The data transmission and compression module applies coding optimization technology, based on information entropy theory, to further reduce the data size by reducing the average length of data representation, ensuring that more valid data can be transmitted under limited transmission resources. The specific formula is: in, is the information entropy, is the probability that the random variable X takes a particular value x; The data transmission and compression module takes security measures during the transmission process, including data encryption and integrity verification, to ensure the confidentiality, integrity and availability of the data and prevent the data from being accessed, tampered with or lost without authorization during the transmission process.

5. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The edge computing and preprocessing module is arranged at a key position in the forest area, and is used to perform preliminary screening and preprocessing on the data collected by the IoT data acquisition module, wherein: The edge computing and preprocessing module has real-time data analysis capabilities, and can monitor and analyze environmental parameters in the forest area in real time. When abnormal data is detected, the early warning mechanism is immediately triggered and an emergency notification is sent to the central management system. The abnormal data includes fire hazards and outbreaks of pests and diseases. The edge computing and preprocessing module performs data cleaning and denoising operations, filters out invalid data, duplicate data and environmental noise data through algorithms, and can perform preliminary formatting of the data to ensure the quality of data transmitted to the central management system.

6. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The central processing system includes a data receiving unit, a data storage unit and a data processing unit, which are used to receive, store and process data from the data transmission and compression module to generate visual management information of the forest area, wherein: The data receiving unit of the central processing system has high concurrent processing capability, can simultaneously receive data streams from multiple data transmission and compression modules, and ensure the integrity of data reception; The data storage unit of the central processing system adopts a distributed storage architecture to disperse and store data on multiple storage nodes, thereby improving the data access speed and supporting data backup and recovery functions; The data processing unit of the central processing system analyzes the received data to obtain the change of forest resources and generate visual management information of the forest area, wherein the visual management information includes forest resource distribution map, growth trend forecast, and pest warning; The steps of analyzing forest resource changes in the central processing system include: S1: retrieve remote sensing images and ground observation data of forest areas at different time periods to ensure data integrity and time continuity; S2: Perform geometric correction, terrain correction, radiation calibration, atmospheric correction, denoising and enhancement on remote sensing images and ground observation data; S3: Extract spectral features and texture features. The texture features include energy, entropy, and contrast. The specific formula is: in: Represents energy, represents the entropy value, represents contrast, represents the probability of a pixel with gray level i being close to another pixel with gray level j, i,j=(1,2,...,L), i,j, L represents the gray level of the image; S4: Use convolutional neural network to build a classifier to identify the type of forest land use. The specific formula is: in, Represents the output layer output; represents the first The remote sensing image features of neurons; N and M represent the number of neurons in the input layer and the number of neurons in the hidden layer respectively; , and , They represent the connection weights and thresholds between the input layer neuron b and the hidden layer neuron k, and between the hidden layer neuron k and the output layer neuron a, respectively; f( ) is the activation function; S5: Classify and identify remote sensing images of different time periods to obtain the land use type distribution of each time period, compare the land use type distribution of different time periods, and detect the change type, change area and change rate of the forest area.

7. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The WebGIS visualization module is connected to the central processing system and is used to receive and display the visualization management information generated by the central processing system, wherein: The WebGIS visualization module uses the mapping tool VirtualGIS Viewer, combines remote sensing images and visual management information of the forest area, and draws a three-dimensional map of the forest area through a three-dimensional modeling algorithm to achieve a three-dimensional visual display of forest resource information in the forest area; When executing three-dimensional map drawing, the WebGIS visualization module first retrieves the elevation value of the forest area from the data storage unit, calculates the terrain grid spacing according to the maximum and minimum height values, and then retrieves the two-dimensional map to perform elevation interpolation calculation, accurately inserts the elevation data into the two-dimensional map to form a continuous terrain surface, and realizes the construction of three-dimensional terrain by constructing a triangular mesh model; The WebGIS visualization module has a texture rendering function, which can extract texture features from the acquired remote sensing images, apply texture information to the three-dimensional terrain model using the Scene Properties tool in WebGIS, and adjust lighting and shadows according to the characteristics of the forest area and display requirements to enhance the realism and visual effects of the three-dimensional map; The WebGIS visualization module enables users to intuitively view the resource distribution, growth status and environmental change information of the forest area through a visualization interface, and provides interactive functions, allowing users to perform zooming, rotating and panning operations.

8. The forest area visualization management system based on Internet of Things technology as claimed in claim 7, characterized in that: The WebGIS visualization module retrieves the elevation value of the forest area from the data storage unit, calculates the terrain grid spacing according to the maximum and minimum height values, and then retrieves the two-dimensional map for elevation interpolation calculation, wherein: The formula for calculating terrain grid spacing in the WebGIS visualization module is: Where s is the terrain grid spacing, W represents the forest area, is the maximum height value, is the minimum height value; The WebGIS visualization module uses the formula for obtaining the two-dimensional map to perform elevation interpolation calculation as follows: in, is the elevation value of the point o to be inserted, is the elevation value of the u-th reference point, is the weight of the u-th reference point, and e is the number of reference points.

9. The forest area visualization management system based on Internet of Things technology as claimed in claim 1, characterized in that: The load balancing and network security module optimizes network performance through a load balancing mechanism, strengthens network security protection, and ensures the security of data transmission and storage, wherein: The load balancing and network security module can monitor the current load and processing capacity of V servers in the system in real time, and calculate the load balancing index of each server as the basis for dynamically allocating new data requests. The specific formula is: in, is the current load of the fth server, is the processing capacity of the fth server, is the load balancing index of the fth server; When the load balancing and network security module receives a new data request, it will automatically select the server with the smallest LBI to process the request, ensuring that the load on each server is even and improving the overall processing speed and response time; The load balancing and network security module integrates network security protection measures, which include firewall technology, intrusion detection system and data encryption technology to ensure the security of data transmission and storage and prevent data leakage and illegal access.

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