Forest grass three-dimensional visual classification monitoring system based on virtual reality and AI identification

Abstract

The invention relates to the field of forest grass resource monitoring and management, and discloses a forest grass three-dimensional visual classification monitoring system based on virtual reality and AI recognition, and the system comprises a data collection module which is used for collecting data from a plurality of sensors, wherein the sensor comprises an unmanned aerial vehicle remote sensing image, a laser radar, an environment monitoring sensor and a climate and ecological data source; the data preprocessing module is used for carrying out denoising, interpolation, format conversion and standardization processing on the collected data; and an adaptive matrix optimization and multi-scale deep learning model module. By integrating the unmanned aerial vehicle remote sensing image, the laser radar, the environment monitoring sensor and the climate ecological data source, the system can comprehensively collect high-resolution and accurate forest and grass resource data, and the monitoring comprehensiveness and accuracy are improved. And the data preprocessing module ensures that a high-quality data basis is provided for subsequent data fusion and analysis through de-noising, interpolation and standardization processing.

Description

Technical Field

[0001] The present invention relates to the field of forest and grassland resource monitoring and management, and specifically to a three-dimensional visual classification monitoring system for forest and grassland based on virtual reality and AI recognition. Background Art

[0002] With the growing demand for environmental protection and ecological restoration, the monitoring and management of forest and grassland resources has become a crucial research area. Existing methods for monitoring forest and grassland resources primarily rely on traditional manual surveys, remote sensing image analysis, and ground-based sensors. While these technologies can provide essential monitoring data to a certain extent, several challenges remain.

[0003] First, traditional manual survey methods are labor-intensive, time-consuming, and have limited accuracy. Especially in large-scale forest and grassland resource management, manual surveys often fail to cover all areas, resulting in incomplete and untimely data collection. Furthermore, manual surveys are susceptible to human factors, making it difficult to ensure the objectivity and consistency of the data.

[0004] Secondly, remote sensing technology has been widely used in forest and grassland resource monitoring. However, due to limitations in image resolution, weather conditions during acquisition, and the complexity of processing algorithms, the accuracy of remote sensing images often fails to meet the needs of refined management. This is especially true in areas with dense vegetation or complex terrain, where traditional remote sensing technology struggles to accurately capture detailed information, hindering precise monitoring and management of resources.

[0005] Furthermore, LiDAR technology, a commonly used method for collecting ground-based point cloud data, offers high accuracy in forest resource monitoring. However, due to the complex data processing and high computing power required, LiDAR data fusion and subsequent analysis face significant challenges. Existing systems often fail to effectively fuse data from different sensors, limiting monitoring accuracy and comprehensive analysis capabilities.

[0006] Furthermore, existing monitoring systems still rely heavily on traditional, single-algorithm data processing and analysis, lacking intelligent data analysis and decision-making support. For complex forest and grassland resource environments, these systems struggle to extract comprehensive features and intelligently classify data from multiple sources and scales, further impacting the accuracy and timeliness of monitoring results.

[0007] Finally, while some existing systems are capable of modeling virtual reality environments, these systems are often limited to static displays, lacking real-time user interaction and unable to dynamically display changes in forest and grassland resources. Furthermore, early warning mechanisms often cannot respond to sudden changes in complex environments in real time, making it difficult to effectively support management decisions.

[0008] Therefore, existing technologies have certain shortcomings in terms of comprehensiveness, intelligence, real-time performance, and multi-source data fusion in forest and grassland resource monitoring and management. To overcome these deficiencies, this paper proposes a three-dimensional visual classification monitoring system for forest and grassland based on virtual reality and AI recognition. This system aims to provide more accurate and efficient monitoring and decision support through innovative data collection, processing, fusion, and analysis methods. Summary of the Invention

[0009] In response to the shortcomings of the existing technology, the present invention provides a three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition, which solves the problems of insufficient accuracy, poor real-time performance and lack of intelligent decision-making support in data collection, processing, fusion and analysis in forest and grassland resource monitoring in the existing technology.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: a three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition, comprising:

[0011] A data acquisition module for collecting data from a variety of sensors, including drone remote sensing images, lidar, environmental monitoring sensors, and climate and ecological data sources;

[0012] A data preprocessing module, used for performing denoising, interpolation, format conversion and standardization on the collected data;

[0013] Adaptive matrix optimization and multi-scale deep learning model module, which is used to fuse data from different data sources based on the adaptive matrix optimization algorithm and perform feature extraction and classification on the data through the multi-scale deep learning model;

[0014] The virtual environment modeling and interaction module is used to convert the processed data into a three-dimensional virtual environment and provide the user with the interactive function of the virtual environment;

[0015] The real-time dynamic monitoring and early warning module is used to continuously receive updated data from the data acquisition module and the adaptive matrix optimization and multi-scale deep learning module, monitor the changing trends of forest and grassland resources in real time, and automatically trigger early warning information when abnormal changes occur by combining the threshold judgment mechanism;

[0016] The data fusion and intelligent decision support module is used to fuse data from multiple data sources and provide intelligent decision support based on the fused data.

[0017] Preferably, the data acquisition module includes:

[0018] UAV remote sensing image acquisition unit, used to collect high-resolution two-dimensional or three-dimensional image data;

[0019] LiDAR acquisition unit, used to collect ground point cloud data;

[0020] Environmental monitoring sensor unit, used to obtain soil moisture, air temperature and air humidity parameters in real time;

[0021] The climate and ecological data source unit is used to provide climate change and ecological environment monitoring data.

[0022] Preferably, the data preprocessing module includes:

[0023] The denoising unit uses the Kalman filter algorithm to denoise the collected sensor data;

[0024] Interpolation unit, which uses bilinear interpolation algorithm to interpolate low-resolution images or point cloud data;

[0025] Format conversion unit converts data in different formats into a unified format.

[0026] Preferably, the adaptive matrix optimization and multi-scale deep learning model module includes:

[0027] Adaptive matrix optimization unit, used to dynamically adjust the weights of different data sources based on adaptive algorithms and perform data fusion;

[0028] Multi-scale deep learning unit, used to extract features of different scales through multi-layer convolutional neural networks and perform object recognition and classification;

[0029] The adaptive matrix optimization process can be expressed by the following formula:

[0030]

[0031] Among them, D fused (t) is the fused data; D i (t) is the input data of the i-th data source; w i is the weight of the i-th data source; n is the number of data sources.

[0032] Preferably, the virtual environment modeling and interaction module includes:

[0033] A three-dimensional environmental modeling unit is used to generate a three-dimensional virtual model based on the processed data to show the spatial distribution of forest and grass resources;

[0034] The virtual interaction unit is used to provide users with interactive functions between the virtual environment, including measurement, area selection and viewing detailed information.

[0035] Preferably, the real-time dynamic monitoring and early warning module includes:

[0036] Dynamic monitoring unit, used to monitor the status of forest and grass resources in real time and obtain environmental change data;

[0037] The early warning unit automatically triggers an early warning and notifies the user when a potential anomaly is detected;

[0038] The anomaly detection process of the monitoring data can be expressed by the following formula:

[0039] A(t)=f(D(t),T threshold );

[0040] Where A(t) is the anomaly detection result at time t; D(t) is the real-time monitoring data at time t; T threshold is the set threshold; f(·) is the anomaly detection function.

[0041] Preferably, the data fusion and intelligent decision support module includes:

[0042] A data fusion unit for fusing data from different sensors;

[0043] Intelligent decision support unit, used to generate decision suggestions based on the integrated data to help manage and protect forest and grassland resources;

[0044] The data fusion process can be expressed by the following formula:

[0045] D fused (t)=f(D1(t),D2(t),...,D n (t));

[0046] Among them, D fused (t) represents the fused data at time t; D i (t) is the data from the i-th sensor; f(·) is the data fusion function.

[0047] Preferably, the system further comprises:

[0048] The real-time data transmission unit is used to transmit the results generated by each module to the cloud platform in real time for centralized management and analysis.

[0049] Preferably, the virtual environment modeling and interaction module further includes:

[0050] The environmental change simulation unit is used to simulate and predict changes in forest and grassland environments and provide reference data for decision makers.

[0051] The present invention also provides a three-dimensional visual classification monitoring method for forests and grasslands based on virtual reality and AI recognition, comprising the following steps:

[0052] S1. Collect image data, point cloud data, environmental monitoring data, and climate change data of forest and grassland areas through various sensors;

[0053] S2, denoising, interpolation, format conversion and standardization of the collected raw data;

[0054] S3: weighted fusion of data from different data sources using an adaptive matrix optimization algorithm, and extracting and classifying data features using a multi-scale deep learning model;

[0055] S4. Convert the processed data into a three-dimensional virtual environment, display the spatial distribution of forest and grassland resources through virtual reality technology, and allow users to interact;

[0056] S5. Real-time monitoring of dynamic changes in forest and grassland resources. When abnormal conditions are detected, an early warning is triggered and relevant personnel are notified.

[0057] S6. Based on the integrated data and monitoring results, decision suggestions are generated through the intelligent decision support unit and provided to forest and grassland resource managers for decision-making.

[0058] The present invention provides a three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition.

[0059] It has the following beneficial effects:

[0060] 1. This invention integrates multiple sensors, such as drone remote sensing imagery, lidar, environmental monitoring sensors, and climate and ecological data sources, to comprehensively collect multidimensional data on forest and grassland resources. This multi-source data collection approach not only enhances the comprehensiveness of monitoring but also ensures high resolution and accuracy, meeting the needs of complex forest and grassland resource monitoring. Furthermore, the data preprocessing module utilizes efficient denoising, interpolation, and normalization methods, providing a high-quality data foundation for subsequent data fusion and analysis.

[0061] 2. By combining adaptive matrix optimization with a multi-scale deep learning model, this invention enables intelligent fusion of information from diverse data sources. This optimization algorithm dynamically adjusts the weights of each data source based on its importance, making the resulting fused data more accurate and reliable. By extracting multi-level features for classification, the multi-scale deep learning model not only improves the accuracy of forest and grassland resource identification but also enhances the system's adaptability to complex environmental changes.

[0062] 3. Through a virtual environment modeling and interactive module, this invention transforms processed forest and grassland resource data into a three-dimensional virtual environment, visually displaying the spatial distribution and dynamic changes of forest and grassland resources. This 3D visualization not only provides decision makers with a clear view of the environment, but also enables users to measure, select areas, and view detailed information in real time through interactive features, significantly improving the operability and understanding of monitoring results.

[0063] 4. The present invention's real-time dynamic monitoring and early warning module monitors changes in forest and grassland resources in real time and triggers timely warnings when potential anomalies occur. This function can issue early warnings for emergencies such as forest fires and pests, providing decision makers with ample time to react. This real-time monitoring and automatic early warning mechanism significantly improves the responsiveness and emergency response capabilities of forest and grassland resource management, helping to reduce the occurrence and losses of disasters.

[0064] 5. The intelligent decision support module of this invention integrates data from various sensors to provide decision support for the management and protection of forest and grassland resources. By comprehensively analyzing various environmental and ecological data, the system generates scientific decision-making recommendations, helping forest and grassland resource managers make rational resource allocation and emergency response decisions. This intelligent decision-making capability makes forest and grassland resource management more precise and efficient, and provides strong adaptability to various complex environmental changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 This is a system architecture diagram of the present invention;

[0066] Figure 2 This is a structural diagram of the data acquisition module of the present invention;

[0067] Figure 3 Schematic diagram of the data preprocessing module structure of the present invention;

[0068] Figure 4 This is a schematic diagram of the adaptive matrix optimization and multi-scale deep learning model module structure of the present invention;

[0069] Figure 5 This is a schematic diagram of the structure of the virtual environment modeling and interaction module of the present invention;

[0070] Figure 6 This is a structural diagram of the real-time dynamic monitoring and early warning module of the present invention;

[0071] Figure 7 This is a structural diagram of the data fusion and intelligent decision support module of the present invention;

[0072] Figure 8 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0074] Please see the attached Figure 1 -Attached Figure 7 The embodiment of the present invention provides a three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition, including the following modules:

[0075] A data acquisition module for collecting data from a variety of sensors, including drone remote sensing images, lidar, environmental monitoring sensors, and climate and ecological data sources;

[0076] In this embodiment, the data acquisition module is designed to efficiently collect and process various data. By combining sensors, data acquisition devices, and data transmission systems, this module collects relevant information from multiple data sources in real time and accurately. It then preprocesses and performs preliminary analysis on this data, providing high-quality input for subsequent data processing and analysis modules.

[0077] The core function of the data acquisition module is to collect data from sensors. Sensors sense the environment, physical quantities, and other factors and convert the collected signals into electrical or digital signals. To ensure the accuracy and stability of data acquisition, this embodiment uses a variety of different types of sensors, which can select the appropriate measurement object according to needs. Specifically, sensor types include temperature and humidity sensors, pressure sensors, and light sensors. These sensors can monitor changes in the surrounding environment or target in real time and convert these changes into digital signals for transmission to the data acquisition module for processing.

[0078] The data acquisition module in this embodiment includes a data preprocessing submodule, which performs noise reduction and filtering on the collected raw data to improve data quality. During data transmission, to prevent information loss or distortion, the preprocessed data undergoes data encoding and compression to improve transmission efficiency. The preprocessed data is then transmitted via a communication network to a data storage module for storage and subsequent analysis.

[0079] The technical details of the data acquisition module are as follows:

[0080] In this embodiment, the choice of sensor varies depending on the measurement target. For example, if temperature information needs to be collected, a temperature sensor is selected; if pressure information needs to be collected, a pressure sensor is selected. The sensor generates an electrical or digital signal in response to changes in the target environment, which is transmitted to the data acquisition unit for further processing. The configuration of different sensor types can be flexibly adjusted based on actual needs. Preferably, the sensor adopts a modular design to facilitate expansion and maintenance.

[0081] In this embodiment, the collected signals are either analog or digital. For analog signals, the data acquisition module converts them into digital signals using an analog-to-digital converter (ADC) for subsequent processing and analysis. For sensors that already generate digital signals, the data acquisition module directly receives the digital signals. All collected signal data is standardized within this module to ensure compliance with subsequent processing requirements.

[0082] To improve data transmission efficiency, the data acquisition module in this embodiment performs encoding and compression before data transmission. Data encoding involves converting the format of the collected data to ensure efficient transmission to the storage or processing module. Data compression primarily uses compression algorithms to reduce the size of data during transmission, thereby saving bandwidth and storage space.

[0083] The processed data is transmitted to the data storage module via wireless or wired communication networks. Encryption technology is used during transmission to ensure data security and privacy. The data storage module saves the received data to a database or cloud storage for subsequent data analysis. The data storage structure and access strategy are carefully designed to improve data access efficiency and security.

[0084] In the data acquisition module, the collected signal data can be expressed as:

[0085] D(t)=f(S(t),P(t),T(t));

[0086] Where D(t) represents the collected signal data; S(t) is the raw sensor signal; P(t) is the data preprocessing process; and T(t) is the data transmission process. This formula describes the entire signal flow in the data acquisition module, from sensor acquisition to processing and transmission.

[0087] A data preprocessing module, used for performing denoising, interpolation, format conversion and standardization on the collected data;

[0088] In this embodiment, the data preprocessing module is primarily used to process the raw data transmitted from the data acquisition module to improve data quality and reliability. The data preprocessing process includes steps such as denoising, filtering, standardization, and calibration. These steps aim to remove noise or interfering signals that may have been introduced during the acquisition process, making the data more accurate and stable, thereby providing high-quality input for subsequent data analysis.

[0089] First, the data preprocessing module receives data from the data acquisition module. The raw data may contain some noise or distortion, especially when collected over a long period of time or under significant environmental changes. Therefore, the data preprocessing module performs a series of processing operations on this data to ensure its accuracy and usability.

[0090] The main technical details of the data preprocessing module are as follows:

[0091] In this embodiment, in order to reduce the interference of the external environment on the collected signal, the data preprocessing module adopts noise filtering technology. The denoising process is achieved by applying a filtering algorithm. Common denoising algorithms include low-pass filtering, high-pass filtering, and Kalman filtering. Specifically, low-pass filtering can effectively remove high-frequency noise, while high-pass filtering helps to remove low-frequency noise. To deal with signal interference in some special cases, the Kalman filtering algorithm is also adopted in this embodiment. This algorithm can dynamically adjust the filtering parameters and adaptively remove noise according to the real-time status of the system. It is particularly suitable for real-time signal processing.

[0092] The signal after denoising will significantly reduce interference and ensure data accuracy in subsequent processing stages.

[0093] Signal filtering is another important step in data preprocessing. During signal acquisition, signals often experience irregular fluctuations due to factors such as equipment noise and electromagnetic interference. Therefore, this embodiment employs various filtering methods, such as weighted moving average filtering and Kalman filtering, to smooth the signal. After filtering, the amplitude of signal fluctuations is reduced, thereby improving signal stability and analyzability.

[0094] Preferably, when filtering is performed, appropriate filtering algorithms and parameters are selected according to the characteristics of the signal to ensure optimal filtering effects.

[0095] To facilitate subsequent data analysis and processing, the data preprocessing module standardizes and normalizes all collected signals. Standardization refers to converting data to a certain standard scale so that data from different sources are comparable. Specifically, signal standardization is usually achieved through the following formula:

[0096]

[0097] Where X is the original data; μ is the mean of the data; σ is the standard deviation of the data; X s td is the normalized data.

[0098] Normalization compresses signal data into a predetermined range, usually scaling the data range to the [0, 1] interval. The formula is as follows:

[0099]

[0100] Among them, X norm is the normalized data; X min and X max The minimum and maximum values ​​of the signal are respectively. Through standardization and normalization, the raw data are converted into a unified format to ensure its consistency and comparability in subsequent analysis.

[0101] In this embodiment, the data preprocessing module also includes a calibration module designed to compensate for possible systematic errors in the sensor. In practical applications, deviations in the collected data may occur due to manufacturing differences in the sensor or changes in environmental conditions. The data calibration process introduces known standard values ​​and uses a calibration algorithm to correct the signal. Common calibration algorithms include linear calibration and polynomial calibration. During calibration, this embodiment uses a linear regression method to compare the collected signal with the known standard value to derive the deviation value and correct it.

[0102] The signal after data calibration can be expressed as:

[0103] D calibrated (t) = D raw (t)+ΔD(t);

[0104] Among them, D calibrated (t) is the calibrated signal; D raw (t) is the original signal; ΔD(t) is the calibration error correction.

[0105] To improve the efficiency of subsequent data storage and transmission, the data preprocessing module also performs data compression and encoding. This compression process uses common data compression algorithms, such as Huffman coding or LZ77 compression, to effectively reduce data volume. Data encoding involves formatting signal data to adapt it to subsequent storage or transmission requirements. This data compression and encoding process effectively saves storage space and improves transmission efficiency.

[0106] After denoising, filtering, normalization, calibration, compression, and encoding, the data is output to the data storage module or data analysis module. This process effectively improves the data quality and formats it for subsequent processing and analysis. Data output can be in binary, JSON, or other formats suitable for subsequent use.

[0107] Adaptive matrix optimization and multi-scale deep learning model module, which is used to fuse data from different data sources based on the adaptive matrix optimization algorithm and perform feature extraction and classification on the data through the multi-scale deep learning model;

[0108] In this embodiment, the Adaptive Matrix Optimization and Multi-Scale Deep Learning Model module aims to combine adaptive matrix optimization technology with a multi-scale deep learning framework to achieve efficient feature extraction and processing, further improving data processing accuracy and model adaptability. This module uses an adaptive matrix optimization algorithm to dynamically adjust the characteristics of the input data. Combined with a multi-scale deep learning model, this module enables the model to comprehensively process data at different scales, thereby improving overall model performance.

[0109] In this embodiment, adaptive matrix optimization technology is a key step in optimizing input data and model parameters. In traditional deep learning models, model parameters are typically static, making it difficult to dynamically adjust them based on varying input data during training. In this embodiment, however, an adaptive matrix optimization algorithm is employed to more effectively process input data with varying characteristics by updating the optimization matrix in real time.

[0110] Specifically, the input data X is optimized by the adaptive matrix M(t) so that the optimized data better meets the requirements of the deep learning model. This process can be described by the following formula:

[0111] X optimized (t) = X(t)·M(t);

[0112] Among them, X(t) is the original input data; M(t) is the adaptive matrix; X optimized (t) is the optimized data. The adaptive matrix M(t) dynamically updates its parameters according to the learning process of the model and the characteristics of the input data to optimize the data processing effect.

[0113] The key to adaptive matrix optimization lies in adjusting the matrix parameters through a learning algorithm, so that when the input data passes through the optimized matrix, it can retain the most critical information of the data while removing redundant information or noise. Therefore, this technology can significantly improve the quality of data and provide more accurate input for subsequent deep learning models.

[0114] In a multi-scale deep learning model, the model learns from data at multiple levels and scales, extracting features at different scales. The core concept behind this model design is that data at different levels or scales may contain different levels of feature information, which is crucial for completing the entire task. Therefore, the model needs to be able to learn at different scales to capture the multidimensional information in the data.

[0115] In this embodiment, the multi-scale deep learning model used includes multiple convolutional and pooling layers. By extracting data features layer by layer and fusing them at different scales, it effectively extracts multi-scale information. The convolution operation at each scale captures features at different levels of the input data, while the pooling operation reduces the dimensionality of the features, thereby reducing the computational effort and retaining the most important feature information.

[0116] Specifically, the multi-scale convolution operation can be expressed as:

[0117]

[0118] Among them, F multi-scale (t) represents the feature output after processing by the multi-scale convolutional layer; W i is the convolution kernel; X optimized (t) is the input data after adaptive matrix optimization; n represents the number of different scales. Through multi-scale convolution operations, the model can capture feature information at different levels in the data, making the final feature expression richer.

[0119] To fully utilize multi-scale information, this embodiment incorporates a feature fusion module. This module combines features at different scales, synthesizing the output of each layer to form a unified feature representation. This feature fusion effectively combines information at different scales, improving the model's understanding and prediction capabilities.

[0120] The feature fusion process can be achieved through weighted summation or splicing. The specific formula is as follows:

[0121]

[0122] Among them, F fused (t) is the fused feature; F i (t) is the feature of the i-th scale; α i is the weight coefficient of the i-th scale. Weight coefficient α i Dynamic adjustments are made during the model training process to assign different importance to features at different scales. Through feature fusion, the model can understand the data at multiple levels and generate more representative feature expressions.

[0123] During the model training process, conventional deep learning optimization methods, such as backpropagation and gradient descent, are used to optimize the model parameters. By minimizing the loss function, the adaptive matrix and the weights of the convolutional layer are gradually adjusted to minimize the model's prediction error. The loss function can be expressed as:

[0124] L(t)=||Y pred (t)-Y true (t)||| 2 ;

[0125] Among them, Y pred (t) is the model prediction output; Y true (t) is the actual value; ||·|| represents the L2 norm. The backpropagation algorithm gradually optimizes the model parameters based on the gradient information, resulting in a final model with high prediction accuracy.

[0126] During the optimization process, by utilizing the dynamic update capability of the adaptive matrix optimization algorithm and combining it with the advantages of multi-scale feature extraction, the model can efficiently learn and adapt to the various features of the input data, making the final prediction results have strong generalization capabilities.

[0127] The final output of the model can be described by the following formula:

[0128] Y final (t) = f(F fused (t),θ);

[0129] Among them, Y final (t) is the final output of the model; F fused (t) is the fused multi-scale feature; θ is the model parameter. By adjusting these parameters through optimization algorithms, the model can gradually improve its performance, ultimately achieving efficient processing and accurate prediction of complex data.

[0130] The virtual environment modeling and interaction module is used to convert the processed data into a three-dimensional virtual environment and provide the user with the interactive function of the virtual environment;

[0131] In this embodiment, the virtual environment modeling and interaction module aims to build an efficient, flexible, and user-friendly virtual environment for real-time interaction and simulation operations. This module integrates virtual environment construction and interaction technologies, allowing users to interact with the virtual environment in various ways, thereby visualizing and controlling system status.

[0132] The virtual environment modeling module in this embodiment utilizes advanced computer graphics technology and multi-dimensional modeling tools to transform physical entities and scenes in the real world into models in a virtual space. This modeling process accurately reproduces the physical properties of the real world and enables users to observe and operate in a virtual environment.

[0133] In virtual environment modeling, the three-dimensional objects and background scenes within the environment are first constructed based on the input data and requirements. Object modeling utilizes a triangulated mesh representation, dividing the object surface into multiple small triangles to more accurately represent complex shapes and curves. Texture mapping technology also imbues the object surface with a sense of realism, enhancing the immersiveness of the virtual environment. Environmental modeling also includes the processing of various factors, such as lighting, shadows, and materials, to ensure that the scenes and objects in the virtual environment simulate the visual effects of the real world.

[0134] The model representation of the virtual environment can be described by the following formula:

[0135]

[0136] Among them, M virtual (t) is the virtual environment model; V i (t) represents the 3D geometric model of the i-th object; T i (t) represents the texture information of the i-th object; L i (t) represents the lighting setting for the i-th object; n is the number of objects in the virtual environment. Through this modeling formula, each object in the environment and its characteristics can be comprehensively represented, thus constructing a complete virtual scene.

[0137] The interactive module in this embodiment utilizes a variety of interactive technologies, allowing users to interact with the virtual environment through various input devices. These include, but are not limited to, a mouse, keyboard, touchscreen, VR gloves, and other virtual reality devices. By capturing user input, the interactive module adjusts the virtual environment's perspective, position, and movement in real time to achieve dynamic feedback.

[0138] The core of interactive operations lies in updating the state of the virtual environment by responding to user actions in real time. Users issue commands through control devices, which the system receives and processes. Based on the user's input and the current state of the virtual environment, the system then performs corresponding scene updates or object interactions. For example, as a user moves through a virtual reality device, the system tracks the user's movements by updating the virtual environment's perspective, creating an immersive experience.

[0139] The mathematical model of the interaction process can be expressed as:

[0140] E interaction(t) = f(I user (t),S current (t));

[0141] Among them, E interaction (t) represents the environmental feedback during the interaction process; I user (t) is the user's input instruction; S current (t) is the current state of the virtual environment; f(·) is the feedback function during the interaction process, which is used to calculate the environment update result based on the user input and the current state of the virtual environment. This formula illustrates how the interaction feedback is generated and the virtual environment is updated by combining the user input with the current state of the virtual environment.

[0142] To ensure real-time and interactivity within the virtual environment, the virtual environment modeling and interaction module in this embodiment integrates a highly efficient real-time rendering engine. This engine renders the graphics model within the virtual environment frame by frame, generating the virtual scene that the user sees in real time. Advanced ray tracing technology is employed during the virtual environment rendering process to simulate real-world lighting effects and object reflections, ensuring realistic and natural rendering.

[0143] When rendering, each object's color, texture, material, and lighting influence the final result. The rendering engine uses a dynamic update mechanism to adjust the image in the virtual environment in real time based on user interaction feedback, ensuring that each frame accurately reflects the user's current actions and perspective changes.

[0144] The rendering process can be described by the following formula:

[0145]

[0146] Among them, R frame (t) represents the rendering result of the current frame; G i (t) is the geometric information of the i-th object; L i (t) is the lighting information of the i-th object; P i (t) is the material property of the i-th object. This formula shows how the properties of each object affect the final rendering result of the virtual environment.

[0147] To ensure a high level of immersion and ease of use when interacting with the virtual environment, the virtual environment modeling and interaction module in this embodiment incorporates multiple optimization strategies during its design. These optimizations include simplifying the user interface, streamlining the interaction process, and minimizing the response time of user operations. By optimizing the user experience, users can interact with the virtual environment more naturally while reducing confusion and delays during operation.

[0148] For example, when a user performs an action through gestures or touch, the system responds and displays feedback in real time. To further enhance the user experience, this embodiment also optimizes the feedback information of the virtual environment to make it more intuitive and accurate. Each user action triggers dynamic adjustments to the system, ensuring that the user can perceive changes in the virtual environment in real time.

[0149] In multi-user virtual environment applications, the virtual environment modeling and interaction module of this embodiment supports state synchronization and dynamic interaction among multiple users. Whenever a user's input causes a change in the virtual environment's state, the system automatically updates the perspectives and states of other users in the virtual environment. This synchronization mechanism, through the integration of network communication technologies, ensures efficient data exchange and real-time updates in a distributed environment.

[0150] The mathematical model of this process can be expressed as:

[0151]

[0152] Among them, S sync (t) represents the state of the virtual environment after synchronization; S i (t) is the virtual environment state of the i-th user; m is the number of users participating in the interaction.

[0153] Through this formula, the system is able to maintain consistency in the virtual environment state among multiple users.

[0154] The real-time dynamic monitoring and early warning module is used to continuously receive updated data from the data acquisition module and the adaptive matrix optimization and multi-scale deep learning module, monitor the changing trends of forest and grassland resources in real time, and automatically trigger early warning information when abnormal changes occur by combining the threshold judgment mechanism;

[0155] In this embodiment, the real-time dynamic monitoring and early warning module is designed to provide real-time monitoring of system status and, based on this monitoring data, provide early warning of potential anomalies or failures. By integrating multiple sensors and data acquisition technologies, this module acquires and processes various environmental and system data in real time. It then utilizes advanced early warning algorithms to identify potential risks and, through an alarm mechanism, notify relevant personnel, thereby avoiding or mitigating potential catastrophic events.

[0156] In this embodiment, the real-time dynamic monitoring module continuously monitors the environment and system status through various sensors and data acquisition devices. These sensors can collect real-time information on physical quantities such as temperature, humidity, pressure, and vibration, and transmit the collected data to the monitoring system via wireless or wired communication networks for processing.

[0157] The connection between the data acquisition module and the monitoring system is usually achieved through standard communication protocols such as Modbus, CAN bus or wireless sensor network. The communication between these sensors and the monitoring system ensures the rapid transmission of data, thus achieving real-time monitoring.

[0158] The monitoring system generates real-time status reports based on data from various sensors, displaying the system's operating status through a graphical interface. These reports include not only current physical quantity values ​​but also system health assessments related to these values, helping monitoring personnel better understand the current system status.

[0159] The data acquisition process can be described by the following formula:

[0160] D(t)={S1(t),S2(t),...,S n (t)};

[0161] Where D(t) represents all sensor data collected at time t; S i (t) is the data collected by the i-th sensor at time t; n is the total number of sensors. Using this formula, the monitoring system can clearly record the values ​​of each sensor at different time points, allowing for comprehensive monitoring and analysis.

[0162] To ensure timely alerts when anomalies occur, the monitoring and early warning module in this embodiment combines multiple data analysis methods, including anomaly detection algorithms, to analyze real-time data. This algorithm compares historical data with real-time data to identify potential anomaly patterns and risk signals. When the system's monitoring data deviates from a set threshold or normal operating pattern, the system triggers an early warning mechanism and issues a warning message to relevant personnel.

[0163] Anomaly detection algorithms are typically implemented using statistical methods, machine learning algorithms, or deep learning algorithms. For example, they build a data distribution model for the system under normal operating conditions and use probabilistic statistical methods to calculate the degree of deviation between the current data point and the model. If the deviation exceeds a preset threshold, an anomaly is considered to have occurred.

[0164] The mathematical model of anomaly detection can be expressed by the following formula:

[0165] A(t)=f(D(t),T threshold );

[0166] Where A(t) represents the anomaly detection result at time t; f(·) is the anomaly detection function; D(t) is the real-time monitoring data at time t; T thresholdThe formula indicates that when the difference between the real-time monitoring data and the threshold or the normal data model exceeds the predetermined range, an abnormal alarm will be triggered.

[0167] When the monitoring system detects a potential anomaly, the early warning module generates a corresponding warning signal. Warning signals can be categorized into different levels, such as general warning, emergency warning, and catastrophic warning. Each warning level corresponds to a different response strategy to ensure a timely and effective response to the anomaly.

[0168] Warning signals are generated based on anomaly detection results and pre-defined warning rules. When anomaly detection results indicate that system status deviates from normal ranges, the warning module assesses the severity of the anomaly and generates a corresponding warning signal. This generated warning signal can be notified to relevant personnel via audio, image, text message, or email.

[0169] The process of generating early warning signals can be described by the following formula:

[0170] P alert (t)=g(A(t),R threshold );

[0171] Among them, P alert (t) is the warning signal at time t; A(t) is the anomaly detection result; g(·) is the warning signal generation function; R threshold is the threshold of the warning rule. Through this formula, the system can generate appropriate warning signals according to the degree of detected anomalies.

[0172] In some application scenarios, in addition to issuing warning signals, the system will also implement corresponding automatic control measures based on the warning level. For example, when a device fails or is overloaded, the system can automatically activate protection mechanisms, shut down certain functions, or adjust the device's operating status to prevent the fault from escalating.

[0173] Automatic control is based on pre-set response strategies. Each warning level corresponds to a different control measure, ranging from automatic adjustment of operating parameters to complete shutdown protection, aiming to minimize system damage.

[0174] This process can be represented by the following control model:

[0175] C(t)=h(P alert (t),S current (t));

[0176] Where C(t) represents the control measure at time t; P alert (t) is the early warning signal at time t; h(·) is the control strategy function; S current(t) is the current system state. According to different early warning signals, the control strategy function will determine the corresponding control measures to ensure system safety.

[0177] The real-time dynamic monitoring and early warning module in this embodiment also features a feedback optimization mechanism. During the system's monitoring, anomaly detection, and early warning response processes, the system continuously records various data and optimizes the monitoring and early warning processes. By analyzing historical data, the optimization algorithm continuously adjusts thresholds, improves anomaly detection algorithms, and enhances system accuracy and response speed.

[0178] The optimization process can be expressed by the following formula:

[0179] O(t)=k(F feedback (t),S history (t));

[0180] Among them, O(t) represents the optimization result at time t; F feedback (t) is the system feedback data; S history (t) represents historical data; k(·) represents the optimization algorithm function. Through the feedback mechanism, the system can gradually adjust monitoring and early warning strategies based on historical data and user feedback, improving the system's intelligence and adaptability.

[0181] A data fusion and intelligent decision support module, used to fuse data from multiple data sources and provide intelligent decision support based on the fused data;

[0182] In this embodiment, the data fusion and intelligent decision support module aims to provide decision makers with efficient and accurate decision support by comprehensively processing and analyzing data from various sources. This module uses data fusion technology to integrate information from multiple sensors, systems, or external data sources, eliminating the biases and deficiencies of a single data source. It then analyzes this information using intelligent decision-making algorithms to achieve more intelligent and accurate decision support.

[0183] In this embodiment, the data fusion module generates a more accurate and comprehensive decision-making basis by comprehensively processing data provided by multiple data sources. The core of data fusion lies in combining multi-dimensional and multi-source data and using appropriate algorithms to weight, integrate, and optimize the data, thereby eliminating redundancy, reducing noise, and filling in data gaps.

[0184] The data fusion process can be divided into the following steps: first, preprocess the data from different sensors or systems, including denoising and standardization; then, fuse the data through weighted averaging, maximum likelihood estimation and other methods to finally obtain a unified and optimized data set.

[0185] In the data fusion process, setting appropriate fusion weights is key. The weight of each data source is determined by its reliability, accuracy, and importance.

[0186] The specific weighted average model can be expressed as:

[0187]

[0188] Among them, D fused (t) is the fused data; D i (t) is the input data of the i-th data source; w i is the weight of the i-th data source; n is the number of data sources. Through this formula, the system can perform a weighted summation of the data of each data source based on its importance to obtain the final fusion result.

[0189] To ensure high data quality, the data fusion module first cleans and preprocesses the raw data. In practical applications, data often contains noise, missing values, and outliers, which can adversely affect subsequent data analysis and decision-making. Therefore, data cleaning is a necessary step in the fusion process.

[0190] The main tasks of data cleaning include removing duplicate data, filling missing data, and correcting outliers. For missing data, this embodiment preferably uses interpolation or a filling algorithm based on similar data sources to ensure data integrity. Cleaned data provides a more accurate and consistent information foundation for the fusion process.

[0191] After data fusion is complete, the intelligent decision support module in this embodiment uses multiple intelligent decision-making algorithms to conduct in-depth analysis of the fused data, thereby providing support to decision makers. Intelligent decision support algorithms can flexibly select appropriate models for decision analysis based on different business needs and application scenarios.

[0192] Intelligent decision-making models typically include rule-based decision systems, machine learning models, and deep learning models. For static problems, rule-based decision systems can provide intuitive and easy-to-understand decision support. For complex, dynamic problems, machine learning and deep learning models can be used. By training on large amounts of historical data, they can automatically discover underlying patterns within the data and make predictions and decisions.

[0193] The mathematical model of the intelligent decision-making process can be expressed as:

[0194] D decision (t) = g(D fused (t),M model );

[0195] Among them, Ddecision (t) is the decision result at time t; D fused (t) is the fused data; M model is the selected decision model; g(·) is the decision function. Using this formula, the system can perform in-depth analysis based on the fused data and intelligent decision model and generate the final decision result.

[0196] To ensure the continued effectiveness of decisions, the intelligent decision support module in this embodiment incorporates a feedback optimization mechanism. During the decision execution process, the system automatically adjusts the parameters or strategies of the decision model based on the gap between the actual execution results and the expected goals, thereby optimizing the subsequent decision-making process.

[0197] The core of feedback optimization lies in adjusting decision rules or model parameters based on real-time feedback data, enabling the system to gradually adapt to changes in the external environment and make more accurate predictions and decisions. For example, reinforcement learning algorithms can be used to dynamically adjust decision models based on reward and penalty mechanisms, achieving continuous optimization.

[0198] The feedback optimization process can be expressed by the following formula:

[0199] ΔM(t)=h(E feedback (t),M model );

[0200] Where ΔM(t) represents the parameter adjustment of the decision model at time t; E feedback (t) is the real-time feedback data; h(·) is the optimization algorithm function. Through this formula, the system can continuously optimize the decision model based on actual feedback data, ensuring efficient and accurate decision-making.

[0201] To improve the operability and transparency of the decision-making process, a visualization module of the decision support system is also designed in this embodiment. The complex decision data is presented to the decision maker in an intuitive way through a graphical interface, enabling him to quickly understand the current decision status and the data behind it.

[0202] Visual displays include data trend charts, decision-making process diagrams, and risk assessment diagrams, helping decision makers analyze from multiple perspectives. Through an interactive graphical interface, users can freely switch between different views and deeply explore key information in the decision-making process.

[0203] The data fusion and intelligent decision support module in this embodiment also supports real-time decision-making. By integrating with the real-time monitoring system, the system can make dynamic decisions based on real-time data. This real-time decision-making function can quickly respond to external changes in complex environments and make timely and effective adjustments to ensure system stability and security.

[0204] The real-time decision-making process can be expressed as:

[0205] D real-time (t) = f(D monitor (t),D fused (t),M model );

[0206] Among them, D real-time (t) is the real-time decision result at time t; D monitor (t) is real-time monitoring data; D fused (t) is the fused historical data; f(·) is the real-time decision function. Through this model, the system can make dynamic decisions based on the combination of real-time monitoring data and historical data.

[0207] Please see the attached Figure 8 The present invention also provides a three-dimensional visual classification monitoring method for forests and grasslands based on virtual reality and AI recognition, comprising the following steps:

[0208] S1. Collect image data, point cloud data, environmental monitoring data, and climate change data of forest and grassland areas through various sensors;

[0209] S2, denoising, interpolation, format conversion and standardization of the collected raw data;

[0210] S3: weighted fusion of data from different data sources using an adaptive matrix optimization algorithm, and extracting and classifying data features using a multi-scale deep learning model;

[0211] S4. Convert the processed data into a three-dimensional virtual environment, display the spatial distribution of forest and grassland resources through virtual reality technology, and allow users to interact;

[0212] S5. Real-time monitoring of dynamic changes in forest and grassland resources. When abnormal conditions are detected, an early warning is triggered and relevant personnel are notified.

[0213] S6. Based on the integrated data and monitoring results, decision suggestions are generated through the intelligent decision support unit and provided to forest and grassland resource managers for decision-making.

[0214] The method of this embodiment can be used to execute the above system embodiment. Its principles and technical effects are similar and will not be described in detail here.

[0215] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition, characterized by: include: A data acquisition module for collecting data from a variety of sensors, including drone remote sensing images, lidar, environmental monitoring sensors, and climate and ecological data sources; A data preprocessing module, used for performing denoising, interpolation, format conversion and standardization on the collected data; Adaptive matrix optimization and multi-scale deep learning model module, which is used to fuse data from different data sources based on the adaptive matrix optimization algorithm and perform feature extraction and classification on the data through the multi-scale deep learning model; The virtual environment modeling and interaction module is used to convert the processed data into a three-dimensional virtual environment and provide the user with the interactive function of the virtual environment; The real-time dynamic monitoring and early warning module is used to continuously receive updated data from the data acquisition module and the adaptive matrix optimization and multi-scale deep learning module, monitor the changing trends of forest and grassland resources in real time, and automatically trigger early warning information when abnormal changes occur by combining the threshold judgment mechanism; The data fusion and intelligent decision support module is used to fuse data from multiple data sources and provide intelligent decision support based on the fused data.

2. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The data acquisition module includes: UAV remote sensing image acquisition unit, used to collect high-resolution two-dimensional or three-dimensional image data; LiDAR acquisition unit, used to collect ground point cloud data; Environmental monitoring sensor unit, used to obtain soil moisture, air temperature and air humidity parameters in real time; The climate and ecological data source unit is used to provide climate change and ecological environment monitoring data.

3. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The data preprocessing module includes: The denoising unit uses the Kalman filter algorithm to denoise the collected sensor data; Interpolation unit, which uses bilinear interpolation algorithm to interpolate low-resolution images or point cloud data; Format conversion unit converts data in different formats into a unified format.

4. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The adaptive matrix optimization and multi-scale deep learning model module includes: Adaptive matrix optimization unit, used to dynamically adjust the weights of different data sources based on adaptive algorithms and perform data fusion; Multi-scale deep learning unit, used to extract features of different scales through multi-layer convolutional neural networks and perform object recognition and classification; The adaptive matrix optimization process can be expressed by the following formula: Among them, D fused (t) is the fused data; D i (t) is the input data of the i-th data source; w i is the weight of the i-th data source; n is the number of data sources.

5. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The virtual environment modeling and interaction module includes: A three-dimensional environmental modeling unit is used to generate a three-dimensional virtual model based on the processed data to show the spatial distribution of forest and grass resources; The virtual interaction unit is used to provide users with interactive functions between the virtual environment, including measurement, area selection and viewing detailed information.

6. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The real-time dynamic monitoring and early warning module includes: Dynamic monitoring unit, used to monitor the status of forest and grass resources in real time and obtain environmental change data; The early warning unit automatically triggers an early warning and notifies the user when a potential anomaly is detected; The anomaly detection process of the monitoring data can be expressed by the following formula: A(t)=f(D(t),T threshold ); Among them, A(t) is the anomaly detection result at time t; D(t) is the real-time monitoring data at time t; T threshold is the set threshold; f(·) is the anomaly detection function.

7. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The data fusion and intelligent decision support module includes: A data fusion unit for fusing data from different sensors; Intelligent decision support unit, used to generate decision suggestions based on the integrated data to help manage and protect forest and grassland resources; The data fusion process can be expressed by the following formula: D fused (t)=f(D1(t),D2(t),...,D n (t)); Among them, D fused (t) represents the fused data at time t; D i (t) is the data from the i-th sensor; f(·) is the data fusion function.

8. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The system further comprises: The real-time data transmission unit is used to transmit the results generated by each module to the cloud platform in real time for centralized management and analysis.

9. The three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to claim 1 is characterized in that: The virtual environment modeling and interaction module further includes: The environmental change simulation unit is used to simulate and predict changes in forest and grassland environments and provide reference data for decision makers.

10. A three-dimensional visual classification monitoring method for forests and grasslands based on virtual reality and AI recognition, applied to a three-dimensional visual classification monitoring system for forests and grasslands based on virtual reality and AI recognition according to any one of claims 1 to 9, characterized in that: The method comprises the following steps: S1. Collect image data, point cloud data, environmental monitoring data, and climate change data of forest and grassland areas through various sensors; S2, denoising, interpolation, format conversion and standardization of the collected raw data; S3: weighted fusion of data from different data sources using an adaptive matrix optimization algorithm, and extracting and classifying data features using a multi-scale deep learning model; S4. Convert the processed data into a three-dimensional virtual environment, display the spatial distribution of forest and grassland resources through virtual reality technology, and allow users to interact; S5. Real-time monitoring of dynamic changes in forest and grassland resources. When abnormal conditions are detected, an early warning is triggered and relevant personnel are notified. S6. Based on the integrated data and monitoring results, decision suggestions are generated through the intelligent decision support unit and provided to forest and grassland resource managers for decision-making.

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