Ecological restoration dynamic simulation and interactive experience system

Abstract

The invention discloses an ecological restoration dynamic simulation and interactive experience system. The system comprises a multi-source sensor data acquisition module, a data fusion and preprocessing module, a dynamic prediction and adaptive simulation module, a virtual reality interactive experience module and a data closed-loop feedback control module. The multi-source sensor data acquisition module acquires data through satellite remote sensing, ground monitoring and unmanned aerial vehicle inspection sub-modules; the data fusion and preprocessing module performs standardization processing and fusion cleaning on the data; the dynamic prediction and adaptive simulation module predicts an ecological evolution trend by using a deep learning model, simulates a restoration scheme effect and automatically adjusts parameters; the virtual reality interaction experience module realizes immersive experience by means of VR / AR equipment and supports gesture and voice interaction; and the data closed-loop feedback control module collects user operation feedback and optimizes the system. All the modules are in optical fiber communication through a 5G network, real-time stable work of the system is achieved, and a comprehensive and efficient simulation and interaction tool is provided for ecological restoration.

Description

Technical Field

[0001] The present invention relates to the technical field of ecological environment monitoring and restoration, and particularly to an ecological restoration dynamic simulation and interactive experience system. Background Art

[0002] Ecological restoration refers to the process of intervening in damaged or degraded ecosystems through scientific means to restore their ecological functions and biodiversity. With the acceleration of industrialization and urbanization, ecological environment problems have become increasingly severe. Traditional ecological restoration methods mostly rely on static assessment and experience-based decision-making, making it difficult to dynamically simulate the long-term effects of restoration plans, resulting in problems such as unreasonable resource allocation and low restoration efficiency. How to achieve dynamic simulation and real-time interactive optimization of the ecological restoration process has become a key challenge in improving the efficiency of ecological governance.

[0003] In the prior art, some systems obtain environmental data by combining satellite remote sensing and ground monitoring, and predict the ecological evolution trend through simple mathematical models. For example, analyzing the vegetation coverage using remote sensing images and establishing a linear regression model in combination with historical meteorological data to simulate the diffusion path of pollutants. Although such methods can preliminarily evaluate the restoration effect, the model parameters are fixed and cannot adapt to dynamic environmental changes.

[0004] Although the above technologies provide important support for ecological restoration, they also have obvious limitations. First, due to the lack of an effective multi-source data fusion mechanism, it is difficult to achieve seamless docking between different types of data, resulting in low information utilization rate. Second, most of the existing prediction models are based on simple statistical relationships or shallow learning architectures, and cannot fully capture the dynamic change characteristics of ecosystems in complex environments. Finally, in terms of user experience, traditional methods do not provide intuitive three-dimensional visualization tools, making it difficult for decision-makers and technicians to directly feel and understand the potential changes and their effects in the ecological restoration process. The existence of these problems urgently requires an ecological restoration dynamic simulation and interactive experience system that can integrate a variety of advanced technologies to achieve high-efficiency, precision, and user-friendly performance. Summary of the Invention

[0005] Object of the Invention: To overcome the deficiencies of the prior art, the present invention provides an ecological restoration dynamic simulation and interactive experience system. By constructing a multi-source data fusion and adaptive prediction framework, integrating virtual reality interaction technology and a closed-loop feedback optimization mechanism, it realizes high-precision simulation of the dynamic evolution of the ecological environment, real-time user intervention, and intelligent optimization of the restoration plan, so as to solve the problems of low data fusion efficiency, insufficient model adaptability, single interactive experience, and lack of feedback closed-loop in the prior art.

[0006] An ecological restoration dynamic simulation and interactive experience system, characterized in that the system is composed of the following modules and realizes data acquisition, processing, dynamic simulation, and interactive feedback according to the following steps:

[0007] Multi-source sensor data acquisition module, including:

[0008] Satellite remote sensing sub-module, used to collect data on the overall environmental conditions in the area, and collect data on the NDVI vegetation index, surface temperature, and water body distribution in the area;

[0009] Ground environmental monitoring sub-module, which monitors multi-dimensional ecological parameters including temperature, humidity, chemical oxygen demand (COD), ammonia nitrogen concentration, and biodiversity index in real time;

[0010] UAV inspection sub-module, used to obtain high-resolution images and video information of the local ecological environment, and carry an optical camera to obtain sub-meter resolution images of the local area;

[0011] Multi-source sensor acquisition ensures the acquisition of multi-dimensional data on the ecological environment, comprehensively reflects the ecological situation, and provides a rich and accurate data basis for subsequent analysis and simulation.

[0012] Data fusion and preprocessing module, whose functions are:

[0013] Perform unified format standardization processing on the data from the foregoing sub-modules;

[0014] Use data cleaning, complementing, and error correction algorithms to generate fusion data and form time-series data records; data fusion and preprocessing improve data quality, ensure unified and accurate data formats, reduce noise and errors, and facilitate subsequent analysis and model construction.

[0015] Dynamic prediction and adaptive simulation module, characterized in that:

[0016] Adopt a neural network model based on deep learning to perform real-time analysis on the time-series data output by the data fusion module;

[0017] Combine historical data with real-time data to predict the future spatio-temporal evolution trend of the ecological environment, and simulate the evolution effects under different ecological restoration schemes;

[0018] Automatically adjust the model parameters according to the prediction results to achieve adaptive simulation;

[0019] Dynamic prediction and adaptive simulation can predict the ecological environment change trend in advance, evaluate the effects of different restoration schemes, provide a scientific basis for ecological restoration decision-making, and improve the pertinence and effectiveness of restoration.

[0020] Virtual reality interactive experience module, whose functions include:

[0021] Using VR / AR display devices, dynamic simulation results are rendered into three-dimensional immersive scenes in real time;

[0022] Integrate gesture recognition and voice control interactive interfaces to enable users to adjust and operate simulation parameters online; after the user operates, the impact of the adjustment on the ecological environment simulation results is displayed in real time;

[0023] The virtual reality interactive experience module provides an immersive and convenient interactive method, which facilitates users to participate in and understand the ecological restoration process, and enhances users' cognition and participation in ecological restoration work.

[0024] The data closed-loop feedback control module has the following functions:

[0025] Collect user operation feedback data during virtual interaction;

[0026] The feedback data is transmitted to the dynamic prediction and adaptive simulation module and the data fusion module as the basis for model modification and data correction, realizing closed-loop adaptive optimization of the whole system; data closed-loop feedback control enables the system to be continuously optimized according to user feedback, improve model accuracy and system performance, and ensure long-term stable and efficient operation of the system.

[0027] In the ecological restoration dynamic simulation and interactive experience system described in the present invention, the data fusion and preprocessing module adopts the Kalman filter algorithm to perform multi-source data fusion to ensure the accuracy and consistency of the data. The state update formula is:

[0028]

[0029] in, is the state estimation, K k is the Kalman gain, z k is the observed value, H k is the observation matrix;

[0030] The abnormal data is eliminated by using the anomaly detection algorithm based on the 3σ criterion. The formula is:

[0031] If |x i -μ|>3σ, then x i Outliers

[0032] Among them, μ is the mean, σ is the standard deviation, and the missing data is supplemented by interpolation to generate fused data and form a time series data record. The Kalman filter algorithm is used to integrate data from different sensors (satellite remote sensing, ground monitoring, and drone inspection), and multiple observation data are comprehensively processed to obtain a more accurate state estimate.

[0033] The ecological restoration dynamic simulation and interactive experience system described in the present invention, the dynamic prediction and adaptive simulation module adopts a deep learning model based on the combination of long short-term memory network (LSTM) and convolutional neural network (CNN) to perform real-time analysis on the time series data output by the data fusion module. The LSTM and CNN combined model has the advantages of processing sequence information and extracting features, and can deeply explore the complex patterns and relationships in the time series data, accurately analyze the ecological environment data,

[0034] The forget gate formula of LSTM is: t =σ(W f ·[h t-1 , x t ]+b f )

[0035] Among them, f t is the output of the forget gate, σ is the sigmoid function, W f and b f are weight and bias respectively;

[0036] Combine historical data with real-time data to predict the future spatial and temporal evolution trend of the ecological environment and simulate the evolution effects under different ecological restoration schemes;

[0037] Through the back propagation algorithm and online learning technology, the gradient descent method is used to adjust the model parameters, and the update formula is:

[0038]

[0039] Among them, θ is the model parameter, α is the learning rate, and J(θ) is the loss function to achieve adaptive simulation.

[0040] The ecological restoration dynamic simulation and interactive experience system described in the present invention, the virtual reality interactive experience module uses the Unity3D engine to render the dynamic simulation results into a three-dimensional immersive scene in real time, and supports the Oculus Rift VR head display device;

[0041] The integration of Leap Motion gesture recognition technology and a voice recognition system based on natural language processing (NLP) supports users to adjust and operate simulation parameters online through gestures and voice commands. The Unity3D engine is combined with VR head-mounted display devices to build realistic three-dimensional scenes, enhance users' immersion in the ecological restoration simulation process, and enable them to more intuitively feel the changes in the ecological environment.

[0042] An ecological restoration dynamic simulation and interactive experience system according to the present invention, wherein the data closed-loop feedback control module collects operation records for adjusting vegetation distribution or water resource allocation, transmits the recorded data to the dynamic prediction and adaptive simulation module, and adjusts the model parameters by calculating the mean square error (MSE). The formula is as follows:

[0043]

[0044] where y i is the actual value, is the predicted value.

[0045] By collecting user operation data and adjusting the model parameters based on the mean square error, the model of the dynamic prediction and adaptive simulation module can better fit the actual situation, improve the accuracy of predicting the ecological environment evolution trend and simulating the restoration plan, form a closed-loop feedback mechanism from user operation data collection to model parameter adjustment, enable the entire ecological restoration dynamic simulation and interactive experience system to continuously optimize itself, adapt to different user operations and ecological environment change scenarios, and ensure the long-term stable and accurate operation of the system.

[0046] An ecological restoration dynamic simulation and interactive experience system according to the present invention, wherein the neural network model adopted by the dynamic prediction and adaptive simulation module is a combined structure of a feedforward neural network and a recurrent neural network (RNN). The RNN part can be implemented by LSTM and is used to process the temporal dependence of multi-dimensional time series data. The model input is multi-dimensional time series data (including environmental parameters and user intervention measures), and the output is the predicted state of the future ecological environment. The training data set is regularly updated through the sliding window technique, and the Adam optimizer is used to achieve dynamic adjustment of the parameters to ensure the dynamic accuracy of the prediction model. The combined neural network structure takes into account the feature extraction ability of the feedforward neural network and the processing ability of RNN (LSTM) for the long-term dependence relationship of time series data, can fully exploit the hidden information in multi-dimensional time series data, and accurately analyze the data related to the ecological environment. It can accurately output the predicted state of the future ecological environment according to the input data, provide a reliable basis for formulating and adjusting the ecological restoration plan, improve the scientificity and effectiveness of the ecological restoration work, regularly update the training data set and dynamically adjust the parameters, enable the model to continuously adapt to new ecological environment data and changes, maintain good prediction performance, and ensure the stable and accurate operation of the system in different stages and scenarios.

[0047] An ecological restoration dynamic simulation and interactive experience system according to the present invention, the data closed-loop feedback control module realizes the following closed-loop control process: transmitting the operation records to the dynamic prediction and adaptive simulation module in real time as the basis for real-time model correction; comparing the model prediction results with the user feedback data, automatically identifying simulation errors, and adjusting the data processing parameters in the data fusion module; realizing the intelligent optimization of the ecological restoration plan and the dynamic improvement of the overall system performance through iterative update.

[0048] An ecological restoration dynamic simulation and interactive experience system according to the present invention, the multi-source sensor data acquisition module, the data fusion and preprocessing module, the dynamic prediction and adaptive simulation module, the virtual reality interactive experience module and the data closed-loop feedback control module achieve high-speed data transmission through 5G network or optical fiber communication, ensuring that the data delay is less than 10ms; using the Network Time Protocol (NTP) for time synchronization to ensure that the timestamp accuracy of the data of each module is at the millisecond level, and realizing the real-time and stability of the overall system collaborative work. It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0049] An ecological restoration dynamic simulation and interactive experience system according to the present invention obtains comprehensive environmental data through the multi-source sensor data acquisition module, including the overall environmental condition data of the acquisition area by the satellite remote sensing sub-module, the real-time monitoring of multiple parameters by the ground environmental monitoring sub-module, and the acquisition of local high-resolution image and video information by the UAV inspection sub-module; after the data fusion and preprocessing module standardizes, cleans, complements and corrects the errors of the data, reliable time series data is generated; the dynamic prediction and adaptive simulation module uses a deep learning model to predict the ecological environment evolution trend and simulate the effects of different restoration plans by combining historical and real-time data, and can also automatically adjust the model parameters; the virtual reality interactive experience module uses VR / AR display devices to render the simulation results into a three-dimensional immersive scene, integrating gesture and voice interaction interfaces, facilitating users to adjust the simulation parameters and view the impacts in real time; the data closed-loop feedback control module collects user operation feedback data and transmits it to other modules as the basis for correction and calibration, realizing the closed-loop adaptive optimization of the entire system. The data is transmitted at high speed between each module through 5G network or optical fiber communication, and the time is synchronized using the network time protocol to ensure the real-time and stability of the system, thereby providing a comprehensive, accurate and efficient simulation analysis and interactive experience tool for ecological restoration work, and helping to make scientific decisions and effectively restore the ecological environment. Description of the Drawings

[0050] Figure 1 It is the system module relationship diagram of the present invention. Detailed Embodiments

[0051] Embodiment 1

[0052] Such as Figure 1The figure shows the relationship diagram between modules of an ecological restoration dynamic simulation and interactive experience system of the present invention. The following is the specific system implementation:

[0053] 1. System configuration and environment setup

[0054] Implementation scenario: Taking a wetland ecological restoration project as the application background, the system is deployed in the ecological monitoring center, and the service objects are ecological restoration engineers and decision-making management personnel.

[0055] Hardware configuration:

[0056] Data acquisition end:

[0057] Satellite remote sensing data source: Connect to Landsat-9 satellite remote sensing images (resolution 30m, band coverage from visible light to thermal infrared)

[0058] Ground monitoring station: Deploy temperature and humidity sensors (accuracy ±0.5°C), multi-parameter water quality monitors (pH / COD / ammonia nitrogen detection accuracy 0.01 level), and bioacoustic monitoring equipment (sampling rate 48kHz)

[0059] Drone: DJI Matrice 300RTK equipped with Zenmuse P1 camera (45MP full-frame, ground resolution 2cm when flying at an altitude of 100m)

[0060] Data processing end:

[0061] Server cluster: 4 NVIDIA DGX A100 nodes, equipped with InfiniBand HDR 200Gb / s high-speed interconnection

[0062] Interactive terminal:

[0063] VR device: Oculus Quest Pro headset (binocular resolution 3664×1920, refresh rate 90Hz) Gesture recognition: Leap Motion controller (tracking accuracy 0.01mm)

[0064] Voice device: iFlytek XFS5152CE chip (Chinese recognition rate 98%)

[0065] Software configuration:

[0066] Data fusion platform: Build a real-time data stream pipeline based on Apache Kafka Prediction model: Implement a hybrid architecture of LSTM-CNN using TensorFlow 2.8 framework (3 LSTM layers, 256 hidden units; 3×3 CNN convolution kernel)

[0067] VR development platform: Unity 2021.3.6f1 engine, HDRP rendering pipeline

[0068] 2. Data Acquisition and Processing Flow

[0069] Step 1: Multi-source Data Synchronous Acquisition

[0070] The satellite remote sensing sub-module obtains the NDVI vegetation index (in the range of 0.78 - 0.85) and surface temperature data (18 - 32 °C) once a day

[0071] The ground monitoring station uploads water quality parameters (COD fluctuation range 15 - 28 mg / L) every 5 minutes

[0072] The UAV conducts 3 fixed-point inspections per week to obtain orthophotos of a 50-hectare core area (single coverage area 2.3 km 2 )

[0073] Step 2: Data Fusion Processing

[0074] Format Standardization:

[0075] Remote sensing data is converted to GeoTIFF format (coordinate system WGS84 UTM 50N)

[0076] Sensor data is unified into JSON format (timestamp accuracy 1 ms)

[0077] Kalman Filter Fusion:

[0078] A 6-dimensional state vector x = [temperature, humidity, COD, ammonia nitrogen, vegetation coverage, biomass] is established

[0079] The observation matrix H is set as the identity matrix, the process noise Q = 0.01I, and the observation noise R = 0.05I

[0080] Outlier Processing:

[0081] For the continuous 24-hour dataset, calculate μ = 22.3 °C, σ = 1.8 °C, and eliminate abnormal temperature records > 28.5 °C or < 16.1 °C

[0082] Linear interpolation is used to complete missing data (maximum continuous missing window 3 hours)

[0083] 3. Dynamic Prediction and Simulation Process

[0084] Model Training:

[0085] Input data: A time series dataset constructed from ecological monitoring data in the past 5 years (time step 1 day, feature dimension 12)

[0086] Training Parameters:

[0087] Sliding window size: 365 days (simulating annual cycle)

[0088] Optimizer: Adam (initial learning rate 0.001, β1 = 0.9, β2 = 0.999)

[0089] Batch size: 64, number of training epochs: 200

[0090] Real-time prediction:

[0091] After loading the latest fused data:

[0092] The LSTM layer extracts temporal features (memory cell state c t with a dimension of 128)

[0093] The CNN processes spatial features (the output feature map size of 3 convolutional layers is 14×14×64)

[0094] Output the prediction for the next 30 days:

[0095] The prediction error of the vegetation coverage rate is ±2.1% (95% confidence interval)

[0096] The RMSE of the water quality parameter prediction: COD = 1.8 mg / L, ammonia nitrogen = 0.12 mg / L

[0097] Scenario simulation:

[0098] Simulation scenario 1: Increase the reed planting area by 20%

[0099] The model shows that the bird diversity index increases by 15% after 3 months

[0100] Predict that the water body COD drops to 18.7±1.2 mg / L

[0101] Simulation scenario 2: Build ecological floating islands (coverage rate 5%)

[0102] The predicted reduction in ammonia nitrogen concentration is 32% (p<0.01)

[0103] 4. Virtual reality interactive operation

[0104] Three-dimensional scene construction:

[0105] Terrain model: Generated based on DEM data (accuracy 0.5 m, grid size 512×512)

[0106] Vegetation rendering: Instantiate and render more than 100,000 plant models (LOD grading: 50 m / 100 m / 200 m) Water body effect: Screen space reflection (SSR) + Smoothed Particle Hydrodynamics simulation (number of SPH particles: 500,000) Interactive operation example:

[0107] Gesture adjustment:

[0108] Spread and slide the five fingers: Adjust the vegetation distribution (response delay < 20 ms)

[0109] Fist clenching and rotation: Change the viewing angle (angular velocity 90° / s)

[0110] Voice command:

[0111] "Display the predicted status in 2043": Trigger long-term simulation (GPU rendering time 1.2 s)

[0112] "Compare Scenarios A / B": Generate a dual-viewport comparison view (resolution 1920×1080×2)

[0113] 5. Closed-loop feedback optimization example

[0114] Feedback data collection:

[0115] Record 10 adjustment operations of the engineer:

[0116] Average of 3.7 parameters adjusted each time

[0117] Median operation interval time 8.5 s

[0118] Model optimization process:

[0119] Calculate the mean squared error:

[0120] Initial prediction error MSE = 3.28

[0121] Drop to MSE = 1.07 after 5 iterations

[0122] Parameter adjustment:

[0123] Learning rate is adaptively adjusted to 0.0005

[0124] Update amount of LSTM forget gate bias Δb_f = 0.0032

[0125] Optimization effect:

[0126] Improvement in prediction accuracy:

[0127] Vegetation cover prediction error reduced by 41%

[0128] Prediction time range of water quality parameters extended to 45 days

[0129] System response time:

[0130] Data closed-loop delay optimized from 12 ms to 8.3 ms

[0131] 6. System performance verification

[0132] Real-time test:

[0133] End-to-end delay:

[0134] Sensor → VR display: 78 ms (5G network latency 9 ms ± 2 ms)

[0135] User operation → Model update: 105 ms (meeting the real-time interaction requirement of < 150 ms)

[0136] Stability verification:

[0137] Continuous operation test:

[0138] 72-hour trouble-free operation

[0139] Data throughput remains stable (average 1.2 GB / min)

[0140] GPU temperature is stable at 68 ± 3 °C

[0141] This embodiment fully demonstrates the implementation process of the system in a real ecological restoration scenario through specific parameter settings and operation procedures. Through actual verification, the system can improve the decision-making efficiency of the restoration plan by 60%, and the simulation prediction accuracy reaches 89.7%, significantly better than the 72.3% benchmark level of traditional methods.

[0142] The above embodiments are exemplary, aiming to illustrate the technical concept and characteristics of the present invention, so that those skilled in this field can understand the content of the present invention and implement it accordingly. It should not be used to limit the protection scope of the present invention. Any changes or modifications made according to the spirit and essence of the present invention should be covered within the protection scope of the present invention.

Claims

1. An ecological restoration dynamic simulation and interactive experience system, characterized in that, The system consists of the following modules and realizes data acquisition, processing, dynamic simulation, and interactive feedback according to the following steps: Multi-source sensor data acquisition module, including: Satellite remote sensing sub-module, used to collect data on the overall environmental conditions in the area; Ground environmental monitoring sub-module, used to monitor parameters such as temperature, humidity, pollution indicators, and biodiversity in real time; UAV inspection sub-module, used to obtain high-resolution images and video information of the local ecological environment; Data fusion and preprocessing module, whose functions are: Perform unified format standardization processing on the data from the aforementioned sub-modules; Generate fusion data using data cleaning, completion, and error correction algorithms, and form time-series data records. Dynamic prediction and adaptive simulation module, characterized in that: Adopt a neural network model based on deep learning to perform real-time analysis on the time-series data output by the data fusion module; combine historical data and real-time data to predict the future spatio-temporal evolution trend of the ecological environment, and simulate the evolution effects under different ecological restoration schemes; Automatically adjust the model parameters according to the prediction results to achieve adaptive simulation; Virtual reality interactive experience module, whose functions include: Use VR / AR display devices to render the dynamic simulation results into a three-dimensional immersive scene in real time; Integrate gesture recognition and voice control interaction interfaces to realize online adjustment and operation of simulation parameters by users; After user operations, display the impact of the adjustment on the ecological environment simulation results in real time; Data closed-loop feedback control module, whose functions are: Collect operation feedback data of users during the virtual interaction process; Transmit the feedback data to the dynamic prediction and adaptive simulation module and the data fusion module as the basis for model correction and data calibration, and realize the closed-loop adaptive optimization of the entire system.

2. An ecological restoration dynamic simulation and interactive experience system according to claim 1, characterized in that The data fusion and preprocessing module uses the Kalman filter algorithm for multi-source data fusion to ensure the accuracy and consistency of the data. The state update formula is: Among them, is the state estimation, K k is the Kalman gain, z k is the observation value, H k is the observation matrix; Use an outlier detection algorithm based on the 3σ criterion to eliminate outlier data. The formula is: If |x i - μ| > 3σ, then x i is an outlier where μ is the mean, σ is the standard deviation, and the missing data is completed by interpolation method to generate fusion data and form time-series data records.

3. An ecological restoration dynamic simulation and interactive experience system according to claim 2, characterized in that The dynamic prediction and adaptive simulation module uses a deep learning model combined with long short-term memory network (LSTM) and convolutional neural network (CNN) to perform real-time analysis on the time-series data output by the data fusion module The forgetting gate formula of LSTM is: f t = σ(W f · [h t-1 , x t + b f ) Among them, f t is the output of the forget gate, σ is the sigmoid function, W f and b f are the weight and bias respectively; Combine historical data and real-time data to predict the future spatio-temporal evolution trend of the ecological environment, and simulate the evolution effects under different ecological restoration schemes; Adjust the model parameters by using the gradient descent method through the backpropagation algorithm and online learning technology. The update formula is: where θ is the model parameter, α is the learning rate, and J(θ) is the loss function to achieve adaptive simulation.

4. An ecological restoration dynamic simulation and interactive experience system according to claim 1, characterized in that The virtual reality interaction experience module uses the Unity3D engine to render the dynamic simulation results into a three-dimensional immersive scene in real time and supports the Oculus Rift VR headset device; Integrates the Leap Motion gesture recognition technology and a speech recognition system based on natural language processing (NLP), supporting users to perform online adjustment and operation of simulation parameters through gesture and voice commands.

5. An ecological restoration dynamic simulation and interaction experience system according to claim 1, characterized in that The data closed-loop feedback control module collects operation records for adjusting vegetation distribution or water resource allocation, transmits the recorded data to the dynamic prediction and adaptive simulation module, and adjusts the model parameters by calculating the mean square error (MSE). The formula is: Among them, y i is the actual value, and is the predicted value.

6. An ecological restoration dynamic simulation and interaction experience system according to claim 3, characterized in that The neural network model adopted by the dynamic prediction and adaptive simulation module is a combined structure of a feedforward neural network and a recurrent neural network (RNN). The RNN part can be implemented by LSTM and is used to process the temporal dependence of multi-dimensional time series data. The model input is multi-dimensional time series data (including environmental parameters and user intervention measures), and the output is the predicted state of the future ecological environment. The training data set is regularly updated through the sliding window technology, and the Adam optimizer is used to achieve dynamic adjustment of parameters to ensure the dynamic accuracy of the prediction model.

7. An ecological restoration dynamic simulation and interaction experience system according to claim 5, characterized in that The data closed-loop feedback control module implements the following closed-loop control process: Transmits the operation record to the dynamic prediction and adaptive simulation module in real time as the basis for real-time model correction; Compares the model prediction result with the user feedback data, automatically identifies the simulation error, and adjusts the data processing parameters in the data fusion module; Realizes the intelligent optimization of the ecological restoration plan and the dynamic improvement of the overall system performance through iterative update.

8. An ecological restoration dynamic simulation and interaction experience system according to claim 1, characterized in that High-speed data transmission is achieved between the multi-source sensor data acquisition module, the data fusion and preprocessing module, the dynamic prediction and adaptive simulation module, the virtual reality interaction experience module, and the data closed-loop feedback control module through a 5G network or optical fiber communication, ensuring that the data delay is less than 10 ms; the network time protocol (NTP) is used for time synchronization to ensure that the timestamp accuracy of the data of each module is at the millisecond level, realizing the real-time and stability of the overall system collaborative work.

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