Digital evolution method and system of ecological system

Abstract

The invention provides a digital evolution method and system of an ecological system, and the method comprises the steps: obtaining animal and plant information, environment quality evaluation conditions and ecological system service function evaluation results in an ecological protection region according to biological data and environment data collected in the ecological protection region; generating a comprehensive feature matrix based on features extracted from animal and plant information, environment quality evaluation conditions and ecological system service function evaluation results, and performing deep learning on the comprehensive feature matrix to construct a digital twin engine; building a digital twinborn large model based on a digital twinborn engine, the large model static base information and a prediction model, wherein the prediction model supports natural evolution prediction and scene deduction; and in response to real-time data input into the digital twin large model, processing the real-time data and target data including ecological system historical data, and displaying ecological system panoramic dynamic information including a natural evolution prediction result and / or a scene deduction result on a visual interface.

Description

Technical Field

[0001] The present application relates to the field of ecological technology, and in particular to a method and system for digital evolution of an ecosystem. Background Art

[0002] With growing awareness of ecological conservation, ecosystem monitoring has become crucial for maintaining biodiversity and ecological balance. Real-time ecosystem monitoring can provide early warnings of environmental degradation, assess ecosystem services, and provide a basis for ecological restoration and sustainable management.

[0003] With the rapid development of digital technology, digital twin technology has gradually emerged and is being applied in various fields. A digital twin is a virtual digital model that corresponds to a physical entity by integrating multiple sources of information, including physical models, sensor data, and historical data. It can reflect the physical entity's state in real time and predict its future behavior. In the ecological field, although relevant technologies have been attempted to build digital twins of ecosystems for ecosystem monitoring, they still face many challenges.

[0004] 1. Data collection is single, mostly focusing only on the collection of environmental data, ignoring the importance of biological data, resulting in an incomplete assessment of ecosystem functions; 2. Due to the wide range of ecosystems and complex environments, there are problems such as difficulty in data collection and poor real-time performance; 3. At the data fusion level, data of different modalities often have different data structures and time resolutions. Simple data superposition cannot fully utilize the advantages of multi-source data, and it is difficult to obtain comprehensive data that accurately reflects the state of the ecosystem; 4. The comprehensive data obtained by simple data fusion is difficult to reflect the actual state of the ecosystem, resulting in distortion of prediction input and inability to obtain effective prediction results; 5. Data collaboration in fields such as ecology, geographic information, and computer science is difficult, making it difficult to form a sustainable digital twin ecosystem.

[0005] It can be seen that there are still many problems to be solved in applying digital twin technology in the ecological field to reflect the state of the ecosystem and predict the state of the ecosystem, and a feasible solution is urgently needed. Summary of the Invention

[0006] The present invention aims to provide a method and system for the digital evolution of an ecosystem to address the deficiencies in the prior art. The technical problems to be solved by the present invention are achieved through the following technical solutions.

[0007] In a first aspect, embodiments of the present application provide a method for digital evolution of an ecosystem, including: Based on the biological and environmental data collected in the ecological reserve, obtain information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within the ecological reserve; Generate a comprehensive feature matrix based on the features extracted from the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment results, and perform deep learning on the comprehensive feature matrix to construct a digital twin engine, wherein the comprehensive feature matrix is ​​composed of fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the plant and animal information, environmental quality assessment, and ecosystem service function assessment results corresponding to the current spatiotemporal unit; Constructing a digital twin large model based on the digital twin engine, large model static base information, and a prediction model, wherein the prediction model supports natural evolution prediction and scenario deduction under set scenarios; In response to real-time data input into the digital twin model, the real-time data and target data are processed, and panoramic dynamic information of the ecosystem is displayed on a visualization interface. The target data includes historical data of the ecosystem. The visualization interface is a virtual reality (VR) interface or an augmented reality (AR) interface. The panoramic dynamic information of the ecosystem includes at least one of natural evolution prediction results and scenario deduction results.

[0008] In a second aspect, an embodiment of the present application provides a digital evolution system for an ecosystem, including: An acquisition module is used to obtain information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within the ecological reserve based on biological and environmental data collected within the ecological reserve; Generate a learning module for generating a comprehensive feature matrix based on the features extracted from the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment results, and perform deep learning on the comprehensive feature matrix to construct a digital twin engine, wherein the comprehensive feature matrix is ​​composed of fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the plant and animal information, environmental quality assessment, and ecosystem service function assessment results corresponding to the current spatiotemporal unit; A construction module for constructing a digital twin large model based on the digital twin engine, large model static base information, and a prediction model, wherein the prediction model supports natural evolution prediction and scenario deduction under set scenarios; A processing and display module is used to process the real-time data input into the digital twin model in response to the real-time data and target data, and display the panoramic dynamic information of the ecosystem on a visualization interface, wherein the target data includes historical data of the ecosystem, and the visualization interface is a virtual reality (VR) interface or an augmented reality (AR) interface, and the panoramic dynamic information of the ecosystem includes at least one of natural evolution prediction results and scenario deduction results.

[0009] The technical solution provided in the embodiment of the present application, after analyzing biological data and environmental data to obtain animal and plant information, environmental quality assessment conditions and ecosystem service function assessment results in the ecological protection zone, extracts features from the animal and plant information, environmental quality assessment conditions and ecosystem service function assessment results to generate a comprehensive feature matrix, performs deep learning on the comprehensive feature matrix to construct a digital twin engine, and constructs a digital twin large model based on the digital twin engine, large model static base information and a prediction model that supports natural evolution prediction and scenario deduction under set scenarios. The digital twin large model processes the target data and real-time data input into the digital twin large model, and displays panoramic dynamic information of the ecosystem including natural evolution prediction results and / or scenario deduction results on a visual interface. This can realize the prediction of the future natural evolution state of the ecosystem and / or the deduction of the ecosystem state under different set conditions, so that the digital twin large model can more comprehensively and accurately reflect the changing trend of the ecosystem and provide strong support for ecological protection and management; the content provided by the digital twin large model is visualized through VR technology or AR technology to provide an immersive user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a digital evolution method for an ecosystem provided by an embodiment of the present application is shown; Figure 2 A simplified flowchart of the digital twin model processing real-time data and target data provided by an embodiment of the present application is shown; Figure 3 The overall implementation flow chart of the digital evolution method of the ecosystem provided by the embodiment of the present application is shown; Figure 4 An interactive architecture diagram provided by an embodiment of the present application is shown; Figure 5 A schematic diagram of the digital evolution system of the ecosystem provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0011] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0012] The present application embodiment provides a digital evolution method for an ecosystem, such as Figure 1 As shown, the following steps are included: Step 101: Based on the biological data and environmental data collected in the ecological protection zone, obtain information on plants and animals, environmental quality assessment, and ecosystem service function assessment results within the ecological protection zone.

[0013] After selecting an ecological protection zone, embodiments of the present application utilize data collection equipment (e.g., unmanned equipment equipped with various sensors) to collect biological and environmental data within the ecological protection zone during one or more specific time periods. After acquiring the biological and environmental data through data collection, the flora and fauna within the ecological protection zone are identified based on the biological data, and the environmental quality within the ecological protection zone is assessed based on the environmental data. This provides information on the flora and fauna within the ecological protection zone and provides an assessment of the environmental quality. Furthermore, the ecosystem service function assessment is obtained by comprehensively considering the biological and environmental data.

[0014] By identifying species in ecological reserves, we can understand information such as the species, quantity and distribution of plants and animals in the ecological reserves; by conducting environmental quality assessments of ecological reserves, it is helpful to timely discover environmental problems in the ecological reserves and provide a scientific basis for ecological protection and management; by obtaining the results of ecosystem service function assessments, we can reveal the complex interactions between organisms and the environment and have a more comprehensive understanding of the overall status of the ecosystem.

[0015] Step 102: Generate a comprehensive feature matrix based on the features extracted from the animal and plant information, environmental quality assessment, and ecosystem service function assessment results, and perform deep learning on the comprehensive feature matrix to construct a digital twin engine, wherein the comprehensive feature matrix is ​​composed of the fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the animal and plant information, environmental quality assessment, and ecosystem service function assessment results corresponding to the current spatiotemporal unit.

[0016] By analyzing biological and environmental data to obtain information on flora and fauna within the protected area, environmental quality assessments, and ecosystem service function assessments, we extract features from these data. These features are then aligned temporally and spatially to generate a comprehensive feature matrix. Deep learning is then performed on this comprehensive feature matrix to construct the digital twin engine.

[0017] The digital twin engine constructed through deep learning includes a dynamic computing layer and a rule engine layer, which are responsible for real-time computing and logical decision-making respectively, and constitute the core functions of the digital twin engine; the dynamic computing layer is located at the bottom layer, responsible for basic computing, and the rule engine layer is located at the upper layer, responsible for logical judgment and decision support.

[0018] When building a digital twin engine through deep learning of a comprehensive feature matrix, the comprehensive feature matrix is ​​used as input, and deep learning forms a dynamic computing layer. With the dynamic computing layer as the core, the rule engine layer is integrated and encapsulated into the digital twin engine. The resulting digital twin engine features real-time data access, state calculation, rule judgment, and response triggering capabilities. It serves as the core driver of the large digital twin model, supporting its dynamic operation and intelligent decision-making.

[0019] Step 103: Build a digital twin big model based on the digital twin engine, the big model static base information and the prediction model. The prediction model supports natural evolution prediction and scenario deduction under set scenarios.

[0020] The digital twin engine constructed through deep learning serves as the core computing and decision-making module of the digital twin large model, responsible for multiple dynamic functions such as real-time data processing, state calculation, and rule response. The large model's static base information provides the large model's spatial structure and basic attributes, including information such as terrain, vegetation type, and soil texture. This information provides the digital twin engine with a spatial reference framework, enabling its calculation results to be accurately located and displayed in geographic space. The predictive model is a model that supports natural evolution predictions and scenario deductions under set scenarios. That is, the predictive model can not only predict the natural evolution trends of the ecosystem, but also simulate the ecosystem's response to different environments or policy interventions by setting different input conditions (scenario parameters), thereby realizing scenario deduction. By predicting the future state of the ecosystem under natural evolution and / or conducting scenario deductions under set conditions, it can provide a strong basis for intelligent decision-making and sustainable management.

[0021] The digital twin engine is responsible for calculation and response; the static base information of the large model is the skeleton, which can provide spatial structure and basic properties; the predictive model is used to predict the future state of the ecosystem and deduce the state changes of the ecosystem under different environments or intervention conditions; the combination of the digital twin engine, the static base information of the large model and the predictive model is used to construct the digital twin large model.

[0022] It should be noted that when the prediction model performs scenario deduction, it simulates the response of the ecosystem under different environments or policy interventions by setting different input conditions, thereby achieving scenario deduction, which is a form of prediction. Scenario deduction is essentially a hypothesis-deduction, and its core purpose is to predict the future (such as how the ecosystem will evolve after a given scenario), but it can also be used for historical scenario review (such as using real scenario parameters from a certain year in the past (such as extreme drought) to reproduce historical states and verify the reliability of the digital twin model) and sensitivity analysis (such as quickly testing the immediate impact of different parameter combinations on the ecosystem at the current time point). The embodiment of this application introduces scenario deduction using the deduction of future states as an example.

[0023] Step 104: In response to the real-time data input into the digital twin model, the real-time data and the target data are processed, and the panoramic dynamic information of the ecosystem is displayed on a visualization interface. The target data includes historical data of the ecosystem. The visualization interface is a virtual reality (VR) interface or an augmented reality (AR) interface. The panoramic dynamic information of the ecosystem includes at least one of a natural evolution prediction result and a scenario deduction result.

[0024] After constructing the digital twin model, real-time data is fed into the model, which then processes both the real-time data and the target data. The real-time data includes real-time plant and animal information, environmental quality assessments, and ecosystem service function assessments, all fed into the model through data interfaces (such as the Internet of Things). The target data includes historical ecosystem data, which provides long-term trends and context, aiding the digital twin model in more accurate analysis. By processing the real-time and target data, the digital twin model can output dynamic, panoramic ecosystem information and display it on a visual interface.

[0025] The visualization interface in this embodiment is a VR (Virtual Reality) interface or an AR (Augmented Reality) interface, which utilizes VR technology or AR technology for visualization and converts abstract data into intuitive three-dimensional images or augmented reality images, allowing users to intuitively understand the panoramic dynamic information of the ecosystem through the VR interface or AR interface, thereby ensuring the user's visual experience.

[0026] The panoramic dynamic information displayed on the visualization interface includes at least one of natural evolution predictions and scenario simulation results. By using the digital twin model to analyze real-time data and historical ecosystem data, the future state of the ecosystem under natural evolution scenarios can be predicted. This allows for the early identification of potential ecological risks, the implementation of preventive measures, and the reduction of ecosystem vulnerability. Furthermore, resource allocation can be planned in advance, ecological protection and restoration measures can be optimized, and resource utilization efficiency can be improved. By using real-time data and historical ecosystem data to perform scenario simulations, the digital twin model can provide feedback on the ecosystem state under different set conditions, providing strong support for ecological protection decisions and optimal resource allocation.

[0027] The above implementation scheme of the embodiment of the present application, after analyzing biological data and environmental data to obtain animal and plant information, environmental quality assessment conditions and ecosystem service function assessment results in the ecological protection zone, extracts features from the animal and plant information, environmental quality assessment conditions and ecosystem service function assessment results to generate a comprehensive feature matrix, performs deep learning on the comprehensive feature matrix to construct a digital twin engine, and constructs a digital twin large model based on the digital twin engine, the static base information of the large model and the prediction model that supports natural evolution prediction and scenario deduction under set scenarios. The digital twin large model processes the target data and the real-time data input into the digital twin large model, and displays the panoramic dynamic information of the ecosystem including the natural evolution prediction results and / or scenario deduction results on a visual interface, which can realize the prediction of the future natural evolution state of the ecosystem and / or the deduction of the ecosystem state under different set conditions, so that the digital twin large model can more comprehensively and accurately reflect the changing trend of the ecosystem and provide strong support for ecological protection and management; the content provided by the digital twin large model is visualized through VR technology or AR technology to provide an immersive user experience.

[0028] The following describes the process of obtaining information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within ecological conservation areas. The process of obtaining information on flora and fauna, environmental quality assessments, and ecosystem service function assessments based on biological and environmental data includes: Generate a biological feature vector based on biological data and generate an environmental feature vector based on environmental data, wherein the biological data includes at least image data, audio data, and positioning data of animals and plants, and the environmental data includes at least meteorological data and physical environment data; Species identification is performed on biological feature vectors based on biological macro models, and environmental analysis is performed on environmental feature vectors based on environmental macro models to obtain information on flora and fauna within ecological protection areas and environmental quality assessments; The information on plants and animals, environmental quality assessment, biological characteristic vectors, environmental characteristic vectors and comprehensive characteristic vectors are processed to obtain the ecosystem service function assessment results of the ecological protection zone. The comprehensive characteristic vector is determined based on the fusion of biological characteristic vectors and environmental characteristic vectors as well as dynamically adjusted biological weights and environmental weights.

[0029] After acquiring biological and environmental data through data collection, a biological feature vector is generated based on the biological data, indicating the biological characteristics of the organisms within the ecological protection zone; and an environmental feature vector is generated based on the environmental data, indicating the environmental characteristics of the ecological protection zone. The collected biological data includes at least image data, audio data, and location data of plants and animals; the collected environmental data includes at least meteorological data and physical environment data. For example, environmental data includes, but is not limited to, temperature, humidity, soil conditions, water quality, light intensity, air pressure, wind speed, and seasonal factors. Collecting this environmental data provides contextual information on the survival and activities of plants and animals within the ecological protection zone, which, combined with the biological data, can more comprehensively describe the regional characteristics of the ecological protection zone.

[0030] After generating a biological feature vector based on biological data, the biological feature vector is subjected to species identification based on a pre-built biological macro model to obtain information about plants and animals. The biological macro model is a species identification model. Based on the biological macro model, inference analysis is performed on the biological feature vector to obtain information about plants and animals within the ecological protection zone output by the biological macro model, thereby effectively identifying species within the ecological protection zone. After generating an environmental feature vector based on environmental data, an environmental quality assessment is performed on the environmental feature vector based on the pre-built environmental macro model. The environmental macro model is an environmental assessment model. Based on the environmental feature vector, an environmental quality assessment of the ecological protection zone output by the environmental macro model can be obtained. The environmental quality assessment output by the environmental macro model includes, for example, an environmental quality score, which is determined based on a comprehensive assessment of environmental factors such as water quality, soil, and meteorological conditions within the ecological protection zone.

[0031] Based on biological feature vectors, animals and plants in ecological protection areas can be identified, and based on environmental feature vectors, the environmental quality in ecological protection areas can be evaluated. By identifying animals and plants in ecological protection areas, we can understand information such as the species, quantity and distribution of animals and plants in ecological protection areas; by evaluating the environmental quality of ecological protection areas, it is beneficial to timely discover environmental problems in ecological protection areas and provide a scientific basis for ecological protection and management.

[0032] After obtaining the information on animals and plants in the ecological protection zone and the environmental quality assessment situation in the ecological protection zone, the large assessment model is used to process the biological characteristic vectors, environmental characteristic vectors, comprehensive characteristic vectors, the information on animals and plants in the ecological protection zone and the environmental quality assessment situation, to evaluate the ecosystem service function of the ecological protection zone and obtain the ecosystem service function assessment results.

[0033] The comprehensive feature vector is determined by integrating the biological feature vector and the environmental feature vector. The biological feature vector reflects information such as biodiversity and species richness, while the environmental feature vector covers multiple environmental factors. By integrating the biological feature vector and the environmental feature vector to form a comprehensive feature vector, the ecological status of the ecological reserve can be more comprehensively assessed.

[0034] Optionally, when processing the plant and animal information, environmental quality assessment, biological feature vectors, environmental feature vectors, and comprehensive feature vectors to obtain the ecosystem service function assessment results of the ecological protection zone, the following may be included: Perform vector splicing based on the biological feature vector, the environmental feature vector and the corresponding weights to determine the comprehensive feature vector; Based on the optimized large-scale assessment model, comprehensive reasoning and analysis are conducted on biological characteristic vectors, environmental characteristic vectors, comprehensive characteristic vectors, animal and plant information and environmental quality assessment situations to generate ecosystem service function assessment results including ecosystem species diversity assessment results and ecosystem environment assessment results.

[0035] A comprehensive feature vector is determined based on the fusion of biological feature vectors, environmental feature vectors, and dynamically adjusted biological and environmental weights. By integrating biological and environmental feature vectors to form a comprehensive feature vector, the health of an ecological conservation area can be more comprehensively assessed. Furthermore, the comprehensive feature vector is formed not only based on the biological and environmental feature vectors but also takes into account the dynamically adjusted biological and environmental weights. The biological and environmental weights are dynamically adjusted based on the contribution of biological and environmental data to the assessment of ecosystem service functions. This dynamic adjustment mechanism allows for flexible weight adjustments based on the characteristics and conservation objectives of different ecological regions, thereby obtaining a targeted comprehensive feature vector. Compared to existing technologies that struggle to integrate multi-source heterogeneous data (such as biological and environmental data) and have fixed weights that cannot be dynamically adjusted, the present embodiment integrates biological data (such as images, audio, and location data) with environmental data (such as meteorological, soil, and water quality) to generate biological and environmental feature vectors. The comprehensive feature vector is then generated based on the dynamically adjusted weights, addressing the data integration challenge. Furthermore, the dynamic weight adjustment mechanism allows for flexible weight adjustments based on the data's real-time contribution to the assessment of ecosystem service functions, thereby more accurately reflecting the overall health of the ecosystem.

[0036] For example, a specific example of generating a comprehensive feature vector with dynamic weight adjustment is given here: 1. Input data and initial feature vector Biological feature vector (dimension = 4): B = [0.2, 0.95, 0.15, 0.5] (indicating the number of Siberian tigers, confidence, number of red deer, and activity range); environmental feature vector (dimension = 4): E = [0.6, 0.65, 0.68, 0.67] (indicating temperature, humidity, soil pH, and snow depth).

[0037] 2. Dynamic weight adjustment rules Default weights (ecological balance scenario): biological weight = 0.5, environmental weight = 0.5; Dynamic adjustment conditions: If the number of key species (such as the Siberian tiger) is detected to have decreased by more than 10%, the biological weight = 0.7 and the environmental weight = 0.3; if the environmental parameters (such as soil pH) exceed the threshold (<6.5 or >7.5), the environmental weight = 0.8 and the biological weight = 0.2.

[0038] Example scenario: The current Siberian tiger population has decreased by 15% compared to last month (triggering the key species protection rule), and the weights are adjusted to: biological weight = 0.7, environmental weight = 0.3.

[0039] 3. Weighted fusion calculation Weighted biological feature vector: B_weighted = biological weight * B = 0.7 * [0.2, 0.95, 0.15, 0.5] = [0.14, 0.665, 0.105, 0.35]; weighted environmental feature vector: E_weighted = environmental weight * E = 0.3 * [0.6, 0.65, 0.68, 0.67] = [0.18, 0.195, 0.204, 0.201].

[0040] Comprehensive feature vector (after splicing): F = concat(B_weighted, E_weighted) = [0.14, 0.665, 0.105, 0.35, 0.18, 0.195, 0.204, 0.201].

[0041] The evaluation model is a large-scale model used to assess ecosystem service functions. It uses the model to intelligently and comprehensively analyze biological feature vectors, environmental feature vectors, comprehensive feature vectors, information on flora and fauna within ecological protection areas, and environmental quality assessments. This can reveal the complex interactions between organisms and the environment, and provide a more comprehensive understanding of the overall state of the ecosystem, thereby enabling a more comprehensive, accurate, and in-depth assessment of ecosystem service functions. The evaluation model can be optimized from multiple perspectives, including data quality, model architecture, training strategies, and evaluation metrics, to improve the accuracy and reliability of the model's assessment of ecosystem service functions. Pre-training or transfer learning can also be used to pre-train the model on large amounts of relevant data, improving its performance and generalization capabilities. For example, relevant data can be fed into the evaluation model in advance to allow the model to learn.

[0042] The ecosystem service functions evaluated in the embodiments of the present application include the results of ecosystem species diversity assessment and ecosystem environment assessment, such as biodiversity, water purification, climate regulation, soil fertility maintenance, etc. By comprehensively and in-depth evaluating ecosystem service functions, more scientific decision-making support can be provided for ecological protection and resource management, thereby formulating effective protection strategies in a targeted manner.

[0043] It should be noted that the biological and environmental feature vectors, respectively, encompass biological and environmental information within the ecological reserve. These two vectors provide foundational data from which more specific biological information (primarily regarding flora and fauna) and environmental quality assessments can be extracted. The flora and fauna information derived from analyzing the biological feature vectors represents higher-level information, such as species diversity and the distribution of key species. The environmental quality assessments derived from analyzing the environmental feature vectors also represent higher-level information, such as water quality compliance and soil contamination levels. While individual flora and fauna information and environmental quality assessments provide important information, they are preliminary results and may lose some details and potential connections within the original data. Analyzing the biological, environmental, and combined feature vectors, while also considering flora and fauna information and environmental quality assessments within the ecological reserve, can uncover more potential information and relationships.

[0044] By comprehensively analyzing biological characteristic vectors, environmental characteristic vectors, comprehensive characteristic vectors, information on plants and animals in ecological protection areas, and environmental quality assessments, we can reveal the complex interactions between organisms and the environment, and gain a more comprehensive understanding of the overall status of the ecosystem. This will enable us to evaluate the ecosystem service functions in a more comprehensive, accurate, and in-depth manner, and obtain the results of the ecosystem service function assessment.

[0045] For example, here is a specific example of how the ecosystem service function assessment results are generated: Based on the comprehensive analysis of biological and environmental data by the evaluation model, quantitative indicators of service function modalities are generated: (1) Carbon sink (t / ha): Input layer: Vegetation cover data: Broadleaf forest proportion (45%), coniferous forest proportion (30%); Meteorological data: Monthly average temperature (5.2°C), historical benchmark temperature (6.0°C); Processing flow: Calculation of vegetation carbon sequestration potential: The carbon sequestration coefficient of broadleaf forest is α=0.8, and that of coniferous forest is β=0.6; Basic carbon sink value = (0.45×α + 0.3×β) = 0.54; Temperature adaptability correction: Use Sigmoid response function (the carbon sequestration efficiency is maximized when the optimum temperature is met (should be 1.0)):

[0046] Wherein, T represents the current ambient temperature (5.2° C. in the embodiment). T0 represents the optimum temperature of the species (or vegetation) (set to 6.0° C. in the embodiment).

[0047] Ecological significance: When the ambient temperature is equal to T0, biological functions (such as carbon sequestration efficiency) reach the optimal state.

[0048] k: represents the sensitivity coefficient (k=0.5), which controls the steepness of the curve and reflects the sensitivity of the species to temperature deviation.

[0049] Temperature correction factor = ≈ 0.8.

[0050] Final carbon sink: Normalized value = basic carbon sink value × temperature correction factor = 0.54 × 0.8 ≈ 0.43.

[0051] (2) Water conservation capacity (%) Input layer: Hydrological data: monthly precipitation (120 mm); soil data: pH value (7.8), snow depth (15 cm); historical benchmark: average annual precipitation (100 mm); Processing flow: Calculation of basic conservation capacity: precipitation factor = min(current precipitation / historical benchmark, 1.2) = 1.2; Soil water retention amendment: pH deviation penalty: When pH When [6.5,7.5], the penalty coefficient γ=0.9; Snow compensation: δ=h*(snow thickness / reference thickness) Snow thickness: measured value (15cm); Baseline thickness: Take the historical average snow thickness of the region in winter (e.g. 22.5cm, which needs to be adjusted according to actual data); Compensation coefficient h: The value range is 0.1~0.3 (needs calibration), which represents the contribution intensity of unit snow thickness to water conservation. In this embodiment, the value is 0.3; δ=0.3×(15cm / 22.5cm) = 0.2; Soil correction factor = γ + δ = 1.1; Comprehensive calculation: Original value = precipitation factor × soil correction factor = 1.2 × 1.1 = 1.32; Normalized value = tanh(original value) ≈ 0.72.

[0052] (3) Biodiversity Index Input layer: Species data: Siberian tiger index (0.58), red deer index (0.15); Vegetation data: Broadleaf forest (45%), shrub (25%); Processing flow: Species diversity calculation: Key species weights: Siberian tiger w1=0.6, red deer w2=0.4; Animal diversity = w1 × Siberian tiger index + w2 × red deer index = 0.6 × 0.58 + 0.4 × 0.15 ≈ 0.41; Vegetation diversity calculation: Shannon index calculation: H'=-Σ(p i ×ln(p i )) Among them, p i represents the relative proportion of the i-th species (or vegetation type) in the community (i.e., the ratio of the number of individuals or area covered by the species to the total). For example: if broadleaf forest accounts for 45% → p1 = 0.45; if coniferous forest accounts for 30% → p2 = 0.3; if shrubs account for 25% → p3 = 0.25.

[0053] ln(p i ): indicates the p i Take the natural logarithm (base e).

[0054] If p i = 0 (a species does not exist), then it is agreed that p i ×ln(p i ) = 0 (to avoid mathematical undefinedness).

[0055] Σ (cumulative): represents the p for all species (or vegetation types) i ×ln(p i ) values.

[0056] Vegetation composition ratio: broad-leaved forest 0.45, coniferous forest 0.3, shrub 0.25.

[0057] H' ≈ 1.03, which is 0.68 after normalization.

[0058] Composite Index: The geometric mean was used: sqrt(animal diversity × vegetation diversity) = sqrt(0.41 × 0.68) ≈ 0.53; After logistic regression calibration, the output was 0.61; The service function feature vector is obtained: S = [0.65, 0.72, 0.61].

[0059] In the above implementation plan for obtaining animal and plant information, environmental quality assessment and ecosystem service function assessment results, the animal and plant information in the ecological protection zone is identified based on the biological big model analysis of biological characteristic vectors, and the environmental quality of the ecological protection zone is assessed based on the environmental big model analysis of environmental characteristic vectors. By identifying animals and plants in the ecological protection zone, information such as the species, quantity and distribution of animals and plants in the ecological protection zone can be understood; by conducting environmental quality assessment of the ecological protection zone, it is beneficial to timely discover environmental problems in the ecological protection zone and provide a scientific basis for ecological protection and management.

[0060] By integrating multi-dimensional data and dynamically adjusting weights, a comprehensive and dynamic assessment of ecosystem service functions is achieved. Compared with static assessments that focus on a single or a few service functions, this application achieves a comprehensive and dynamic assessment of ecosystem service functions through multimodal data fusion and dynamic analysis, which can more realistically reflect the changing trends of the ecosystem.

[0061] The following describes the solution for building a digital twin engine. This involves generating a comprehensive feature matrix based on features extracted from plant and animal information, environmental quality assessments, and ecosystem service function assessments. Deep learning is then performed on the comprehensive feature matrix to build the digital twin engine. This includes: After preprocessing and aligning the multi-source data, features are extracted from the multi-source data and fused to generate a fused feature vector corresponding to the same time and space dimensions. A comprehensive feature matrix is ​​constructed based on multiple fused feature vectors. The multi-source data includes animal and plant information, environmental quality assessment results, and ecosystem service function assessment results. Deep learning is performed on the comprehensive feature matrix to generate a dynamic kernel consisting of a weight matrix, a response function, and a threshold rule. The weight matrix is ​​used to quantify the interaction between species and the environment and service functions; the response function is used to describe the response relationship between ecosystem variables; the threshold rule is used to define the critical state of the ecosystem; and the dynamic kernel is used to calculate the ecosystem state, assess the trend of ecosystem state changes, and identify critical states. Encapsulate the dynamic kernel and rule engine to build a digital twin engine. The rule engine makes decision responses based on the output results of the dynamic kernel.

[0062] After obtaining plant and animal information, environmental quality assessments, and ecosystem service function assessment results through the above processing, multi-source data is constructed based on these information, environmental quality assessments, and ecosystem service function assessment results. By sequentially performing data preprocessing, data alignment, feature extraction, and feature fusion on the multi-source data, a fused feature vector corresponding to the same temporal and spatial dimensions is generated. A comprehensive feature matrix is ​​constructed based on these multiple fused feature vectors. In other words, the comprehensive feature matrix is ​​a structured data representation generated through preprocessing, spatiotemporal alignment, feature extraction, and feature fusion of multi-source heterogeneous data. It is the core input for constructing a large digital twin model and provides a unified, standardized data foundation for a series of subsequent operations.

[0063] The process of generating a comprehensive feature matrix mainly involves four steps: 1. Data preprocessing and data format unification; 2. Spatiotemporal alignment and gridding; 3. Feature extraction and vectorization; 4. Feature fusion and matrix generation.

[0064] In the data preprocessing and data format unification steps, the provided data (including animal and plant information, environmental quality assessment, and ecosystem service function assessment results) are preprocessed, such as denoising, outlier removal, and missing value filling, and the data format is unified (such as rasterization and time series alignment), and data processing is performed based on dimensional normalization or standardization.

[0065] In the spatiotemporal alignment and gridding steps, data are aligned in time and space based on a unified spatiotemporal benchmark to determine standard data. For example, a unified spatial grid (e.g., 10m×10m) is created at the spatial level, and a unified temporal resolution (e.g., 1 hour, 10 hours, etc.) is determined at the temporal level. Alignment can be performed using, for example, spatial interpolation to map species observation point data to the grid, and temporal interpolation to align data with different sampling frequencies to a unified timestamp.

[0066] During the feature extraction and vectorization step, multimodal features (biometric, environmental, and service function features) are extracted from the standard data to generate fixed-dimensional feature vectors. All feature vectors are organized into grid-time units. Biometric features include image features, audio features, and positioning features. Environmental features include time-series statistical features extracted from meteorological and physical environment data. Service function features are features extracted from service function data.

[0067] During the feature fusion and matrix generation steps, feature vectors from different modalities are concatenated. Feature importance can also be considered, assigning different weights to feature vectors from different modalities. After feature fusion, each grid-time unit corresponds to a fused feature vector. All fused feature vectors form a comprehensive feature matrix, which serves as input for subsequent modeling.

[0068] In the implementation process of generating a comprehensive feature matrix, consistency checks can be used to ensure the spatial consistency of different data sources within the same grid; integrity checks can be used to identify the presence of null values ​​or outliers; and logical checks can be used to verify the ecological rationality between species distribution and environmental conditions (for example, wetland species should not appear in arid areas).

[0069] By aligning the three types of data, namely species, environment, and services, to a unified grid and time axis, scale differences are eliminated and the comparability of variables in the same dimension is ensured; and plant and animal information supplements biological data, environmental data depicts the background information of species, and service functions provide ecological output indicators. The three complement each other to increase the amount of information; the matrixed data retains effective features and improves the data signal-to-noise ratio.

[0070] Exemplarily, an embodiment of generating a comprehensive feature matrix is ​​given here: 1. Assume input data (single spatiotemporal unit) Spatiotemporal unit: Grid A of a certain ecological protection area, time: 2023-10-01 10:00.

[0071] The input data includes the following: Plant and animal information (biological characteristics): Species 1 (Siberian tiger): number = 2, confidence = 0.95, activity range = 5km²; Species 2 (red deer): number = 15, confidence = 0.90.

[0072] Environmental quality assessment (environmental characteristics): temperature = 18°C, humidity = 65%, soil pH = 6.8, snow depth = 20 cm.

[0073] Ecosystem service function assessment results (service function characteristics): carbon sequestration = 2.4 t / ha, water conservation capacity = 85%, biodiversity index = 0.78.

[0074] 2. Generate fusion feature vector of single spatiotemporal unit (1) Data preprocessing Normalize the values ​​(scale to [0, 1]): Number of Siberian tigers: 2 → normalized to 0.2 (assuming the maximum number of tigers in the area = 10); temperature: 18°C ​​→ 0.6 (assuming the temperature range is [-10, 30]°C); carbon sink: 2.4t / ha → 0.8 (assuming the maximum carbon sink = 3 t / ha). (2) Multimodal feature extraction Biological feature vector (dimension = 4): [0.2, 0.95, 0.15, 0.5] (Note: 0.15 = normalized value of elk population, 0.5 = normalized value of activity range); Environmental feature vector (dimension = 4): [0.6, 0.65, 0.68, 0.67] (Note: 0.65 = normalized value of humidity, 0.68 = normalized value of soil pH, 0.67 = normalized value of snow depth); Service function modal feature vector (dimension = 3): [0.8, 0.85, 0.78] (Note: 0.85 = normalized value of water conservation capacity).

[0075] It should be noted that, in this embodiment, it is still preferred to consider dynamically adjusting the weights of each modality.

[0076] The weights of the three types of vectors must meet the normalization condition: biological weight + environmental weight + service weight = 1. The following is an example of dynamic weight calculation: basic weight (default scenario): biological weight = 0.4, environmental weight = 0.4, service weight = 0.2.

[0077] Example adjustment rule: If the species diversity index drops below a threshold (e.g., 10%), then: biological weight = 0.6, environmental weight = 0.3, service weight = 0.1.

[0078] If the water pH exceeds the safe range (6.5-7.5), then: environmental weight = 0.7, biological weight = 0.2, service weight = 0.1.

[0079] In the scenario prioritizing the protection of key species, the input data are: the number of Siberian tigers decreased by 15% month-on-month (triggering the protection rule); environmental parameters are normal (pH = 6.8, temperature = 18°C); carbon sequestration is stable, and the weights are adjusted: biological weight = 0.6, environmental weight = 0.3, and service weight = 0.1.

[0080] Weighted eigenvectors: Biometric feature vector B = [0.2, 0.95, 0.15, 0.5]→0.6*B = [0.12, 0.57, 0.09,0.3]; Environmental feature vector E = [0.6, 0.65, 0.68, 0.67]→0.3*E = [0.18, 0.195,0.204, 0.201]; Service function vector S = [0.8, 0.85, 0.78]→0.1*S = [0.08, 0.085, 0.078].

[0081] 3. Feature stitching Concatenate the vectors of the three modalities into a fused feature vector (dimension = 11): F = concat(0.6B, 0.3E, 0.1S) = [0.12, 0.57, 0.09, 0.3,0.18, 0.195, 0.204, 0.201, 0.08, 0.085, 0.078].

[0082] 4. Construct a comprehensive feature matrix Repeat the above process to generate fused feature vectors of multiple spatiotemporal units and arrange them in spatiotemporal order:

[0083] The final generated comprehensive feature matrix is ​​in the form of: [0.12, 0.57, 0.09, 0.30, 0.18, 0.195, 0.204, 0.201, 0.08, 0.085,0.078], grid A 10:00; [0.18, 0.552, 0.108, 0.30, 0.186, 0.189, 0.210, 0.195, 0.079, 0.083,0.077], grid B 11:00; [0.00, 0.00, 0.00, 0.00, 0.165, 0.210, 0.195, 0.180, 0.07, 0.075,0.065], grid C 10:00.

[0084] After obtaining the comprehensive feature matrix, a deep learning model (such as the neural network shown in the figure) conducts deep learning on the comprehensive feature matrix, outputting a weight matrix, response function, and threshold rules. Together, these form the dynamic kernel, providing logical support for subsequent calculations. The dynamic kernel is then integrated with the rule engine, encapsulating it as the digital twin engine. The digital twin engine provides real-time data access, state calculation, rule judgment, and response triggering capabilities. It is the core driver module of the large digital twin model, supporting its dynamic operation and intelligent decision-making.

[0085] Species, environment, and service functions are all represented as variables in deep learning models. For example, species variables include species richness, diversity index, and the presence of key species. Environmental variables include water pH, soil organic matter, temperature, and precipitation. Service function variables include carbon sequestration, water conservation capacity, and pollination service intensity. The weight matrix includes coupling weights between species and environment, species and service functions, environment and service functions, and species and environment and service functions. A response function describes the response relationship between ecosystem variables by expressing how one variable (such as an environmental factor) changes in another variable (such as species richness or service functions). It defines the mode and intensity of influence between variables and can be linear, nonlinear, or conditional. For example, a response function can represent the effect of improved water quality on species richness or the enhancement of carbon sequestration capacity by increased vegetation cover.

[0086] When performing deep learning on the comprehensive feature matrix, the comprehensive feature matrix is ​​input into the deep learning model, the coupling relationship between species, environment and service functions is learned through model training, and the weight matrix between species, environment and service functions is output; a nonlinear activation layer is embedded in the deep learning model to learn the response relationship between variables and obtain the response function; loss function constraints are introduced during the model training process to automatically learn key ecological thresholds and obtain threshold rules.

[0087] The deep learning model is used to learn the coupling relationship between species, environment and service functions from the comprehensive feature matrix; the nonlinear activation layer is used to introduce nonlinear transformations to enable the model to fit more complex functional relationships. The nonlinear activation layer is embedded in the model to enhance the model's ability to model the nonlinear response relationship between ecosystem variables, fit the complex functional relationship between species, environment and service function variables in the ecosystem, and thus determine the response function; by introducing loss function constraints, threshold information can be learned, and then the threshold rule can be obtained.

[0088] Learning the coupling relationships between species, environments, and service functions is essentially learning the coupling relationships between variables. Learning the coupling relationships between species, the environment, and service functions is essentially modeling the interactions and response mechanisms between variables through deep learning. Deep learning models automatically extract coupling weights, response functions, and threshold rules by learning the correlations, causality, or response patterns between these variables.

[0089] Through deep learning of the comprehensive feature matrix, coupling weights for species and environment, species and service functions, environment and service functions, and multi-factor synergy are derived to form a weight matrix. The multi-factor synergy coupling weight can be understood as the combined influence, or coupling weight, of species and environment on ecosystem services. It not only considers the impact of species on services or the impact of the environment on services in isolation, but also comprehensively considers the interactions between species and environment, as well as their synergistic effects on services (such as carbon sequestration, water conservation, and biodiversity maintenance). For example, the species-environment-service synergy weight indicates how changes in species and service functions interact under specific environmental conditions, or how environmental changes affect service functions under specific species composition. For example, if increased precipitation (an environmental factor) and increased vegetation cover (a species factor) combine to significantly improve water conservation capacity, the multi-factor synergy coupling weight indicates the strength of the positive impact of these two combined effects on water conservation. For example, under the combined effects of soil acidification (environmental factor) and the reduction of key species (species factor), the soil carbon sequestration capacity has significantly decreased, and the coupling weight of multi-factor synergy represents the intensity of the negative impact of the combined effects of the two on carbon sequestration.

[0090] In this embodiment, the dynamic kernel composed of a weight matrix, a response function, and threshold rules is responsible for calculating the interactions and state changes between various elements of the ecosystem (species, environment, and service functions) in real time, analyzing the trend of ecosystem state changes, identifying critical situations, and outputting the current system state, the analyzed state change trend, and whether the critical conditions have been met; the rule engine performs logical judgment and response actions based on the output of the dynamic kernel to achieve intelligent feedback and decision support.

[0091] Specifically, the dynamic kernel is primarily used to calculate the current real-time state of the ecosystem, analyze state change trends, and identify critical states. For example, when calculating the real-time state of the ecosystem, it assesses species status (such as species richness, diversity, and distribution), environmental status (such as water quality, soil, and meteorological parameters), and service function status (such as carbon sequestration capacity, water conservation capacity, and biodiversity maintenance capacity). When analyzing state change trends, it uses recent data, historical ecosystem data, and response functions to calculate the direction and magnitude of change in the current state relative to the previous moment. For example, it assesses whether species richness is increasing or decreasing, water quality is improving or deteriorating, and service capacity is increasing or decreasing. When identifying critical states, it determines whether ecological thresholds have been reached (such as whether pH is less than 6.5 or whether species richness has decreased by more than 20%). The dynamic kernel can provide input to the rule engine, for example, by transmitting the current ecosystem state value, state change trends, and critical judgment results to the rule engine, which then processes the information accordingly.

[0092] It should be noted that the dynamic kernel mainly processes real-time data, but the historical data of the ecosystem serves as background information to enhance the accuracy and reliability of the analysis and to assist in a more comprehensive and accurate understanding of the changes in the ecosystem.

[0093] The rules engine is primarily responsible for performing the following tasks based on the current state, state change trends, and critical identification results output by the dynamic core: 1. Logical judgment and decision triggering; 2. Response action execution. During logical judgment and decision triggering, it determines whether pre-set business rules or response conditions are met. For example, a pH value below 6.5 triggers an acidification warning, a species richness decrease of more than 20% triggers an ecological degradation alert, and a carbon sink estimate decrease of more than 10% triggers a service adjustment recommendation. When executing a response action, the rules engine automatically executes matching actions based on the judgment results, such as issuing a warning and recommending intervention measures (such as planting water-purifying plants or limiting pollutant discharge). The rules engine also supports feedback of judgment results and response actions to a visual interface, allowing relevant personnel to review risks and intervention recommendations, providing auxiliary information for ecological protection and resource regulation decisions. It is important to note that the rules engine not only considers the current state but also integrates state change trends in its logical judgment, thereby enabling more intelligent and proactive ecological response and decision support.

[0094] Specifically, the dynamic core calculates the current state of the ecosystem, analyzes state change trends, and identifies critical areas, providing a basis for real-time monitoring and response. Based on the dynamic core's output of the current state, state change trends, and critical areas, the rule engine, combined with pre-set business rules, executes logical judgments and triggers corresponding response actions, enabling intelligent feedback and decision support for the ecosystem. As a key module for achieving intelligent feedback and decision support, the rule engine forms a closed-loop "judgment + response" mechanism with the dynamic core, building the digital twin engine as the core driver of the digital twin model.

[0095] In the above implementation plan for building a digital twin engine, deep learning is performed on the comprehensive feature matrix to generate a dynamic kernel that calculates the state of the ecosystem, evaluates the trend of state changes, and identifies critical states. The dynamic kernel and the rule engine are integrated to build a digital twin engine, which can provide guarantees for the subsequent generation of large digital twin models.

[0096] The following describes the implementation process of generating a prediction model and building a large digital twin model based on the digital twin engine and prediction model. The following steps are involved in generating a prediction model: Determine a data sample set based on historical ecosystem samples with labeled scenario categories and labeled data indicating real observations at the target moment. The data sample set includes a training set, a validation set, and a test set. The labeled data is the model training target, and the target moment is the future moment relative to the historical moment. Train machine learning models suitable for ecosystem time series forecasting based on the training set, update model parameters through optimization algorithms, and evaluate model performance based on the validation set; The prediction accuracy and scenario response consistency of the model are evaluated based on the test set, and the model structure and hyperparameters are tuned based on the evaluation indicators to determine the prediction model.

[0097] The prediction model trained in this embodiment has the ability to predict the future state of an ecosystem and to deduce its state. These two core functions are not mutually exclusive and together constitute the intelligent analysis and decision-making capabilities of the digital twin model. When predicting the future state of an ecosystem, the model uses historical and current data to predict the natural evolution of the ecosystem at a specific time in the future, providing a reference for future trends. When performing deductions, the model generates deduction results based on the provided data and the set scenarios.

[0098] During the prediction model training phase, scenario categories, real-world observations, and historical ecosystem samples are incorporated into the process. The goal of predictive model training is to predict future states and ensure responsiveness to different scenario inputs. Historical ecosystem samples serve as input features for the prediction model, describing ecosystem states (recording the historical conditions of species, the environment, and service functions), documenting evolutionary processes (recording changes in the ecosystem over time), reflecting variable relationships (reflecting the interactions between species, the environment, and service functions), and identifying anomalous events (capturing sudden impacts such as pollution, disasters, and invasions). They serve as fundamental data for model training and validation, helping the model learn the relationships between ecosystem operating patterns and variables. By labeling historical ecosystem samples with scenario categories, the model understands which changes occur under which scenarios when learning historical patterns, thereby developing responsiveness to different scenarios. Labeled data, representing real-world observations, is essential for training the prediction model. As the target output of model training, they enable the model to learn the mapping between inputs and outputs, thereby accurately predicting future states. Labeled data is not required in the inference mode, as the goal is to simulate responses under specific scenarios, not to learn the mapping between inputs and outputs.

[0099] The model training process includes the following stages: data preparation (collecting historical samples, labeling data, scenario categories, and dividing data sets), feature engineering (extracting time series features, constructing spatiotemporal features, scenario encoding, and standardization), model construction (multi-input structure, model selection, and introduction of enhancement mechanisms), model training (supervised learning, multi-task learning, and performance evaluation), and model evaluation and tuning (performance evaluation and tuning methods).

[0100] During the data preparation stage, historical samples of ecosystems (such as species status, environmental status, and service function status) are collected as input features; real observations at a certain moment in the future are collected as label data; scenario categories (such as climate scenarios and intervention measure types) are labeled for each sample, and the source of the scenario categories is clarified; training sets, validation sets, and test sets are divided to ensure that the scenario categories are evenly distributed in each set.

[0101] During the feature engineering phase, extract temporal features (sliding window mean, trend, seasonality), clarify the sliding window size and seasonal cycle; construct spatiotemporal features (neighborhood mean, spatial lag), and clarify the definition of spatial neighborhood; encode scenario categories as numerical values ​​or embedded vectors; and standardize features to improve training efficiency.

[0102] During the model building phase, a multi-input structure is adopted, such as LSTM, Transformer, GNN and other models, and a conditional generation mechanism or attention mechanism is introduced to enhance the scenario response capability and clarify the specific form of conditional input.

[0103] During the model training phase, supervised learning is performed using the training set, and the loss function uses mean squared error (MSE) or mean absolute error (MAE) to measure the prediction error. Multi-task learning is used to simultaneously predict the states under natural evolution and scenario intervention. Performance is evaluated on the validation set to prevent overfitting.

[0104] During the model evaluation and tuning phase, we assess prediction accuracy and scenario response consistency on the test set, clarify the evaluation objectives, adjust the model structure and hyperparameters, and improve robustness and generalization. Prediction accuracy measures the closeness between the model's predicted values ​​and the true observed values, while scenario response consistency measures the rationality and stability of the model's output under different scenarios.

[0105] After generating a prediction model through model training, a digital twin large model is constructed based on the digital twin engine, large model static base information, and the prediction model, including: Load the static base information of the large model used to construct the 3D spatial skeleton and basic attributes into the digital twin engine to complete spatial registration and logical binding; Integrate the trained prediction model into the digital twin engine; Configure various model parameters in the digital twin engine to initialize the digital twin large model.

[0106] The static base information of the large model is used to construct the three-dimensional spatial skeleton and basic properties of the digital twin large model. It constructs the three-dimensional spatial skeleton and basic properties of the digital twin large model by providing terrain structure and ecological properties, which is the spatial and property basis for the operation of the large model.

[0107] The three-dimensional spatial skeleton includes, for example, a terrain DEM (Digital Elevation Model), spatial gridding, and spatial positioning. The terrain DEM provides information such as elevation, slope, and aspect at each spatial point, thereby providing continuous elevation and terrain attributes. Spatial gridding divides the ecological region into regular grids (e.g., 10m x 10m), with each grid corresponding to a spatial unit, thereby discretizing the DEM into numbered grid cells. Spatial positioning spatially locates and overlays all dynamic data (species distribution, environmental parameters) based on this skeleton. In other words, grid IDs are used to accurately map real-time data such as species and environmental conditions to corresponding terrain units.

[0108] Basic attributes include, for example, a vegetation type raster and a soil texture map. The vegetation type raster provides rasterized vegetation attribute values, while the soil texture map provides rasterized soil attribute values. By unifying grid coordinates, vegetation, soil, and dynamic data are aligned on the same spatial basis to avoid misalignment. The vegetation type raster characterizes cover by annotating specific vegetation types (forest, grassland, wetland, etc.) within each spatial grid. These vegetation types directly influence species habitat suitability, carbon sequestration capacity, and other ecological processes, providing key ecological attribute inputs for the digital twin model. The soil texture map provides soil ecological parameters by specifying attributes such as soil type, organic matter content, and pH value for each grid cell. These parameters significantly influence plant growth rates, nutrient availability, pollutant adsorption, and water purification efficiency, and serve as fundamental inputs for the digital twin model's ecological simulations.

[0109] The process of initializing the digital twin large model based on the digital twin engine, the large model's static base information, and the prediction model mainly includes the following steps: 1. Loading the large model's static base information into the digital twin engine to provide the large model with a three-dimensional spatial skeleton and basic attributes, ensuring the large model's spatial accuracy and authenticity; 2. Through spatial alignment and data association, the large model's static base information is integrated with the dynamic kernel and rule engine in the digital twin engine to establish a correspondence between space and attributes, enabling the digital twin engine to perform state calculations and response judgments based on the large model's static base information; 3. Integrating the prediction model into the digital twin engine as a functional module for future state prediction and scenario deduction; 4. Based on the characteristics of the actual ecosystem (based on characteristics obtained from long-term historical statistics, such as multi-year average species richness, seasonal water quality fluctuation range, soil background value, etc.), configure the parameters in the digital twin engine, such as coupling weights, response functions, threshold rules, etc., and initialize the large model state to ensure that the large model can accurately reflect the initial situation of the ecosystem.

[0110] Coupling weights quantify the strength of interactions between species, the environment, and service functions, and are key parameters for describing intervariable relationships within the macromodel. Response functions describe the response relationships between variables and are functions that describe the patterns of change between variables within the macromodel. Threshold rules define the critical state of an ecosystem, determining whether warning or response conditions have been met and forming the basis for logical judgment and response triggering within the macromodel. Together, these parameters constitute the dynamic core of the digital twin macromodel, serving as key model parameters for describing the relationships, patterns of change, and critical states between ecosystem variables. They are core components of the macromodel's ability to accurately reflect ecosystem status and behavior, and they underpin its ability to perform real-time computations, status updates, trend analysis, critical judgments, and response triggering.

[0111] During the above implementation process, by pre-training a prediction model with the function of predicting future states and deducing functions, and integrating the prediction model into the digital twin engine, a large digital twin model that supports future state prediction and state deduction can be constructed.

[0112] The following describes the process of displaying the dynamic information of the ecosystem panorama on the visual interface. In response to the real-time data input into the digital twin model, the real-time data and target data are processed, and the dynamic information of the ecosystem panorama is displayed on the visual interface, including: The acquired real-time data is connected to the digital twin engine through a data interface, driving the digital twin engine to update the model status of the digital twin model. The real-time data includes real-time plant and animal information, real-time environmental quality assessment, and real-time ecosystem service function assessment results. Processing real-time data and target data based on the updated digital twin model to output ecosystem operation results and ecosystem prediction results. The ecosystem operation results include at least one of the current state of the ecosystem, the trend of ecosystem state changes, the critical state of the ecosystem, and the decision response. The ecosystem prediction results include at least one of the natural evolution prediction results and the scenario deduction results. Based on the ecosystem operation results and ecosystem prediction results provided by the digital twin model, panoramic dynamic information of the ecosystem is generated and visualized through a VR interface or an AR interface.

[0113] The digital twin engine obtains real-time data (real-time plant and animal information, real-time environmental quality assessment, and real-time ecosystem service function assessment results) through data interfaces (such as the Internet of Things interface). The digital twin engine dynamically updates the status of the digital twin large model based on the real-time data obtained, keeping the large model synchronized with the real ecosystem.

[0114] After updating the digital twin model, the real-time data and target data are processed based on the updated digital twin model to output ecosystem operation results and ecosystem prediction results. The provided ecosystem operation results include at least one of the ecosystem's current state, ecosystem state change trends, ecosystem criticality, and decision responses. The provided ecosystem prediction results include at least one of natural evolution prediction results and scenario-based deduction results. After the digital twin model outputs the ecosystem operation results and ecosystem prediction results, panoramic ecosystem dynamic information is generated based on the ecosystem operation results and ecosystem prediction results, visualized using VR or AR technology, and displayed on a VR or AR interface.

[0115] Among them, when processing real-time data and target data based on the updated digital twin model and outputting ecosystem operation results and ecosystem prediction results, it includes: Based on the updated digital twin model, real-time data and historical ecosystem data are processed to calculate the current state of the ecosystem, assess the trend of changes in the ecosystem state, and identify the critical state of the ecosystem to obtain ecosystem assessment information and provide decision-making responses based on logical judgment. Scenario simulations are conducted based on ecosystem assessment information, set scenario parameters, and target data, and / or natural evolution predictions are conducted based on ecosystem assessment information and target data, with the output of ecosystem prediction results. The target data also includes external driving factors, temporal information, and spatial information.

[0116] During the processing process, the digital twin engine utilizes a dynamic core to perform state calculations based on real-time data, historical ecosystem data, and static information from the large model. This core evaluates the ecosystem's current state, its changing trends, and identifies criticality. Based on the dynamic core's output, the rules engine executes logical judgments and response actions, such as triggering early warnings, to provide decision support for ecological protection and management. The dynamic core outputs the ecosystem's current state, changing trends, and criticality as ecosystem assessment information, which is then fed into the prediction model.

[0117] When the prediction model makes natural evolution predictions, the data provided by the dynamic kernel is one of the core inputs. It also needs to use the ecosystem's historical data, time information, spatial information and external driving factors to predict the future natural evolution path of the ecosystem.

[0118] The dynamic kernel provides data on the current state of the ecosystem (e.g., species richness, environmental quality, and service functions), trends in ecosystem state change (e.g., upward, downward, or fluctuating trends), and identified critical ecosystem states. Historical ecosystem data, including historical species identification, environmental monitoring, and service function assessments, is used for contextual modeling. This data can capture long-term evolutionary patterns and seasonal variations. Temporal information, including the current time point, forecast start time, and forecast duration (e.g., 7 days, 30 days ahead), is used to determine the forecast window and model input sequence length. Spatial information, including grid IDs and regional attributes (e.g., topography, vegetation, and soils), is used for spatially coupled modeling. External drivers include meteorological forecasts (e.g., future precipitation and temperature) and the intensity of human activities (e.g., development and pollution).

[0119] The data provided by the dynamic kernel, ecosystem historical data, time information, spatial information and external driving factors are input into the prediction model as input data. The prediction model makes natural evolution predictions and outputs ecosystem prediction results (natural evolution prediction results).

[0120] When the prediction model performs scenario simulations, data provided by the dynamic kernel is one of the core inputs. It also relies on scenario parameters, historical ecosystem data, temporal information, spatial information, and external driving factors. Scenario parameters are user-defined or preset, such as water replenishment, temperature changes, and vegetation restoration area, to define the simulation scenarios. This data is then fed into the prediction model, which then performs scenario simulations and outputs ecosystem prediction results (scenario simulation results).

[0121] Figure 2 A simplified flowchart of the digital twin large model processing real-time data and target data provided by an embodiment of the present application is shown.

[0122] The dynamic kernel receives real-time data (real-time plant and animal information, real-time environmental quality assessment, and real-time ecosystem service function assessment results) accessed through the Internet of Things. It combines historical ecosystem data, large-scale model static base information, coupling weights, response functions, and threshold rules to calculate the current state of the ecosystem, analyze the trend of changes in the ecosystem state, and identify whether the ecosystem state has reached critical conditions. The output includes the current state value of the ecosystem, the trend of state changes, and the output content of critical identification.

[0123] The rules engine executes business logic judgments and response actions based on the output of the dynamic core. The rules engine receives the ecosystem's current state, state change trends, and critical identification output from the dynamic core. Based on the set response strategy, it determines whether the warning conditions are met, executes the response, and outputs decision recommendations.

[0124] The prediction model receives the output content provided by the dynamic kernel, combines the historical data of the ecosystem, time information, spatial information and external driving factors to predict natural evolution, and outputs the natural evolution prediction results; and / or, the prediction model receives the output content provided by the dynamic kernel, combines the scenario parameters, historical data of the ecosystem, time information, spatial information and external driving factors to perform scenario deduction, and outputs the scenario deduction results.

[0125] The decision responses provided by the rule engine, the current state of the ecosystem, state change trends, and criticality identification output by the dynamic core, and the natural evolution predictions and / or scenario-based deductions provided by the predictive model are rendered in a visual interface. Real-time data from the digital twin master model, connected via the IoT, drives the digital twin engine to refresh the master model in real time, ensuring that the master model remains synchronized with the real ecosystem. The dynamic core, rule engine, and predictive model work together to output a comprehensive and dynamic overview of the ecosystem based on real-time data, supporting real-time monitoring, intelligent response, and future deduction.

[0126] The ecosystem operation results provided by the digital twin large model cover multiple dimensions including the current state of the ecosystem, state change trends, critical state identification, and response actions. The ecosystem prediction results provided by the digital twin large model include natural evolution prediction results and / or scenario deduction results, which can comprehensively reflect the operation status, response mechanism and prediction of the ecosystem.

[0127] In a specific example of the present application, the panoramic dynamic information of the ecosystem displayed through the VR interface or AR interface includes: the current state of the ecosystem, the trend of changes in the state of the ecosystem, the critical state of the ecosystem, the decision-making response results, the natural evolution prediction results and the scenario deduction results.

[0128] The current state of an ecosystem includes species status, environmental quality, and ecosystem service function status. Species status includes, for example, real-time identification of species names, abundance, diversity indices, and spatial distribution heat maps within each grid cell. Environmental quality status includes, for example, real-time raster values ​​for water quality, soil quality, and weather conditions. Ecosystem service function status includes, for example, grid-based carbon sink flux, water conservation capacity, biodiversity maintenance index, and pollutant purification efficiency. The real-time environmental quality assessment is a comprehensive evaluation based on the raw data. The digital twin model then rasterizes these evaluation results and outputs continuous physical quantities (such as pH, dissolved oxygen, and organic matter) for each grid cell, representing the environmental status. The real-time ecosystem service function assessment results in a comprehensive score or ranking of the raw data. The digital twin model rasterizes these evaluation results and converts them into continuous physical quantities or fluxes for each grid cell, representing the service function.

[0129] The current state of an ecosystem can be visualized in a visualization interface, displaying species distribution maps, environmental quality maps, and service function status maps. Species distribution maps can be presented in various formats, including heat maps (using color depth to represent species density or richness), dot maps (using discrete dots to indicate the location of each individual observation or sampling point), and three-dimensional models (overlaying species data on a three-dimensional terrain or vegetation model to provide an immersive spatial perspective). These three types of maps can be presented individually or in combination to form a comprehensive visualization of species spatial distribution. Environmental quality maps, for example, plot parameters such as water quality (pH, dissolved oxygen), soil (organic matter, heavy metals), and meteorological (temperature, humidity) into spatial distribution maps by grid or region. For example, a pH distribution map or temperature heat map can quickly identify areas with acidic water quality or high temperatures. Service function status maps, for example, plot the spatial distribution of ecosystem output capacity (carbon sequestration, water conservation, biodiversity index, etc.), visually displaying areas with high carbon sequestration values, hot spots for water conservation, and cold and hot spots for biodiversity.

[0130] Trends include the changing trends (e.g., increases, decreases, fluctuations), rates of change, and magnitudes (e.g., daily changes in the water quality index) of various elements (species, environment, and services). Trends can be visualized using trend graphs, dynamic evolution graphs, and rate of change graphs. For example, trend graphs show the temporal changes in species richness, water quality index, and service indicators; dynamic evolution graphs use animations or timeline playback to visually illustrate how ecosystem status changes over time; and rate of change graphs use spatial distribution to display the rate of increase or decrease of various ecological elements per unit time.

[0131] Critical state identification includes identifying whether the ecological threshold has been reached. The warning map can show which areas have triggered the ecological threshold warning, the warning list can list all current warning information (such as low pH, decreased species richness), and the critical area can be highlighted through the critical mark.

[0132] For response actions and decision-making recommendations, the large model can automatically trigger response actions, recommend intervention measures (such as planting water-purifying plants, limiting pollution discharge, and ecological restoration plans), and can use scenario simulation to simulate the ecological restoration effects under different intervention measures.

[0133] When displaying natural evolution predictions and scenario simulations in a visual interface, various methods can be used to intuitively and clearly present future predictions and scenario simulations of ecosystem natural evolution. Dynamic evolution diagrams can be used to display changes in ecosystem status over time. For example, dynamic evolution diagrams can display the evolutionary process through animation or timeline playback. Displays include changes in species distribution (using animations to show changes in species distribution at different time points), changes in environmental quality (showing trends in water quality, soil, and meteorological parameters over time), and changes in service functions (showing changes in carbon sequestration, water conservation, biodiversity, and other service functions over time). Interactive timelines can be used to display ecosystem status at different points in time. For example, users can drag the timeline to view the ecosystem status at different time points. Displays include real-time data (real-time monitoring data at the current time point), historical data (recorded data at past time points), and predicted data (data at future time points). Scenario comparison diagrams can be used to display changes in ecosystem status under different scenarios. For example, displays can show the future state of the ecosystem without intervention and the future state of the ecosystem under intervention measures (such as water replenishment or vegetation restoration). The differences between different scenarios can be presented in charts or maps.

[0134] In an optional embodiment of the present application, the method further comprises: In response to receiving an interactive operation on the VR interface or the AR interface, updating a model state of the digital twin macro model, and obtaining updated ecosystem panoramic dynamic information output by the digital twin macro model; Based on the updated ecosystem panoramic dynamic information, the visual content on the VR interface or the AR interface is updated in real time.

[0135] The digital twin model outputs a panoramic ecosystem dynamic information (ecosystem operation results and ecosystem prediction results) that is visualized using VR or AR technology, allowing users to intuitively understand the ecosystem's current state, state change trends, critical situations, response decisions, and ecosystem predictions. The visualization interface supports interactive operations. User interactions on the visualization interface can update the model state of the digital twin model, which then outputs the latest panoramic ecosystem dynamic information, which in turn updates the visualization interface.

[0136] The visual interface can support user interaction in the following ways, for example: Parameter adjustment: allows users to adjust parameters such as environmental factors and species numbers, and the large model will give a response. Scenario simulation: provides different scenario options, and users can view the changes in the simulated ecosystem under different scenario options. Layer control: users can choose to display or hide different data layers, such as species distribution and water quality. Warning information viewing: displays warning details, and users can click to view specific risk areas and recommended measures. Timeline operation: users can drag the timeline to view historical changes and trends in the state of the ecosystem. The above-mentioned interactive methods supported allow users to actively participate in model operations, explore different scenarios, and view the state of the ecosystem at different time points (such as historical status, future natural evolution status), so as to better understand the state of the ecosystem and assist in decision-making.

[0137] The visualization interface can be a VR interface or an AR interface. The VR interface supports immersive operation. For example, users can wear a VR headset to enter a three-dimensional virtual ecosystem, interact with the virtual environment through handles, gestures, eye tracking, etc., and view the ecological status of different areas. For example, in the parameter adjustment scene, environmental parameters (such as precipitation and temperature) can be adjusted on the virtual interface; in the information query and feedback scene, users can click on a species or area to view detailed information (such as species name, abundance, and health status); in the deduction scene, users can set different scenario parameters to view the deduction results under each scenario parameter, and support multi-scenario comparison. Users can view the ecosystem status under different scenarios at the same time and intuitively compare the differences; in the natural evolution prediction scene, users can drag the timeline to view the natural evolution prediction results at different time points.

[0138] The AR interface supports augmented reality overlays, overlaying virtual ecological information onto real-world scenes through AR glasses or cameras. That is, when users wear AR glasses or use their phone's camera, they can see real-time monitoring data superimposed on the real scene, such as water quality indicators displayed above a real river or species distribution maps superimposed on a forest. Users can view real-time monitoring data (such as water and air quality) in the real environment and click to view details, historical trends, and early warning information. In scenario simulations, the effects of different intervention measures (such as vegetation restoration and pollution control) can be simulated, comparing the current ecological state with the simulated state, and comparing the differences between simulations under different scenarios. In the natural evolution prediction scenario, users can view natural evolution prediction results at different time points.

[0139] While utilizing a visualization interface to display panoramic dynamic information of an ecosystem, the embodiments of the present application support user interaction on the visualization interface. In response to the user's interaction, the panoramic dynamic information of the ecosystem is updated, enabling intuitive and real-time interaction between the user and the large digital twin model. Immersive interaction is also performed based on a VR interface or an AR interface, thereby enhancing the user's interactive experience.

[0140] Figure 3 The overall implementation flow chart of the digital evolution method of the ecosystem in the embodiment of the present application is shown.

[0141] Step 301: Generate biological feature vectors and environmental feature vectors based on biological data and environmental data collected in the ecological protection area, analyze the biological feature vectors and environmental feature vectors, and obtain animal and plant information and environmental quality assessment.

[0142] Step 302: Perform vector concatenation based on the biological feature vector, the environmental feature vector and the corresponding weights to determine a comprehensive feature vector.

[0143] Step 303: Based on the optimized evaluation model, a comprehensive reasoning analysis is performed on the biological feature vector, environmental feature vector, comprehensive feature vector, plant and animal information, and environmental quality evaluation to generate an ecosystem service function evaluation result.

[0144] Step 304: preprocess the multi-source data, align the time and space of the data, determine the standard data, and extract multimodal features from the standard data to construct a comprehensive feature matrix. The multi-source data includes animal and plant information, environmental quality assessment status, and ecosystem service function assessment results. The comprehensive feature matrix is ​​composed of fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the animal and plant information, environmental quality assessment status, and ecosystem service function assessment results corresponding to the current spatiotemporal unit.

[0145] Step 305: Perform deep learning on the comprehensive feature matrix to generate a dynamic kernel, and encapsulate the dynamic kernel and the rule engine to build a digital twin engine.

[0146] Step 306: Load the static base information of the large model into the digital twin engine, integrate the prediction model that supports natural evolution prediction and scenario deduction into the digital twin engine, configure various model parameters in the digital twin engine, and initialize the digital twin large model.

[0147] Step 307: In response to the real-time data input into the digital twin macro model, the model state of the digital twin macro model is updated, the real-time data and target data are processed based on the updated digital twin macro model, and the panoramic dynamic information of the ecosystem is output and displayed through a visualization interface.

[0148] Step 308: In response to receiving the interactive operation on the visualization interface, obtain the latest dynamic information of the ecosystem panorama and update the visualization content on the visualization interface.

[0149] The above-mentioned implementation process of this application realizes the real-time twinning of the ecosystem status based on digital twin technology, realizes the prediction results and scenario deduction of the natural evolution of the ecosystem based on the integrated prediction function, uses the visual interface to display content and support real-time interaction, providing users with a good experience.

[0150] To further illustrate the solution provided by the embodiments of this application, a specific example is provided below. This example focuses on the monitoring and management of tropical rainforest ecosystems. It aims to achieve real-time monitoring, future state prediction, and scenario simulation of tropical rainforest ecosystems through multimodal data fusion, dynamic kernel construction, natural evolution prediction, scenario simulation, and AR interaction technologies, providing scientific decision-making support for protected area managers.

[0151] 1. Multimodal data acquisition and processing (collecting biological data and environmental data and processing them) When collecting and processing biological data: high-resolution cameras are deployed to capture canopy animal activity, and feature vectors (such as species ID, number, and activity frequency) are extracted using a CNN model. Directional microphone arrays are used to capture bird calls, and feature vectors (species ID, number of calls, and voiceprint similarity) are generated using a voiceprint recognition model. GPS tags are attached to iconic species (such as monkeys) to generate spatial trajectory feature vectors (movement speed, habitat range, and migration path). Image, audio, and location data are aligned in time and space to construct a temporally and spatially consistent biological feature vector, and weights can be dynamically assigned, such as automatically adjusting feature weights based on data confidence.

[0152] When collecting and processing environmental data: Distributed deployment of soil moisture IoT sensors generates gridded feature vectors (humidity value, spatial coordinates, and timestamps); canopy light meter data is used to construct feature vectors (light intensity, diurnal variation curve, and canopy transmittance); and integrated micro-meteorological stations output vectors (temperature, rainfall, and wind speed). Environmental feature vectors are generated based on a unified spatiotemporal benchmark.

[0153] 2. Multi-source data acquisition After obtaining the biological and environmental feature vectors, the biological feature vectors are analyzed to obtain information on the flora and fauna within the ecological reserve, and the environmental feature vectors are analyzed to obtain an environmental quality assessment within the ecological reserve. The biological and environmental feature vectors are then weighted and concatenated to generate a comprehensive feature vector. The comprehensive analysis of the biological, environmental, and comprehensive feature vectors, along with flora and fauna information and environmental quality assessments, yields the ecosystem service function assessment results. This provides information on flora and fauna, environmental quality assessments, and ecosystem service function assessments.

[0154] 3. Dynamic kernel construction After obtaining information on flora and fauna, environmental quality assessments, and ecosystem service function assessments, features are extracted from these data and fused to generate a fused feature vector corresponding to the same temporal and spatial dimensions. A comprehensive feature matrix is ​​then constructed based on multiple fused feature vectors. Deep learning of this comprehensive feature matrix is ​​then used to construct a dynamic kernel. For example, a Transformer + LSTM hybrid model is used to process the comprehensive feature matrix, generating a dynamic kernel consisting of a weight matrix, a response function, and threshold rules. The weight matrix quantifies the coupled relationship between light intensity and parrot reproduction rate, and its ecological rationality is verified through interpretability analysis. The response function fits nonlinear relationships, such as determining the relationship between reproduction rate and light, humidity, and temperature. Threshold rules are used to detect critical states, such as triggering an "extreme drought" alert when humidity is <25% and high temperatures are >7 consecutive days. The dynamic kernel supports fine-tuning through online learning.

[0155] 4. Construction and application of large digital twin models A digital twin engine is built based on the dynamic kernel and rule engine. A large digital twin model is constructed based on the digital twin engine, the large model's static base information, and the predictive model. The large digital twin model is used to process real-time data and historical ecosystem data, outputting panoramic dynamic information about the ecosystem for visualization. This panoramic dynamic information includes ecosystem operational results and ecosystem prediction results. Ecosystem operational results include at least one of the current ecosystem state, ecosystem state change trends, ecosystem criticality, and decision-making response results. Ecosystem prediction results include at least one of natural evolution prediction results and scenario-based simulation results.

[0156] 5. AR interaction implementation Based on the user's operation on the AR interface, the display content of the visual interface is updated.

[0157] The following example uses the prediction of the natural evolution of tropical rainforest ecosystems and climate scenario simulation to explain this. Compared to existing technologies that only display the current state but cannot predict long-term changes, this example overcomes these limitations by using natural evolution prediction and climate scenario simulation to provide actionable future scenario simulations and scientific decision-making support for protected area managers.

[0158] 1. Implementation of Natural Evolution Prediction Technology (1) Data preprocessing and feature engineering The input data includes historical 10-year time series data, biological data includes parrot population size (monthly census), tree species diversity index (annual survey); environmental data includes temperature / precipitation (daily average of weather stations), soil carbon content (quarterly sampling); ecosystem service function data includes carbon sequestration.

[0159] The data were preprocessed by aligning the data into 1 km × 1 km grids for spatial normalization, unifying the temporal resolution to monthly, constructing a 15-dimensional feature tensor (format: [120 months × 100 grids × 15 dimensions]), and embedding the coupling features output by the dynamic kernel, such as the coupling weights of temperature and parrot reproduction rate, as prior knowledge.

[0160] (2) Prediction model architecture Dual-module collaborative design (LSTM temporal layer and Transformer spatial layer) LSTM temporal layer: Input is 15-dimensional multimodal features (biological, environmental, and service functions), a two-layer structure captures seasonal periodicity and interannual trends, and outputs 64-dimensional temporal features (preserving historical dependencies). Transformer spatial layer: A four-head attention mechanism identifies cross-grid ecological connections (such as canopy-soil interactions) and outputs fused spatiotemporal features (including global spatial relationships).

[0161] Ecological constraint mechanism The weight matrix (64×64) has an initial value that inherits the weights provided by the dynamic kernel to force features to conform to ecological rules (e.g., rising temperature leads to a decrease in carbon sequestration); it transforms the feature space to suppress feature combinations that violate ecological laws (e.g., herbivores exceeding the carrying capacity of vegetation), and outputs a feature tensor of ecological constraints.

[0162] (3) Prediction process Temporal processing: LSTM encodes historical influences (such as the lagged effect of rainfall on carbon sequestration), spatial association: attention mechanism calculates interactions between grids (such as chain reactions in drought areas), ecological correction: weight matrix projects features into the ecological rule space.

[0163] (4) Result verification Uncertainty quantification: Generate 95% confidence intervals; Accuracy comparison: The 5-year prediction error of the rainforest measurement is 3.2%, while the error of the traditional model is >15%.

[0164] The above-mentioned natural evolution prediction implementation scheme adopts spatiotemporal fusion to realize LSTM to capture temporal dynamics and Transformer to model spatial associations. Based on the weight matrix, it forces the prediction to conform to ecological laws, and the error rate is greatly reduced compared with traditional models. It also realizes effective prediction of the natural evolution of ecosystems in future periods through gridded data, hybrid model architecture and ecological rule embedding.

[0165] 2. Climate Scenario Simulation (1) Scenario parameter injection mechanism Parameter types include boundary conditions (temperature gradient fields) and scenario parameters (variables used to define specific scenarios). Boundary conditions are the fundamental input conditions for running the large model, providing the context for scenario simulations and helping the large model understand how the climate would change without human intervention. Scenario parameters include intervention variables (such as artificial irrigation areas) and other parameters, which are used to simulate the impact of human intervention measures on the climate and ecology. Data fusion: Scenario parameters (here, intervention variables) are injected into the base data through a function, modifying the base data temperature and adding intervention tags.

[0166] (2) Deduction workflow Forward propagation: inject scenario parameters into the digital twin model, triggering dynamic kernel recalculation, such as weight matrix adjustment and response function update; critical state detection: monitor output variables in real time, and when the carbon sink amount is lower than the critical value, activate the rule engine to generate suggestions; multi-threaded deduction: parallel calculation of different scenarios and comparison of key indicator differences.

[0167] (3) Visualization of deduction results The results are presented through a dynamic heat map. Red areas indicate where carbon sequestration has fallen below critical values, and blue arrows indicate recommended irrigation priority areas. Carbon sequestration can reflect the ecosystem's response to climate change and is therefore used as a result of climate scenario deduction.

[0168] For example, in the Basin Rainforest Project, through deduction, it was predicted that the carbon sink would drop abnormally in a certain year. Based on the deduction results, it was recommended to take artificial irrigation measures to restore the carbon sink. Through the implementation of these intervention measures, the carbon sink recovered to 92% of the baseline level.

[0169] The following example illustrates visual interaction. A reserve manager uses AR glasses to make decisions. Users gesture to select an area of ​​rainforest and enter the command "Cut down 10% of diseased trees." The large digital twin model, based on a dynamic kernel and predictive model, generates real-time deductions and outputs: a 20% decrease in the risk of disease transmission in the short term (response function calculation) and an 8% loss in bird diversity in the long term (weight matrix chain reaction). The AR interface overlays the area where diseased trees will be removed (red translucent) and changes in bird habitats (e.g., dynamic shrinking animations). Compared to existing technologies that lack real-time feedback on intervention effectiveness, this embodiment achieves a real-time closed loop of interaction, deduction, and visualization.

[0170] Figure 4 An interactive architecture diagram provided by an embodiment of the present application is shown.

[0171] AR glasses support gesture recognition and spatial anchoring positioning; the interactive middleware is used to convert user gestures into operation instructions; the digital twin model runs on the edge computing node and supports second-level response; the AR rendering engine is responsible for converting the output content of the digital twin model into a visual AR scene in real time, allowing users to understand it intuitively; the dynamic kernel database stores and manages various parameters and status data required for the operation of the large model, such as ecological parameters, environmental variables, weight information, etc., providing necessary data support for the digital twin model.

[0172] AR glasses communicate with the interactive middleware through low-latency, two-way communication. For example, a ranger using AR glasses to manage a rainforest can use gestures to select an area corresponding to approximately 2.3 hectares of endangered tree habitat. The ranger then uses gestures and voice to issue commands through the AR glasses, such as felling 10% of diseased trees in the selected area. The interactive middleware recognizes the gestures as polygons in GeoJSON format (a format that accurately describes the area selected by the ranger in the AR glasses) and converts the voice commands into text.

[0173] The interaction process between the interactive middleware and the large digital twin model is as follows: the interactive middleware converts gestures into polygons in GeoJSON format and voice commands into text. It then parses the text commands, identifies key information such as "fell" and "10%," and converts this information into standardized operation codes. For example, "fell 10% of diseased trees" might be converted into an operation type code (such as TREE_REMOVAL) and an intervention intensity parameter (such as intensity=0.1). To ensure that the selected felled area is completely within the ecological protection zone, the interactive middleware uses topology tools to perform spatial relationship calculations and verify whether the user-selected polygon area is completely within the ecological protection zone, thereby avoiding unnecessary ecological damage. Once the command is standardized and spatial verification is passed, the interactive middleware sends this data to the large digital twin model.

[0174] The interaction between the large digital twin model and the dynamic core database proceeds as follows: the large digital twin model requests detailed data about the current ecosystem, such as the distribution of diseased trees, the status of healthy trees, and soil conditions. The dynamic core database then compiles the data based on the request and returns a response containing all relevant ecological parameters. Based on this detailed data, the large digital twin model can generate a real-time twin output of the ecosystem's current state. It can also simulate the immediate impact of felling 10% of diseased trees (such as increased sunlight and changes in the growth rate of healthy trees) and analyze how these changes affect ecosystem trends. It can also identify critical ecosystem states and make decisions and responses once potential critical states are identified. It can also perform natural evolution predictions and scenario simulations. For example, it can predict how the ecosystem will evolve naturally without additional intervention, including the natural death of diseased trees and the growth of new trees. Furthermore, it can simulate the ecosystem's potential responses under defined intervention scenarios (such as felling different proportions of diseased trees) and evaluate the effectiveness of different management strategies.

[0175] The interaction between the digital twin model and the AR rendering engine is as follows: the model sends output (such as ecosystem operational results and ecosystem predictions) to the AR rendering engine, including specific values ​​and data to be visualized. The AR rendering engine then converts this data into visualizations such as heat maps and animations for display on AR glasses. If ecological risks are identified, the AR rendering engine also generates warnings.

[0176] The AR rendering engine interacts with the AR glasses via a wireless connection, rapidly transmitting visual data to them. Rangers can use gestures to interact with the visualizations on the AR glasses, viewing detailed information. The AR glasses then instantly display the impact of ranger commands on the ecosystem, aiding decision-making.

[0177] Through the above process, forest rangers can clearly understand the potential impact of felling diseased trees and make management decisions accordingly. The real-time feedback mechanism improves the efficiency and accuracy of decision-making and enhances the ability to manage the ecosystem.

[0178] The present application also provides a digital evolution system for an ecosystem, such as Figure 5 Shown, including: An acquisition module 501 is configured to acquire information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within the ecological protection zone based on biological and environmental data collected within the ecological protection zone. A generation learning module 502 is used to generate a comprehensive feature matrix based on the features extracted from the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment results, and to perform deep learning on the comprehensive feature matrix to construct a digital twin engine, wherein the comprehensive feature matrix is ​​composed of fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the plant and animal information, environmental quality assessment, and ecosystem service function assessment results corresponding to the current spatiotemporal unit; A construction module 503 is used to construct a digital twin large model based on the digital twin engine, the large model static base information and the prediction model, wherein the prediction model supports natural evolution prediction and scenario deduction under a set scenario; The processing and display module 504 is used to process the real-time data input into the digital twin model and the target data, and display the panoramic dynamic information of the ecosystem on a visualization interface. The target data includes historical data of the ecosystem. The visualization interface is a virtual reality (VR) interface or an augmented reality (AR) interface. The panoramic dynamic information of the ecosystem includes at least one of natural evolution prediction results and scenario deduction results.

[0179] Optionally, the acquisition module includes: a generating submodule, configured to generate the biometric feature vector based on the biometric data and the environmental feature vector based on the environmental data, wherein the biometric data includes at least image data, audio data, and positioning data of plants and animals, and the environmental data includes at least meteorological data and physical environment data; An acquisition submodule is used to perform species identification on the biological feature vector based on a biological macro model, and to perform environmental analysis on the environmental feature vector based on an environmental macro model, so as to obtain information on flora and fauna within the ecological protection zone and an assessment of environmental quality; The processing and acquisition submodule is used to process the animal and plant information, the environmental quality assessment, the biological feature vector, the environmental feature vector and the comprehensive feature vector to obtain the ecosystem service function assessment result of the ecological protection zone. The comprehensive feature vector is generated based on the fusion of the biological feature vector and the environmental feature vector.

[0180] Optionally, the processing and obtaining submodule includes: a splicing determination unit, configured to perform vector splicing based on the biological feature vector and the environmental feature vector in combination with corresponding weights to determine the comprehensive feature vector; An analysis and generation unit is used to perform comprehensive reasoning and analysis on the biological feature vector, the environmental feature vector, the comprehensive feature vector, the animal and plant information, and the environmental quality assessment based on an optimized evaluation model, and generate an ecosystem service function assessment result including an ecosystem species diversity assessment result and an ecosystem environment assessment result.

[0181] Optionally, the generating learning module includes: A first processing submodule is configured to extract features from the multi-source data after sequentially preprocessing and aligning the data, fuse the features to generate a fused feature vector corresponding to the same time and space dimension, and construct the comprehensive feature matrix based on the multiple fused feature vectors, wherein the multi-source data includes the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment result; A learning generation submodule is configured to perform deep learning on the comprehensive feature matrix to generate a dynamic kernel comprising a weight matrix, a response function, and a threshold rule. The weight matrix is ​​used to quantify the interaction between species and the environment and service functions; the response function is used to describe the response relationship between ecosystem variables; the threshold rule is used to define the critical state of the ecosystem; and the dynamic kernel is used to calculate the ecosystem state, assess the trend of changes in the ecosystem state, and identify the critical state. A construction submodule is used to encapsulate the dynamic kernel and the rule engine, and to build the digital twin engine. The rule engine makes a decision response based on the output result of the dynamic kernel.

[0182] Optionally, the system further comprises: a determination module for determining a data sample set based on historical ecosystem samples labeled with scenario categories and labeled data indicating true observations at a target moment, the data sample set comprising a training set, a validation set, and a test set, the labeled data being a model training target, and the target moment being a future moment relative to a historical moment; a first processing module, configured to perform model training on a machine learning model suitable for ecosystem time series prediction based on the training set, update model parameters through an optimization algorithm, and evaluate model performance based on the validation set; The second processing module is used to evaluate the prediction accuracy and scenario response consistency of the model based on the test set, and to tune the model structure and hyperparameters based on the evaluation indicators to determine the prediction model.

[0183] Optionally, the building blocks include: A loading submodule is used to load the static base information of the large model used to construct the three-dimensional spatial skeleton and basic attributes into the digital twin engine to complete spatial registration and logical binding; An integration submodule, configured to integrate the trained prediction model into the digital twin engine; The configuration submodule is used to configure various model parameters in the digital twin engine to initialize the digital twin large model.

[0184] Optionally, the processing and display module includes: A second processing submodule is configured to connect the acquired real-time data to the digital twin engine based on a data interface, and drive the digital twin engine to update the model state of the digital twin large model, wherein the real-time data includes real-time plant and animal information, real-time environmental quality assessment status, and real-time ecosystem service function assessment results; a third processing submodule, configured to process the real-time data and the target data based on the updated digital twin macromodel, and output an ecosystem operation result and an ecosystem prediction result, wherein the ecosystem operation result includes at least one of the current state of the ecosystem, the trend of change in the state of the ecosystem, the critical state of the ecosystem, and the decision response; and the ecosystem prediction result includes at least one of the natural evolution prediction result and the scenario deduction result; The fourth processing submodule is used to generate the panoramic dynamic information of the ecosystem based on the ecosystem operation results and ecosystem prediction results provided by the digital twin model, and to visualize it through the VR interface or the AR interface.

[0185] Optionally, the third processing submodule includes: A first processing unit is configured to process the real-time data and the historical ecosystem data based on the updated digital twin model, calculate the current state of the ecosystem, evaluate the trend of changes in the ecosystem state, identify the critical state of the ecosystem, obtain ecosystem assessment information, and provide a decision response based on logical judgment; A second processing unit is configured to perform scenario deduction based on the ecosystem assessment information, set scenario parameters, and the target data, and / or to perform natural evolution prediction based on the ecosystem assessment information and the target data, and output the ecosystem prediction result, wherein the target data also includes external driving factors, time information, and spatial information.

[0186] Optionally, the system further comprises: An update acquisition module, configured to update the model state of the digital twin macro model in response to receiving an interactive operation on the VR interface or the AR interface, and acquire updated ecosystem panoramic dynamic information output by the digital twin macro model; An updating module is used to update the visual content on the VR interface or the AR interface in real time based on the updated ecosystem panoramic dynamic information.

[0187] As for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0188] It should be noted that the above detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs.

[0189] In the above detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, similar symbols typically identify similar components, unless the context dictates otherwise. The illustrated embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used, and other changes may be made, without departing from the spirit or scope of the subject matter presented herein.

[0190] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A digital evolution method for an ecosystem, characterized in that: include: Based on the biological and environmental data collected in the ecological reserve, obtain information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within the ecological reserve; Generate a comprehensive feature matrix based on the features extracted from the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment results, and perform deep learning on the comprehensive feature matrix to construct a digital twin engine, wherein the comprehensive feature matrix is ​​composed of fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the plant and animal information, environmental quality assessment, and ecosystem service function assessment results corresponding to the current spatiotemporal unit; Constructing a digital twin large model based on the digital twin engine, large model static base information, and a prediction model, wherein the prediction model supports natural evolution prediction and scenario deduction under set scenarios; In response to real-time data input into the digital twin model, the real-time data and target data are processed, and panoramic dynamic information of the ecosystem is displayed on a visualization interface. The target data includes historical data of the ecosystem. The visualization interface is a virtual reality (VR) interface or an augmented reality (AR) interface. The panoramic dynamic information of the ecosystem includes at least one of natural evolution prediction results and scenario deduction results.

2. The method according to claim 1, characterized in that The biological and environmental data collected in the ecological protection zone are used to obtain information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within the ecological protection zone, including: generating the biometric feature vector based on the biometric data and generating the environmental feature vector based on the environmental data, wherein the biometric data includes at least image data, audio data, and positioning data of plants and animals, and the environmental data includes at least meteorological data and physical environment data; Perform species identification on the biological feature vector based on a biological macro model, and perform environmental analysis on the environmental feature vector based on an environmental macro model to obtain information on flora and fauna within the ecological protection zone and an environmental quality assessment; The animal and plant information, the environmental quality assessment, the biological feature vector, the environmental feature vector and the comprehensive feature vector are processed to obtain the ecosystem service function assessment result of the ecological protection zone, wherein the comprehensive feature vector is determined based on the fusion of the biological feature vector and the environmental feature vector and the dynamically adjusted biological weight and environmental weight.

3. The method according to claim 2, characterized in that The processing of the animal and plant information, the environmental quality assessment, the biological feature vector, the environmental feature vector, and the comprehensive feature vector to obtain the ecosystem service function assessment result of the ecological protection zone includes: Perform vector concatenation based on the biological feature vector and the environmental feature vector in combination with corresponding weights to determine the comprehensive feature vector; Based on the optimized evaluation model, a comprehensive reasoning analysis is performed on the biological feature vector, the environmental feature vector, the comprehensive feature vector, the animal and plant information and the environmental quality evaluation situation to generate an ecosystem service function evaluation result including an ecosystem species diversity evaluation result and an ecosystem environment evaluation result.

4. The method according to claim 1, wherein The generating of a comprehensive feature matrix based on the features extracted from the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment results, and performing deep learning on the comprehensive feature matrix to construct a digital twin engine includes: After preprocessing and aligning the multi-source data in sequence, extracting features from the multi-source data and fusing them to generate a fused feature vector corresponding to the same time and space dimension, and constructing the comprehensive feature matrix based on multiple fused feature vectors, wherein the multi-source data includes the animal and plant information, the environmental quality assessment, and the ecosystem service function assessment result; Deep learning is performed on the comprehensive feature matrix to generate a dynamic kernel including a weight matrix, a response function, and a threshold rule. The weight matrix is ​​used to quantify the interaction between species and the environment and service functions; the response function is used to describe the response relationship between ecosystem variables; the threshold rule is used to define the critical state of the ecosystem; and the dynamic kernel is used to calculate the ecosystem state, assess the trend of ecosystem state changes, and identify the critical state. The dynamic kernel and rule engine are encapsulated to build the digital twin engine, and the rule engine makes a decision response based on the output result of the dynamic kernel.

5. The method according to claim 1, characterized in that The method further comprises: Determining a data sample set based on historical ecosystem samples labeled with scenario categories and labeled data indicating real observations at a target moment, wherein the data sample set includes a training set, a validation set, and a test set, the labeled data being a model training target, and the target moment being a future moment relative to a historical moment; Performing model training on a machine learning model suitable for ecosystem time series prediction based on the training set, updating model parameters through an optimization algorithm, and evaluating model performance based on the validation set; The prediction accuracy and scenario response consistency of the model are evaluated based on the test set, and the model structure and hyperparameters are tuned based on the evaluation indicators to determine the prediction model.

6. The method according to claim 1 or 5, characterized in that The digital twin big model is constructed based on the digital twin engine, the big model static base information and the prediction model, including: Loading the static base information of the large model used to construct the three-dimensional spatial skeleton and basic attributes into the digital twin engine to complete spatial registration and logical binding; Integrating the trained prediction model into the digital twin engine; Configure various model parameters in the digital twin engine to initialize the digital twin large model.

7. The method according to claim 4, characterized in that The method of processing the real-time data and target data in response to the real-time data input into the digital twin macro model and displaying panoramic dynamic information of the ecosystem on a visual interface includes: Connecting the acquired real-time data to the digital twin engine based on a data interface to drive the digital twin engine to update the model state of the digital twin large model, wherein the real-time data includes real-time plant and animal information, real-time environmental quality assessment, and real-time ecosystem service function assessment results; Processing the real-time data and the target data based on the updated digital twin macro model, and outputting an ecosystem operation result and an ecosystem prediction result, wherein the ecosystem operation result includes at least one of the current state of the ecosystem, the trend of change in the state of the ecosystem, the critical state of the ecosystem, and the decision response; and the ecosystem prediction result includes at least one of the natural evolution prediction result and the scenario deduction result; Based on the ecosystem operation results and ecosystem prediction results provided by the digital twin model, the ecosystem panoramic dynamic information is generated and visualized through the VR interface or the AR interface.

8. The method according to claim 7, characterized in that The updated digital twin model is used to process the real-time data and the target data, and output the ecosystem operation results and the ecosystem prediction results, including: Processing the real-time data and the ecosystem historical data based on the updated digital twin model to calculate the current state of the ecosystem, assess the trend of changes in the ecosystem state, and identify the critical state of the ecosystem to obtain ecosystem assessment information, and provide decision-making responses based on logical judgment; Scenario deduction is performed based on the ecosystem assessment information, the set scenario parameters and the target data, and / or natural evolution prediction is performed based on the ecosystem assessment information and the target data, and the ecosystem prediction result is output, where the target data also includes external driving factors, time information and spatial information.

9. The method according to claim 7 or 8, characterized in that The method further comprises: In response to receiving an interactive operation on the VR interface or the AR interface, updating a model state of the digital twin macro model, and obtaining updated ecosystem panoramic dynamic information output by the digital twin macro model; Based on the updated ecosystem panoramic dynamic information, the visual content on the VR interface or the AR interface is updated in real time.

10. A digital evolution system for an ecosystem, characterized in that: include: An acquisition module is used to obtain information on flora and fauna, environmental quality assessments, and ecosystem service function assessments within the ecological reserve based on biological and environmental data collected within the ecological reserve; Generate a learning module for generating a comprehensive feature matrix based on the features extracted from the plant and animal information, the environmental quality assessment, and the ecosystem service function assessment results, and perform deep learning on the comprehensive feature matrix to construct a digital twin engine, wherein the comprehensive feature matrix is ​​composed of fused feature vectors of multiple spatiotemporal units arranged in a spatiotemporal sequence, and the fused feature vector of a single spatiotemporal unit is formed by multimodal feature splicing of the plant and animal information, environmental quality assessment, and ecosystem service function assessment results corresponding to the current spatiotemporal unit; A construction module for constructing a digital twin large model based on the digital twin engine, large model static base information, and a prediction model, wherein the prediction model supports natural evolution prediction and scenario deduction under set scenarios; A processing and display module is used to process the real-time data input into the digital twin model in response to the real-time data and target data, and display the panoramic dynamic information of the ecosystem on a visualization interface, wherein the target data includes historical data of the ecosystem, and the visualization interface is a virtual reality (VR) interface or an augmented reality (AR) interface, and the panoramic dynamic information of the ecosystem includes at least one of natural evolution prediction results and scenario deduction results.

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