Virtual reality landscaping simulation method and system fused with dynamic navigation

Abstract

This invention relates to a virtual reality landscaping simulation method and system integrating dynamic navigation. The method first constructs a digital twin model of a campus ecosystem, including various ecological entities and their quantified state parameters; establishes an ecological interaction model, defining and quantifying the type and intensity of interaction relationships between any two entities through mathematical functions; and generates a visualized ecological relationship network diagram based on this model. Users intervene in the model within the VR environment by editing commands on the network diagram, and the system translates these commands into modifications to the interaction model. Subsequently, the modified model is driven to perform dynamic ecological simulation calculations, obtaining the process of ecosystem state changes triggered by the editing operations. Finally, a dynamic visualization of this change process is generated and displayed in the VR environment. This invention achieves quantitative simulation and intuitive dynamic display of the ecological effects of greening schemes, enhancing the ecological intelligence and decision support capabilities of VR design tools.

Description

Technical Field

[0001] This invention relates to the field of interdisciplinary technology of virtual reality and ecological simulation, and in particular to a virtual reality gardening simulation method and system that integrates dynamic navigation. Background Technology

[0002] With the increasing application of virtual reality (VR) technology in landscape design and education, existing technologies can construct three-dimensional landscape scene models, allowing users to immerse themselves in spatial layout and vegetation configuration within VR environments. However, most of these systems focus on the geometric shape and visual representation of the scene, with their simulation depth remaining at the static or simple animation level. For landscape greening, its core value lies not only in landscape aesthetics but also in its function and sustainability as a dynamic ecosystem. Existing VR tools generally lack the ability to model the intrinsic operating mechanisms of ecosystems, failing to quantify the complex interactions (such as competition, symbiosis, predation, and environmental factors) between ecological entities such as plants, animals, microorganisms, and the abiotic environment. Furthermore, they cannot predict the chain ecological effects and long-term dynamic evolution caused by design changes. This makes it difficult for designers and decision-makers to scientifically assess the ecological impact of solutions during the VR design phase, and for educators to intuitively reveal hidden ecological laws to students. Therefore, developing a technical method that can deeply integrate quantitative simulation of ecological processes with dynamic VR navigation display has become an urgent technical problem to be solved in this field. Summary of the Invention

[0003] In view of the above problems, the purpose of this invention is to provide a virtual reality landscape simulation method and system that integrates dynamic navigation, in order to solve the technical problem that existing VR landscape design tools cannot quantify, simulate and intuitively display the complex interactions between ecological entities and their dynamic evolution processes.

[0004] To achieve the above objectives, in a first aspect, the present invention provides a virtual reality landscape simulation method integrating dynamic navigation, comprising the following steps:

[0005] S1: Construct a digital twin model of the campus ecosystem. The digital twin model contains multiple ecological entities with quantified state parameters. The ecological entities include at least two of the following: individual plants, soil regions, water units, individual animals, and microbial communities.

[0006] S2: Based on the digital twin model, an ecological interaction model is established. This model defines the interaction relationship between any two ecological entities using mathematical functions, where:

[0007] The interaction relationship has a preset relationship type, which includes inter-biological interaction type and environmental factor interaction type. The inter-biological interaction type includes any one of competition, symbiosis, predation, parasitism, and co-evolution. The environmental factor interaction type is used to characterize the influence of abiotic ecological entities on biotic ecological entities.

[0008] For each type of interaction relationship, the strength of the interaction is quantified by the corresponding mathematical function. The sign of the output value of the mathematical function indicates the direction of the interaction, with positive values ​​indicating a promoting effect and negative values ​​indicating an inhibiting effect.

[0009] S3: Based on the aforementioned ecological interaction model, generate a visualized ecological relationship network diagram, wherein:

[0010] Each ecological entity is represented as a node in the ecological relationship network graph;

[0011] Each pair of interacting ecological entities is represented by a connecting edge;

[0012] The visual feature mapping of connected edges corresponds to the relationship type and intensity of the interaction.

[0013] S4: Receive editing instructions from the user on the ecological relationship network graph via a virtual reality interactive device. These editing instructions include operations on nodes and connecting edges. Convert the editing instructions into modification instructions for the ecological interaction model. These modification instructions include deleting the ecological entities corresponding to the nodes.

[0014] Modify the state parameters of an ecological entity, or modify one or more of the interaction relationships between ecological entities;

[0015] S5: In response to the editing instruction, based on the modified ecological interaction model, perform ecological dynamic simulation calculations to obtain the process of ecosystem state change caused by the editing operation;

[0016] S6: In a virtual reality environment, based on the process of changes in the state of the ecosystem, generate and display dynamic visualization effects.

[0017] Furthermore, the mathematical function is a dynamic equation based on ecological principles, and its specific form is determined according to the interaction relationship and the state parameter types of the two ecological entities involved. The two ecological entities are respectively denoted as the first ecological entity and the second ecological entity.

[0018] For the aforementioned inter-biological interaction type, when the state parameters of both ecological entities involved in the interaction relationship include parameters characterizing biological population size or biomass, the mathematical function is a first-class function used to quantify the influence intensity of the rate of change of state parameters between biological ecological entities, specifically including:

[0019] If the relationship type is competition, the following formula is used to describe the competition relationship:

[0020] ;

[0021] ;

[0022] in, and These are the state parameters of the first ecological entity and the second ecological entity, respectively. and These are the inherent growth parameters of the first and second ecological entities, respectively. and These are the carrying capacity parameters for the first and second ecological entities, respectively. This represents the competition coefficient between the second ecological entity and the first ecological entity. This represents the competition coefficient between the first ecological entity and the second ecological entity.

[0023] If the relationship type is symbiotic, the symbiotic relationship is described by the following formula:

[0024] ;

[0025] ;

[0026] in, This represents the symbiotic benefit coefficient of the second ecological entity to the first ecological entity. >0; This represents the symbiotic benefit coefficient of the first ecological entity to the second ecological entity. Greater than 0;

[0027] If the relationship type is predation, the predation relationship is described by the following formula:

[0028] ;

[0029] ;

[0030] in, These are the state parameters of the ecological entity that acts as prey. For the state parameters of the ecological entity as a predator; The inherent growth parameter of the prey; This is the predator's attack rate coefficient, used to quantify the intensity of predation. This refers to the energy conversion efficiency parameter. For predator loss rate parameters;

[0031] If the relationship type is parasitic, the parasitic relationship is described by the following formula:

[0032] ;

[0033] ;

[0034] in, These are the state parameters of the ecological entity that serves as the host. These are the state parameters of the ecological entity as a parasite; and These represent the host's intrinsic growth parameter and carrying capacity parameter, respectively; γ is the parasitism rate coefficient, used to quantify the intensity of parasitism; θ is the parasite's energy acquisition efficiency parameter; and μ is the parasite's loss rate parameter.

[0035] If the relationship type is co-evolution, the following formula is used to describe the co-evolutionary relationship:

[0036] ;

[0037] ;

[0038] in, and These are the adaptive trait state parameters for the first and second ecological entities, respectively. and These are the theoretical optimal values ​​for the adaptive traits; and The intrinsic rate parameter for the evolution of each trait toward the optimal value; This represents the synergy coefficient between the second ecological entity and the changes in the traits of the first ecological entity. >0; This represents the synergy coefficient between the first ecological entity and the changes in the traits of the second ecological entity. >0;

[0039] For the aforementioned type of environmental factor interaction, when the interaction involves a biotic ecological entity and an abiotic ecological entity, the mathematical function is a second-type function used to quantify the influence of the state parameters of the abiotic ecological entity on the rate of change of the state parameters of the biotic ecological entity, expressed by the following formula:

[0040] ;

[0041] Wherein, B represents the state parameter of the biological ecological entity; S represents the state parameter of the non-biological ecological entity; This represents the rate of change of the state parameters of the biological ecological entity; It is a function that describes the dynamics of the biological ecological entity itself; It is an environmental impact factor function with S as the independent variable, used to quantify the impact intensity of the non-biological ecological entities.

[0042] Furthermore, step S5 specifically includes:

[0043] S51: Based on the modified ecological interaction model, construct a set of differential equations describing the changes of state parameters of each ecological entity over time, wherein the form of each equation is determined by the mathematical function.

[0044] S52: Set the simulation time step Δt, and update the initial conditions or coefficient parameters of the differential equation system based on the ecological entity state parameters or interaction relationship parameters modified by the editing instructions;

[0045] S53: The updated differential equation system is solved step by step using a numerical iterative method. The state parameter values ​​of each ecological entity are calculated after each time step, thereby obtaining the continuous trajectory of the evolution of the state of the ecosystem over simulation time from the editing time.

[0046] Furthermore, step S4 also includes converting the editing instructions into modification instructions for the ecological interaction model, and then performing model consistency verification and adaptive update operations, specifically including:

[0047] In a virtual reality environment, a modified ecological interaction model is subjected to real-time conflict detection based on a predefined ecological constraint rule base, and the detected ecological logical inconsistencies are fed back to the user through visual or auditory warning signals.

[0048] In response to the user's confirmation of the repair instruction, conflict resolution options are presented to the user through a virtual control panel. Based on the user's selection, an optimization model is solved with ecological stability index as the objective function and the ecological constraint rules as the constraint conditions. The system automatically calculates and implements adjustment schemes for the state parameters of relevant ecological entities and the strength of their interactions, so that the ecological interaction model is restored to a stable equilibrium state that conforms to ecological constraints. The adjustment details are also displayed synchronously through a virtual information panel.

[0049] Based on the connections in the modified ecological interaction model, all ecological entities and their interactions that are directly or indirectly affected by the current editing operation are derived through a graph traversal algorithm, generating impact propagation path data. In the ecological relationship network graph, the corresponding nodes and connecting edges are dynamically visually encoded according to the impact propagation path data. At the same time, a spatial coordinate mapping relationship is established between each ecological entity identifier in the impact propagation path data and the corresponding 3D model in the virtual reality 3D scene.

[0050] When a user's selection command for a highlighted node in the ecological relationship network diagram is captured through a virtual reality interactive device, the observation perspective of the virtual reality environment is automatically navigated and focused on the corresponding three-dimensional model of the ecological entity in the three-dimensional scene according to the spatial coordinate mapping relationship, and the following information is superimposed and displayed around the three-dimensional model of the ecological entity: the evolution curve of the state parameters corresponding to the ecological entity in the process of the change of the state of the ecosystem.

[0051] Furthermore, step S6 also includes the following steps:

[0052] A time series analysis is performed on the trajectory data of the state parameters of each ecological entity output by the ecological dynamic simulation calculation over time. The time points when the rate of change of the state parameters exceeds the first preset threshold or the system stability index is lower than the second preset threshold are identified as key time points. Based on the intensity data of each interaction relationship in the ecological interaction model, the interaction relationship with the greatest intensity at the key time point is determined as the dominant interaction relationship.

[0053] The perturbation analysis algorithm is used to quantify the causal contribution of different editing operations to the final ecosystem state, specifically including:

[0054] Let the set of edit operations be E = {e1, e2, ..., e}. n After performing all editing operations, the final state vector of the ecosystem is S. final The baseline state vector that has not undergone any editing operations is S. base Each editing operation e is simulated and calculated by controlling variables. i The state change vector ΔS triggered by a single entity i Then its contribution C i Calculated according to the following formula:

[0055] ;

[0056] Where ||·|| represents the norm of the vector. This represents the sum of the norms of the state change vectors triggered individually by all editing operations;

[0057] Based on contribution level C iEditing operations are sorted, and those with a contribution exceeding a preset contribution threshold are identified as primary causal factors.

[0058] The system automatically integrates the editing instructions, the coefficients of the mathematical functions defined in the ecological interaction model, the key time points and their corresponding dominant interaction relationships, and the causal contribution ranking results and the identified main causal factors to generate a structured virtual experiment report.

[0059] Furthermore, the method also includes:

[0060] Construct a multi-scale observation model, which includes at least a macro-ecosystem scale observation model, a meso-biological community scale observation model, and a micro-biological functional unit scale observation model, and establish a mapping relationship of state parameters for the ecological entities in the digital twin model between different scale observation models;

[0061] In the virtual reality environment, in response to the user's scale switching command for the target ecological entity, based on the mapping relationship of state parameters established between different scale observation models, the observation perspective is smoothly transitioned from the current scale to the target scale, and the focus is automatically placed on the corresponding representation of the target ecological entity in the target scale observation model.

[0062] When the observation perspective is at the scale of the microscopic biological functional unit, the following sub-steps are performed:

[0063] From the ecological dynamic simulation calculation performed in step S5, extract the parameter values ​​corresponding to the environmental factor action types defined by the ecological interaction model established in step S2 that act on the currently focused biological functional unit, and use them as environmental factor parameters; at the same time, extract at least one state variable value of the biological functional unit in the ecological dynamic simulation calculation, and use it as biological state parameters; input the environmental factor parameters and biological state parameters into a preset physiological response function, and calculate one or more microscopic physiological process rate values ​​in real time.

[0064] Depending on the type of microscopic physiological process, the corresponding rendering engine is invoked, and the calculated rate value of the microscopic physiological process or the process state variable calculated from the rate value is used for dynamic visualization:

[0065] For mass transport processes, the microscopic physiological process rate values ​​are used to drive the particle system, simulating the movement path and flux of matter.

[0066] For surface state and energy conversion processes, the rate value of the microscopic physiological process or the process state variable is used as an input parameter to drive the dynamic shader to change the surface visual properties of the three-dimensional model of the biological functional unit in real time.

[0067] Furthermore, the method also includes the following steps:

[0068] At the beginning of each simulation time step in step S5, the environmental factor parameters currently acting on the macroscopic ecological entity to which the current focused biological functional unit belongs are obtained, and the environmental factor parameters are used as input parameters of the physiological response function to drive the physiological response function to calculate the current time step.

[0069] Based on the calculation results of the physiological response function, the microscopic physiological process rate value of the currently focused biological functional unit is obtained; then, according to the spatial representation relationship between the biological functional unit and its corresponding macroscopic ecological entity, the microscopic physiological process rate values ​​of all related biological functional units are aggregated, and the net influence ΔB(t) on the core biological state parameters of the macroscopic ecological entity within the current time step is calculated. The formula for calculating the net influence ΔB(t) is as follows:

[0070] ΔB(t)=Σ[R k(t) ×w k ]×Δt;

[0071] Among them, R k(t) w represents the rate of the microphysiological process of the k-th related biological functional unit at the current time step. k Δt is the aggregation weight coefficient of the k-th biological functional unit, and Δt is the simulation time step.

[0072] The net impact ΔB(t) is used as a correction term and fed back to the ecological interaction model to update the instantaneous rate of change equation of the biological state parameter B of the macroscopic ecological entity in the current time step calculation. The update method of the instantaneous rate of change equation of the biological state parameter B is as follows:

[0073] ;

[0074] in, This is the basic rate of change calculated solely based on the inter-organism interactions in the aforementioned ecological interaction model;

[0075] The calculation of the current time step is completed based on the instantaneous rate of change equation of the updated biological state parameter B, and then the iteration of the next time step begins.

[0076] Furthermore, the method also includes:

[0077] S7: In the virtual reality environment, user interaction behavior data is collected in real time through a data acquisition interface. The interaction behavior data includes:

[0078] A sequence of operations to execute editing instructions on nodes or connecting edges in the ecological relationship network graph;

[0079] The duration of time spent in front of the interface displaying the ecological relationship network diagram generated in step S3 or the dynamic visualization of the ecosystem state change process displayed in step S6.

[0080] The viewing focus frequency of a specific type of ecological entity or the cluster area corresponding to that ecological entity in the digital twin model;

[0081] S8: Process the collected interaction behavior data based on the preset user classification model to generate a user profile, wherein the user profile includes at least two of the following: user identity type, ecological knowledge level, and learning preferences.

[0082] S9: Based on the user profile and the preset teaching knowledge graph, a personalized VR campus ecology tour route is generated through a path planning algorithm, wherein the preset teaching knowledge graph is associated with the digital twin model;

[0083] The path planning algorithm optimizes the spatial paths connecting the various ecological entities in the digital twin model based on the following constraints:

[0084] Maximize the number of ecological knowledge points covered by the path that are related to the relationship types defined in the ecological interaction model;

[0085] Minimize the sum of the total spatial length of the path and the estimated learning time required to complete all interactions on the path;

[0086] The matching degree between the types of ecological entities traversed by the path and the types of interactions associated with them and the learning preferences in the user profile is maximized.

[0087] The difference in difficulty level values ​​between adjacent teaching nodes in the path does not exceed a preset first threshold, and the sequence of difficulty level values ​​of the entire path satisfies monotonically non-decreasing or wave-shaped gradual change, wherein the teaching node is a key ecological entity or ecological entity cluster area in the digital twin model.

[0088] S10: During VR tours, based on the user's real-time location within the virtual space of the digital twin model, dynamic navigation guidance is provided through spatial path rendering technology. This dynamic navigation guidance includes:

[0089] S101: Based on the spatial relationship between the user's current location and the planned route, generate and overlay navigation instructions in real time onto the virtual reality scene;

[0090] S102: Based on the user's travel speed and direction, predict and preload the three-dimensional model data of the ecological entities associated with the next navigation point and their relationship data in the ecological interaction model;

[0091] S103: When it is detected that the distance between the user's current location and the planned route exceeds a preset threshold, the path replanning algorithm is triggered to recalculate the guiding path.

[0092] Furthermore, step S102 includes:

[0093] Based on the user's location, direction of travel, and instantaneous velocity vector obtained through real-time positioning, and combined with the spatial orientation of the planned route, the spatial area to be covered by the user's view frustum or travel path within a preset time period ΔT is predicted using linear extrapolation or curve fitting algorithms; ecological entities located within the predicted spatial area and belonging to the digital twin model are identified as the potential access target set Z.

[0094] For each ecological entity E in the set of potential access targets Z i Perform ecological network importance assessment and user interest matching assessment;

[0095] The assessment of the importance of the ecological network includes: calculating the ecological entity E. i The normalized degree centrality and normalized betweenness centrality are calculated from the ecological relationship network graph, and the normalized degree centrality and normalized betweenness centrality are weighted and summed to obtain the ecological entity E. i The corresponding network importance score S net(i) ;

[0096] The user interest matching assessment includes: constructing ecological entity E i The attribute feature vector is obtained, which includes at least its ecology type encoding and the encoding of the associated dominant interaction type; the cosine similarity between this vector and the learning preference feature vector extracted from the current user profile is calculated to obtain the interest matching score S. pref(i) ;

[0097] Calculate ecological entity E using the following formula. i Comprehensive preloading priority

[0098] P riority（i） =w i· S net(i) +(1−w i )·S pref(i) ;

[0099] Among them, the fusion weight coefficient w i w is a variable that is dynamically adjusted based on the real-time available graphics memory usage of the computing device running the virtual reality environment. When the available graphics memory usage exceeds a first usage threshold, w is set. i The first value is used; when the available graphics memory usage is lower than the second threshold, w is set. i The second value, w iThe value range is (0,1), and the first value is greater than the second value;

[0100] Based on the calculated comprehensive preloading priority, all ecological entities in the potential access target set Z are sorted in descending order, and the following hierarchical loading strategy is executed according to the sorting results:

[0101] For ecological entities ranked in the top R1% of priority, load the complete 3D model, first-resolution texture, real-time state parameters and direct relationship data in the ecological relationship network corresponding to the ecological entity.

[0102] For ecological entities ranked in the middle R2%, load the simplified 3D model, second-resolution texture, and key state parameters of the ecological entity.

[0103] For ecological entities ranked in the bottom 3% of priority, only the spatial location identifier and name label of the ecological entity are loaded.

[0104] The first resolution is higher than the second resolution. R1, R2, and R3 are preset percentage parameters, and R1+R2+R3=100.

[0105] In a second aspect, the present invention provides a virtual reality landscape simulation system integrating dynamic navigation, the system being used to perform the method as described in the first aspect of the present invention, the system comprising:

[0106] The model building module is used to build a digital twin model of the campus ecosystem. The digital twin model contains multiple ecological entities with quantified state parameters. The ecological entities include at least two of the following: individual plants, soil regions, water units, individual animals, and microbial communities.

[0107] An interaction modeling module is used to establish an ecological interaction model based on the digital twin model. This ecological interaction model defines the interaction relationship between any two ecological entities through mathematical functions, wherein:

[0108] The interaction relationship has a preset relationship type, which includes inter-biological interaction type and environmental factor interaction type. The inter-biological interaction type includes any one of competition, symbiosis, predation, parasitism, and co-evolution. The environmental factor interaction type is used to characterize the influence of abiotic ecological entities on biotic ecological entities.

[0109] For each type of interaction relationship, the strength of the interaction is quantified by the corresponding mathematical function. The sign of the output value of the mathematical function indicates the direction of the interaction, with positive values ​​indicating a promoting effect and negative values ​​indicating an inhibiting effect.

[0110] The network graph generation module is used to generate a visualized ecological relationship network graph based on the ecological interaction model, wherein:

[0111] Each ecological entity is represented as a node in the ecological relationship network graph;

[0112] Each pair of interacting ecological entities is represented by a connecting edge;

[0113] The visual feature mapping of connected edges corresponds to the relationship type and intensity of the interaction.

[0114] The interaction and instruction conversion module is used to receive editing instructions from the user on the ecological relationship network graph through a virtual reality interaction device. The editing instructions include operations on nodes and connecting edges. The module converts the editing instructions into modification instructions for the ecological interaction model. The modification instructions include any one or more of the following: deleting the ecological entity corresponding to the node, modifying the state parameters of the ecological entity, and modifying the interaction relationship between the ecological entities.

[0115] A dynamic simulation calculation engine is used to respond to the editing command and perform dynamic ecological simulation calculations based on the modified ecological interaction model to obtain the process of ecosystem state change caused by the editing operation.

[0116] The virtual reality rendering and display module is used to generate and display dynamic visualization effects in a virtual reality environment based on the process of ecosystem state change.

[0117] Unlike existing technologies, the virtual reality landscaping simulation method and system integrating dynamic navigation described above belongs to the field of VR and ecological simulation technology. This method first constructs a digital twin model of a campus ecosystem containing various ecological entities and their quantified state parameters. Then, an ecological interaction model is established, defining and quantifying the type and intensity of interaction relationships (such as competition, symbiosis, predation, and environmental factors) between any two entities through mathematical functions. Based on this model, a visualized ecological relationship network graph is generated, where nodes and connecting edges represent ecological entities and their interactions, respectively. Users intervene in the model in the VR environment by using editing commands on the network graph (such as adding or deleting entities and modifying relationships). The system translates these commands into modifications to the interaction model. Subsequently, the modified model is driven to perform dynamic ecological simulation calculations, obtaining the process of ecosystem state changes triggered by the editing operations. Finally, the dynamic visualization effect of this change process is generated and displayed in the VR environment. This invention achieves quantitative simulation and intuitive dynamic display of the ecological effects of greening schemes, enhancing the ecological intelligence and decision support capabilities of VR design tools.

[0118] The above description of the invention is merely an overview of the technical solution of the present invention. In order to enable those skilled in the art to better understand the technical solution of the present invention and to implement it based on the description and drawings, and to make the above-mentioned objectives and other objectives, features and advantages of the present invention easier to understand, the following description is provided in conjunction with the specific embodiments and drawings of the present invention. Attached Figure Description

[0119] The accompanying drawings are only used to illustrate the principles, implementation methods, applications, features, and effects of specific embodiments of the present invention and other related contents, and should not be considered as limitations on the present invention.

[0120] In the accompanying drawings of the instruction manual:

[0121] Figure 1 A flowchart illustrating a virtual reality landscape simulation method incorporating dynamic navigation, as described in the first exemplary embodiment of this application;

[0122] Figure 2 A flowchart illustrating a virtual reality landscape simulation method incorporating dynamic navigation, as described in the second exemplary embodiment of this application;

[0123] Figure 3 A flowchart illustrating a virtual reality landscape simulation method incorporating dynamic navigation, as described in the second exemplary embodiment of this application;

[0124] Figure 4 A flowchart illustrating a virtual reality landscape simulation method incorporating dynamic navigation, as described in the second exemplary embodiment of this application;

[0125] Figure 5 A flowchart illustrating a virtual reality landscape simulation method incorporating dynamic navigation, as described in the second exemplary embodiment of this application;

[0126] Figure 6 This is a schematic diagram of a module of a virtual reality landscape simulation system with integrated dynamic navigation according to an exemplary embodiment of this application;

[0127] The reference numerals used in the above figures are explained as follows:

[0128] 20. A virtual reality landscape simulation system integrating dynamic navigation;

[0129] 201. Model building module;

[0130] 202. Interaction Modeling Module;

[0131] 203. Network diagram generation module;

[0132] 204. Interaction and instruction conversion module;

[0133] 205. Dynamic simulation calculation engine;

[0134] 206. Virtual Reality Rendering and Display Module. Detailed Implementation

[0135] To illustrate the possible application scenarios, technical principles, implementable specific solutions, and achievable objectives and effects of this invention in detail, the following description, in conjunction with the listed specific embodiments and accompanying drawings, provides a detailed explanation. The embodiments described herein are merely illustrative of the technical solutions of this invention and are therefore intended only as examples, not as limiting the scope of protection of this invention.

[0136] In this document, the term "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The term "embodiment" appearing in various places throughout the specification does not necessarily refer to the same embodiment, nor does it specifically limit its independence or connection with other embodiments. In principle, in this invention, as long as there are no technical contradictions or conflicts, the technical features mentioned in each embodiment can be combined in any way to form corresponding implementable technical solutions.

[0137] Unless otherwise defined, the technical terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the use of related terms herein is merely for the purpose of describing particular embodiments and is not intended to limit the invention.

[0138] In the description of this invention, the term "and / or" is used to describe the logical relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A exists, B exists, and A and B exist simultaneously. Additionally, the character " / " generally indicates that the preceding and following objects have an "or" logical relationship.

[0139] In this invention, terms such as “first” and “second” are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any actual quantity, hierarchy, or order between these entities or operations.

[0140] Without further limitations, the use of terms such as “comprising,” “including,” “having,” or other similar open-ended expressions in this invention is intended to cover non-exclusive inclusion, which does not exclude the presence of additional elements in a process, method, or product that includes the stated elements, such that a process, method, or product that includes a list of elements may include not only those defined elements but also other elements not expressly listed, or elements inherent to such a process, method, or product.

[0141] In this invention, expressions such as "greater than", "less than", and "exceeding" are understood to exclude the stated number; expressions such as "above", "below", and "within" are understood to include the stated number. Furthermore, in the description of the embodiments of this invention, "multiple" means two or more (including two), and similar expressions related to "multiple" are also understood in this way, such as "multiple groups" and "multiple times", unless otherwise explicitly specified.

[0142] In the first aspect, such as Figure 1 As shown, this application provides a virtual reality landscape simulation method integrating dynamic navigation, comprising the following steps:

[0143] S1: Construct a digital twin model of the campus ecosystem. The digital twin model contains multiple ecological entities with quantified state parameters. The ecological entities include at least two of the following: individual plants, soil regions, water units, individual animals, and microbial communities.

[0144] S2: Based on the digital twin model, an ecological interaction model is established. This model defines the interaction relationship between any two ecological entities using mathematical functions, where:

[0145] The interaction relationship has a preset relationship type, which includes inter-biological interaction type and environmental factor interaction type. The inter-biological interaction type includes any one of competition, symbiosis, predation, parasitism, and co-evolution. The environmental factor interaction type is used to characterize the influence of abiotic ecological entities on biotic ecological entities.

[0146] For each type of interaction relationship, the strength of the interaction is quantified by the corresponding mathematical function. The sign of the output value of the mathematical function indicates the direction of the interaction, with positive values ​​indicating a promoting effect and negative values ​​indicating an inhibiting effect.

[0147] S3: Based on the aforementioned ecological interaction model, generate a visualized ecological relationship network diagram, wherein:

[0148] Each ecological entity is represented as a node in the ecological relationship network graph;

[0149] Each pair of interacting ecological entities is represented by a connecting edge;

[0150] The visual feature mapping of connected edges corresponds to the relationship type and intensity of the interaction.

[0151] S4: Receive editing instructions from the user on the ecological relationship network graph via a virtual reality interactive device. These editing instructions include operations on nodes and connecting edges. Convert the editing instructions into modification instructions for the ecological interaction model. These modification instructions include deleting the ecological entities corresponding to the nodes.

[0152] Modify the state parameters of an ecological entity, or modify one or more of the interaction relationships between ecological entities;

[0153] S5: In response to the editing instruction, based on the modified ecological interaction model, perform ecological dynamic simulation calculations to obtain the process of ecosystem state change caused by the editing operation;

[0154] S6: In a virtual reality environment, based on the process of changes in the state of the ecosystem, generate and display dynamic visualization effects.

[0155] In this embodiment, a digital twin model refers to a virtual mapping model constructed using digital technology based on data such as the real geographical environment, biological distribution, and environmental conditions of a campus ecosystem. Its core feature is that it contains multiple quantifiable ecological entities, and the state parameters of these entities (such as plant biomass, soil moisture, and water pH) are consistent with or correlated with the real ecosystem, thus reflecting the structure and state of the real ecosystem.

[0156] Ecological entities are the basic units that constitute the campus ecosystem, encompassing biological entities (individual plants, individual animals, and microbial communities) and abiotic entities (soil regions and water bodies). Each entity possesses quantifiable state parameters and is the core research object of ecological interaction models.

[0157] Ecological interaction models are models based on ecological principles that quantify the interaction relationships between any two ecological entities through mathematical functions. Their core function is to transform complex ecological relationships into calculable and analyzable mathematical expressions, providing a logical foundation for subsequent dynamic simulations.

[0158] An ecological relationship network diagram is a visual representation of an ecological interaction model. Nodes represent ecological entities and connecting edges represent interaction relationships. The visual characteristics of the connecting edges (such as color, line type, and thickness) intuitively map the relationship type and the intensity of the interaction, providing users with an intuitive interface for interactive operation.

[0159] Virtual reality interactive devices include hardware devices such as VR headsets and VR controllers, which can capture user operation commands (such as clicking, dragging, and deleting) and convert them into signals that the system can recognize, enabling immersive interaction between users and the virtual ecosystem.

[0160] Specifically, the working principle of the method involved in this embodiment is as follows:

[0161] In step S1, basic data on the campus ecosystem is first collected, including topographic data (such as slope and altitude), biological distribution data (such as plant species, quantity, and distribution location; animal habitat range; and microbial community distribution area), and environmental parameter data (such as soil type, humidity, nutrient content, water area, depth, and water quality indicators). Based on this data, a virtual campus ecological scene is constructed using 3D modeling technology (such as GIS and BIM). Quantitative state parameters are assigned to each ecological entity; for example, growth rate, biomass, and light requirements are set for individual plants, and pH, water content, and nutrient concentration are set for soil areas. This ultimately forms a digital twin model that accurately maps the structure and state of the real campus ecosystem. In step S2, based on the digital twin model, the types of interaction relationships between ecological entities are first clarified, divided into inter-biological interaction types and environmental factor interaction types. Interbiotic interactions encompass competition (e.g., two herbaceous plants competing for sunlight and water), symbiosis (e.g., legumes and rhizobia), predation (e.g., birds preying on insects), parasitism (e.g., dodder parasitizing other plants), and co-evolution (e.g., plants and pollinating insects adapting to each other's traits). Environmental factors focus on the influence of abiotic entities on biotic entities (e.g., soil moisture affecting plant growth, water pH affecting aquatic animal survival). For each type of relationship, the intensity of the interaction is quantified using a corresponding mathematical function. The sign of the output value of the mathematical function determines the direction of the interaction; a positive output value indicates promotion (e.g., mutual promotion of growth in a symbiotic relationship), while a negative output value indicates inhibition (e.g., one party inhibiting the growth of the other in a competitive relationship), thus transforming qualitative ecological relationships into quantitative mathematical expressions.

[0162] In step S3, based on the ecological interaction model established in step S2, each ecological entity is abstracted as a node in the network graph, and the interaction relationship between each pair of entities is abstracted as a connecting edge. To achieve a visual distinction between relationship types and interaction strength, a unified visual coding rule can be set for the connecting edges. For example, red solid lines can represent competition relationships, and green dashed lines can represent symbiotic relationships. The thickness of the connecting edge is positively correlated with the interaction strength coefficient; the greater the strength, the thicker the edge. At the same time, key parameters (such as competition coefficient and symbiotic benefit coefficient) can be labeled on the connecting edges. Finally, an intuitive and easy-to-understand ecological relationship network graph is generated, providing an operational platform for user interaction.

[0163] In step S4, the user can edit the ecological relationship network graph using a virtual reality interactive device such as a VR controller. Editing operations include node deletion (e.g., deleting an invasive plant entity), node attribute modification (e.g., adjusting plant growth rate parameters), and edge modification (e.g., changing the competitive relationship between two plants to no interaction). These operation commands are then captured by the sensors of the virtual reality interactive device and converted into modification commands for the ecological interaction model. For example, the instruction to delete a node corresponds to removing the ecological entity and all interactions related to that entity from the model; the instruction to modify attributes corresponds to adjusting the state parameter values ​​of that entity in the model; and the instruction to modify edges corresponds to adjusting the type or intensity coefficient of related interactions in the model.

[0164] In step S5, after the ecological interaction model is modified, the state change process of the ecosystem needs to be calculated based on the modified model. The core logic is as follows: the modified ecological interaction model corresponds to a new set of ecological association rules and parameter configurations. Based on these rules and parameters, combined with ecological principles (such as population growth laws and material cycling laws), numerical calculation methods (such as solving differential equations) are used to simulate the trajectory of state parameters of each ecological entity over time. For example, if a user deletes a predator node for a certain type of insect, the system will simulate that the insect population increases due to the lack of natural predators, leading to a decrease in its food (such as a certain type of plant), ultimately triggering a chain reaction of changes throughout the food chain. In step S6, the state change process calculated in step S5 is transformed into an immersive dynamic effect using VR rendering technology. For example, when simulating plant growth, the process from seedling to mature plant is shown through scaling and color gradients of the 3D model; when simulating population changes, the changes are shown through dynamic increases and decreases in the number of entities and switching of status indicators (such as healthy / withered, active / dormant); when simulating changes in ecological relationships, the strengthening, weakening, or disappearance of interactions is shown through highlighting, blinking, or color changes of connecting edges. Simultaneously, combined with dynamic navigation technology, the display angle is automatically adjusted according to the user's viewing perspective, ensuring that the user can clearly observe key ecological change processes.

[0165] Taking a virtual reality (VR) landscape architecture student's experiment using a virtual reality landscape simulation system to design a campus greening scheme as an example, the interaction process is as follows: First, the VR landscape simulation system loads a digital twin model of the existing campus ecosystem, generating an ecological relationship network diagram containing entities such as plants, soil, water, insects, and birds. The simulation system analyzes the strength and symbol of the connecting edges in the network diagram, automatically identifying areas where the "competition" between two types of shrubs exceeds a preset threshold. It then sends a warning message to the student, highlighting the competition or displaying a pop-up window, indicating that the competition may lead to growth inhibition. After receiving the warning message, the student selects one of the shrub nodes suggested for adjustment using the VR controller and performs a deletion operation. The simulation system then converts this editing command into a model modification command, removing the shrub entity and its related connecting edges. Following this, a dynamic simulation calculation is performed: after deletion, the remaining shrubs experience reduced competition, resulting in increased growth rate, gradually increasing biomass, and a rise in the number of insects dependent on the shrub, thus attracting more birds to inhabit the area. Finally, the simulation system dynamically displays this process in the VR environment, such as the remaining shrubs gradually growing taller and the branches and leaves becoming more lush, the number of insects moving among the branches and leaves increasing, and birds flying into the area to forage and build nests. Students can intuitively observe the chain of ecological changes caused by this editing operation through VR headsets.

[0166] The method described in this embodiment brings many beneficial effects by constructing a complete virtual reality ecosystem simulation framework, as detailed below:

[0167] First, it improves the scientific rigor and accuracy of ecological simulation. By accurately mapping the real campus ecosystem through digital twin models, complex ecological entities and relationships are transformed into quantifiable and computable mathematical models. This avoids the ambiguity of ecological relationships in traditional simulation methods, enabling simulation results to truly reflect the inherent laws of the ecosystem.

[0168] Secondly, it lowers the barrier to understanding ecological complexity for users. Ecological relationship network diagrams display complex ecological connections through intuitive visual encoding. Users do not need in-depth ecological knowledge to quickly understand the interactions between different entities by observing the characteristics of nodes and connecting edges, thus solving the problems of complex operation and high difficulty in understanding traditional ecological simulation tools.

[0169] Furthermore, it supports exploratory learning and experimentation. Users can actively explore the impact of different operations on the ecosystem by editing the ecological model in real time. For example, they can simulate scenarios such as introducing new plants, removing certain organisms, and changing environmental parameters, thereby gaining a deeper understanding of the core principles of ecosystem stability and interconnectedness. This is especially suitable for scenarios such as landscape design and ecology teaching.

[0170] Finally, it enhances immersion and teaching effectiveness. The virtual reality environment provides users with an immersive observation and operation experience. Dynamic visualization effects transform abstract ecological change processes into intuitive visual presentations. Compared to traditional two-dimensional charts and text descriptions, it is more likely to stimulate users' learning interest and help them quickly master ecological knowledge and landscape design skills.

[0171] In some embodiments, the mathematical function is a dynamic equation based on ecological principles, and its specific form is determined according to the interaction relationship and the state parameter types of the two ecological entities involved, which are respectively denoted as the first ecological entity and the second ecological entity.

[0172] For the aforementioned inter-biological interaction type, when the state parameters of both ecological entities involved in the interaction relationship include parameters characterizing biological population size or biomass, the mathematical function is a first-class function used to quantify the influence intensity of the rate of change of state parameters between biological ecological entities, specifically including:

[0173] If the relationship type is competition, the following formula is used to describe the competition relationship:

[0174] ;

[0175] ;

[0176] in, and These are the state parameters of the first ecological entity and the second ecological entity, respectively. and These are the inherent growth parameters of the first and second ecological entities, respectively. and These are the carrying capacity parameters for the first and second ecological entities, respectively. This represents the competition coefficient between the second ecological entity and the first ecological entity. This represents the competition coefficient between the first ecological entity and the second ecological entity.

[0177] If the relationship type is symbiotic, the symbiotic relationship is described by the following formula:

[0178] ;

[0179] ;

[0180] in, This represents the symbiotic benefit coefficient of the second ecological entity to the first ecological entity. >0; This represents the symbiotic benefit coefficient of the first ecological entity to the second ecological entity. Greater than 0;

[0181] If the relationship type is predation, the predation relationship is described by the following formula:

[0182] ;

[0183] ;

[0184] in, These are the state parameters of the ecological entity that acts as prey. For the state parameters of the ecological entity as a predator; The inherent growth parameter of the prey; This is the predator's attack rate coefficient, used to quantify the intensity of predation. This refers to the energy conversion efficiency parameter. For predator loss rate parameters;

[0185] If the relationship type is parasitic, the parasitic relationship is described by the following formula:

[0186] ;

[0187] ;

[0188] in, These are the state parameters of the ecological entity that serves as the host. These are the state parameters of the ecological entity as a parasite; and These represent the host's intrinsic growth parameter and carrying capacity parameter, respectively; γ is the parasitism rate coefficient, used to quantify the intensity of parasitism; θ is the parasite's energy acquisition efficiency parameter; and μ is the parasite's loss rate parameter.

[0189] If the relationship type is co-evolution, the following formula is used to describe the co-evolutionary relationship:

[0190] ;

[0191] ;

[0192] in, and These are the adaptive trait state parameters for the first and second ecological entities, respectively. and These are the theoretical optimal values ​​for the adaptive traits; and The intrinsic rate parameter for the evolution of each trait toward the optimal value; This represents the synergy coefficient between the second ecological entity and the changes in the traits of the first ecological entity. >0; This represents the synergy coefficient between the first ecological entity and the changes in the traits of the second ecological entity. >0;

[0193] For the aforementioned type of environmental factor interaction, when the interaction involves a biotic ecological entity and an abiotic ecological entity, the mathematical function is a second-type function used to quantify the influence of the state parameters of the abiotic ecological entity on the rate of change of the state parameters of the biotic ecological entity, expressed by the following formula:

[0194] ;

[0195] Wherein, B represents the state parameter of the biological ecological entity; S represents the state parameter of the non-biological ecological entity; This represents the rate of change of the state parameters of the biological ecological entity; It is a function that describes the dynamics of the biological ecological entity itself; It is an environmental impact factor function with S as the independent variable, used to quantify the impact intensity of the non-biological ecological entities.

[0196] In this embodiment, the dynamic equation is a mathematical equation derived from ecological principles that describes the changes in the state parameters of ecological entities over time. Its core feature is that it can quantify the influence of interaction relationships on the rate of change of state parameters, and it is the core mathematical implementation form of the ecological interaction model.

[0197] The first type of function specifically refers to mathematical functions used to quantify the influence of interactions between organisms (where the state parameters of both biological entities include population size or biomass parameters) on the rate of change of state parameters, covering the dynamic equations corresponding to competition, symbiosis, predation, parasitism, and co-evolution.

[0198] The second type of function specifically refers to mathematical functions used to quantify the influence of environmental factors (interactions between biological and abiotic ecological entities) on the rate of change of state parameters of biological entities. Its core is to separate the dynamics of the organism itself from the influence of the environment and achieve the coupling of the two through a product form.

[0199] State parameters are quantitative indicators that describe the characteristics of ecological entities. For biological entities, these include population size, biomass, and adaptive traits; for abiotic entities, they include soil moisture, light intensity, and water pH. These are the core variables in the dynamic equations.

[0200] The first type of function is applicable when the two interacting biological entities have quantifiable core indicators in their state parameters, such as population size (e.g., number of individuals) or biomass. This type of function is based on classic population ecology dynamics models and describes the rate of change of state parameters through differential equations. Specifically, it includes functions for competition, symbiosis, predation, parasitism, and co-evolution.

[0201] The second type of function is applicable when the interaction involves a biotic ecological entity and a non-biotic ecological entity (such as plants and soil, aquatic animals and water bodies), and its formula is: In this context, B represents the state parameters of biological entities (such as plant biomass and animal population size), S represents the state parameters of non-biological entities (such as soil moisture and water pH), and dB / dt represents the rate of change of biological state parameters. f(B) is a function describing the dynamics of the organism itself. For example, for plants, f(B) can be expressed as a growth function based on biomass, reflecting the plant's own growth pattern. g(S) is an environmental influence factor function, the form of which is determined according to the type of environmental parameter. For example, when S is soil moisture, g(S) can adopt a normal distribution function (the value of g(S) is the largest when the humidity is within a suitable range, and gradually decreases when it exceeds the range, quantifying the intensity of the influence of soil moisture on plant growth). The core logic of the second type of function is that the rate of change of biological state parameters is the coupling result of its own dynamics and environmental influences. The superposition of the two is achieved through a product form, which not only ensures the rationality of the organism's own growth pattern, but also accurately reflects the influence of environmental parameters. In this embodiment, all mathematical functions are derived based on ecological principles (such as the Lotka-Volterra model, population growth theory, and co-evolution theory), accurately reflecting the inherent laws of different ecological interactions. This avoids the problem of simulation results not matching actual ecological laws that may be caused by user-defined functions, thus ensuring the scientific basis of the simulation. The mathematical functions cover the main types of interbiotic interactions (competition, symbiosis, predation, parasitism, and co-evolution) and types of environmental factors, meeting the simulation needs of most ecological relationships in the campus ecosystem. Whether it is the biological interaction between plants, animals, or plants and animals, or the environmental impact of abiotic entities such as soil and water on organisms, all can be quantified through the corresponding functions.

[0202] The coefficients in the mathematical functions (such as competition coefficient, symbiotic benefit coefficient, and attack rate coefficient) are all adjustable parameters. Users can modify these parameters according to experimental needs to explore ecological changes under different scenarios. For example, adjusting the predator attack rate coefficient can help observe its impact on prey populations, providing flexible tools for ecological experiments and optimizing landscape design schemes. The mathematical functions transform ecological relationships into explicit differential equations or product functions, enabling the system to solve for the rate of change of state parameters using numerical calculation methods. This provides executable computational logic for the dynamic simulation in step S5, ensuring the smoothness of the simulation process and the accuracy of the results.

[0203] In some embodiments, such as Figure 2 As shown, step S5 specifically includes:

[0204] S51: Based on the modified ecological interaction model, construct a set of differential equations describing the changes of state parameters of each ecological entity over time, wherein the form of each equation is determined by the mathematical function.

[0205] S52: Set the simulation time step Δt, and update the initial conditions or coefficient parameters of the differential equation system based on the ecological entity state parameters or interaction relationship parameters modified by the editing instructions;

[0206] S53: The updated differential equation system is solved step by step using a numerical iterative method. The state parameter values ​​of each ecological entity are calculated after each time step, thereby obtaining the continuous trajectory of the evolution of the state of the ecosystem over simulation time from the editing time.

[0207] In this embodiment, the differential equation system is a system of multiple differential equations. Each equation corresponds to the state parameter change law of an ecological entity. The overall solution of the equation system can reflect the state change trajectory of all entities in the entire ecosystem.

[0208] Initial conditions refer to the state parameter values ​​of each ecological entity at the start of the simulation (such as initial population size, initial biomass, and initial environmental parameter values). They are the starting point for solving the differential equation system, and their values ​​are determined by the user's editing instructions or the system default values.

[0209] The coefficient parameters refer to the constant coefficients of each term in the differential equation system (such as competition coefficient, intrinsic growth parameter, and environmental impact coefficient). Their values ​​are determined by the ecological interaction model. The user's editing commands can modify these coefficients, thereby affecting the solution of the equation system.

[0210] The simulation time step Δt refers to the smallest unit of time in dynamic simulation (such as 1 day, 1 week, 1 month). The system realizes the continuous evolution simulation of the ecosystem over time by calculating the state parameter values ​​after each time step step. The size of the time step can be adjusted according to the simulation accuracy and computational efficiency requirements.

[0211] Numerical iterative methods are numerical computation methods for solving systems of differential equations, including the Euler method and the Runge-Kutta method. Their core principle is to obtain the state parameter values ​​for the next time step by iteratively calculating the state parameter values ​​based on the state parameter values ​​at the current time step, and gradually accumulate the state change trajectory throughout the entire simulation cycle.

[0212] This application transforms the ecological interaction model into a system of differential equations, which are then solved using numerical iterative methods to obtain the trajectory of ecosystem state changes. The detailed working mechanism of each step is as follows:

[0213] In step S51, based on the modified ecological interaction model, corresponding differential equations are constructed for the state parameters of each ecological entity, ultimately forming a set of differential equations. Specifically, for each pair of ecological entities with an interaction relationship, a corresponding mathematical function (first-type function or second-type function) is selected according to the type of relationship. The selected mathematical function is used as the rate of change equation (dB / dt or dN / dt) of the state parameters, and the rate of change equations of all ecological entities are integrated to form a set of differential equations describing the state changes of the entire ecosystem.

[0214] For example, a campus ecosystem contains three ecological entities: turfgrass, shrubs, and soil moisture. Turfgrass and shrubs are in a competitive relationship (corresponding to the competition equation in the first type of function), while soil moisture acts as an environmental factor on both turfgrass and shrubs (corresponding to the second type of function). The constructed differential equation set includes: the rate of change equation for turfgrass, the rate of change equation for shrubs, and the rate of change equation for soil moisture (if soil moisture is a dynamic parameter, such as being affected by precipitation or transpiration, a corresponding rate of change equation can be constructed).

[0215] In step S52, the simulation time step Δt is first set. The choice of time step needs to balance simulation accuracy and computational efficiency. The smaller Δt is, the higher the simulation accuracy, but the greater the computational load; the larger Δt is, the higher the computational efficiency, but it may lead to a decrease in accuracy. For example, Δt = 1 day can be selected to simulate short-term plant growth (within 1 month), and Δt = 1 month can be selected to simulate long-term evolution (within 10 years). Subsequently, the initial conditions or coefficient parameters of the differential equation system are updated according to the user's editing instructions: if the user performs node attribute modification operations (such as adjusting the growth rate of lawn grass), the coefficient parameters in the corresponding equations are updated. If the user performs a node deletion operation (such as deleting a certain type of insect), then the equation corresponding to that insect and all terms related to that insect are removed from the equation set; if the user performs a connection modification operation (such as changing a competitive relationship to a symbiotic relationship), then the functional form of the corresponding equation is replaced (from a competitive equation to a symbiotic equation) and the correlation coefficient is updated (such as replacing the competition coefficient with a symbiotic benefit coefficient).

[0216] In step S53, a numerical iterative method (such as the fourth-order Runge-Kutta method, which has the characteristics of high accuracy and good stability and is suitable for simulating complex nonlinear systems such as ecosystems) is used to solve the updated differential equations step by step. The specific process is as follows:

[0217] Starting with the initial conditions determined in step S52, calculate the values ​​of each state parameter after the first time step Δt; use the result of the first time step as the initial condition for the second time step, and calculate the parameter values ​​after the second time step; and so on, iteratively calculating the state parameter values ​​after each time step until the preset simulation period (e.g., 1 year, 5 years) is reached. Through this process, continuous trajectory data of the evolution of the state parameters of each ecological entity over simulation time is obtained from the editing time (e.g., the change curve of turfgrass population size over time, the fluctuation trend of soil moisture over time).

[0218] The above scheme, by constructing and solving a system of differential equations step by step, can simulate the continuous change of ecological entity state parameters over time, rather than discrete state jumps. This makes the simulation results more consistent with the evolutionary laws of real ecosystems (such as continuous processes of plant growth and population size changes), helping users to observe ecological change trends more intuitively. Users can choose the time step according to their simulation needs, balancing simulation accuracy and computational efficiency. For short-term processes requiring detailed observation (such as plant seedling growth), a small step step can be selected; for long-term trend prediction (such as the 5-year evolution of a campus ecosystem), a large step step can be selected to meet the needs of different scenarios. The continuous state trajectory data obtained through numerical calculation contains detailed state information of each ecological entity at each time step, providing a rich data source for the dynamic visualization in step S6, ensuring that the visualization effect can accurately reflect the ecological change process. For example, based on continuous data on population size, the number, size, and other attributes of the VR model can be dynamically adjusted.

[0219] In some embodiments, such as Figure 3 As shown, step S4 further includes converting the editing instructions into modification instructions for the ecological interaction model, and then performing model consistency verification and adaptive update operations, specifically including:

[0220] S31: In a virtual reality environment, real-time conflict detection is performed on the modified ecological interaction model based on a predefined ecological constraint rule base, and the detected ecological logical inconsistencies are fed back to the user through visual or auditory warning signals.

[0221] S32: In response to the user's confirmation of the repair instruction, the conflict solution options are presented to the user through the virtual control panel. Based on the user's selection, the system automatically calculates and implements the adjustment scheme for the state parameters of relevant ecological entities and the strength of their interaction relationships by solving an optimization model with ecological stability index as the objective function and the ecological constraint rules as the constraint conditions. This restores the ecological interaction model to a stable equilibrium state that conforms to ecological constraints. The adjustment details are also displayed synchronously through the virtual information panel.

[0222] S33: Based on the connection relationships in the modified ecological interaction model, all ecological entities and their interaction relationships directly or indirectly affected by the current editing operation are derived through a graph traversal algorithm, generating influence propagation path data; in the ecological relationship network graph, the corresponding nodes and connecting edges are dynamically visually encoded according to the influence propagation path data; at the same time, a spatial coordinate mapping relationship is established between each ecological entity identifier in the influence propagation path data and the corresponding three-dimensional model in the virtual reality three-dimensional scene.

[0223] S34: When the user's selection instruction for the highlighted node in the ecological relationship network diagram is captured through the virtual reality interaction device, the observation perspective of the virtual reality environment is automatically navigated and focused on the corresponding three-dimensional model of the ecological entity in the three-dimensional scene according to the spatial coordinate mapping relationship, and the following information is superimposed and displayed around the three-dimensional model of the ecological entity: the evolution curve of the state parameters corresponding to the ecological entity in the process of the change of the state of the ecosystem.

[0224] In this embodiment, the ecological constraint rule base is a database that stores preset basic ecological rules. These rules are formulated based on ecological principles and are used to judge the rationality of ecological interaction models. For example, "the population size cannot be negative", "energy conservation (the energy obtained by the predator cannot exceed the energy provided by the prey)", and "the environmental tolerance parameters of organisms cannot exceed physiological limits", etc., are the basis for conflict detection.

[0225] Ecological inconsistency refers to situations where, after user editing, the ecological interaction model violates ecological constraints. For example, after deleting all predators of a certain plant, the population of that plant grows to exceed the carrying capacity of the environment, or the modified competition coefficient causes the population of a certain plant to become negative.

[0226] Ecological stability indicators are quantitative indicators that measure the stability of an ecosystem. They include population size fluctuations, ecological balance, and material cycling efficiency. For example, a population size fluctuation of no more than ±20% can be considered stable and is the objective function of the optimization model.

[0227] Graph traversal algorithms are used to analyze the relationships between nodes and edges in an ecological network graph. These algorithms include depth-first search and breadth-first search. Their core function is to deduce all directly or indirectly affected ecological entities and their interactions from user-edited nodes or edges, generating impact propagation paths.

[0228] Dynamic visual coding refers to the process of changing the visual characteristics of nodes or connecting edges in an ecological relationship network diagram (such as color highlighting, flashing, and thickening borders) to highlight the entities and relationships affected by editing operations, helping users to intuitively identify the scope of the impact.

[0229] Spatial coordinate mapping establishes the association between node identifiers in the ecological relationship network diagram and the spatial coordinates of the corresponding ecological entity 3D model in the VR 3D scene. For example, mapping the "lawn grass" node identifier to the 3D model coordinates of the lawn area in the VR scene is the core link for realizing view navigation.

[0230] The method described in this embodiment ensures the rationality of the ecosystem model and clarifies its scope of influence after user editing operations through model consistency verification and adaptive update operations. It also enables linked navigation between the network graph and the VR scene. The detailed working mechanism of each step is as follows:

[0231] In step S31, after the user's editing command is converted into a model modification command, the ecological constraint rule library is first invoked to perform real-time conflict detection on the modified ecological interaction model. The conflict detection process involves traversing all ecological entities, interaction relationships, and parameters in the model and comparing them with each rule in the constraint rule library to determine if any rule violations exist. For example, it checks whether population size-related parameters are negative, whether the modified competition coefficient causes a population growth rate to exceed physiological limits, and whether the intensity of environmental factors exceeds the tolerance range of organisms. If a conflict is detected (such as an excessively large modified symbiotic benefit coefficient causing a plant population to grow beyond the environmental carrying capacity in a short period of time), the system provides feedback to the user through visual or auditory warning signals in the VR environment. Visual warnings can take the form of node flashing or pop-up red prompt boxes, while auditory warnings can use prompt sounds to clearly inform the user of the conflict type (such as "population size exceeds environmental carrying capacity").

[0232] In step S32, after the user confirms the repair, the simulation system presents the user with multiple conflict resolution options (such as "reduce the symbiotic benefit coefficient", "increase environmental limiting factors", and "reduce the initial population size") through a virtual control panel. Upon receiving the user's selection instruction, the simulation system constructs an optimization model: using ecological stability indicators (such as minimizing population fluctuations and maximizing ecological balance) as the objective function, and ecological constraint rules as constraints (such as population size ≤ environmental carrying capacity and parameter values ​​within physiological limits), it automatically calculates adjustment schemes for relevant parameters using optimization algorithms (such as genetic algorithms and linear programming). For example, it adjusts the excessively high symbiotic benefit coefficient from 0.8 to 0.3, or increases the environmental carrying capacity parameter value. After the adjustment is completed, the simulation system synchronously displays the adjustment details (such as the adjusted parameter name, original value, new value, and reason for adjustment) to the user through a virtual information panel, ensuring the user understands the details of the model modification.

[0233] In step S33, after conflict resolution, the simulation system analyzes the impact range of the editing operation using a graph traversal algorithm (such as breadth-first search). Specifically, starting with the node or edge edited by the user, it first identifies directly related entities and relationships (e.g., when editing a plant node, the directly related entities are its predators, competitors, and symbiotic partners; the directly related relationships are the corresponding predator-competitor-symbiotic relationships). Then, starting with the directly related entities as a new starting point, it identifies indirectly related entities and relationships (e.g., the predator's prey, the competitor's symbiotic partner), and so on, until all affected nodes and edges are traversed, generating impact propagation path data (e.g., "Edit plant A → Affect predator B → Affect prey C → Affect competitor D").

[0234] Subsequently, the simulation system dynamically visually encodes the affected nodes and connecting edges in the ecological relationship network diagram. For example, directly affected nodes are highlighted in red with thicker borders, while indirectly affected nodes are highlighted in yellow with thinner borders. Directly affected connecting edges are highlighted and flashed, while indirectly affected connecting edges flash normally, allowing users to intuitively distinguish between direct and indirect impacts. Simultaneously, the system establishes a spatial coordinate mapping relationship between each ecological entity identifier in the impact propagation path and the corresponding model in the VR 3D scene. For example, it associates the "Predator B" node identifier with the X, Y, and Z coordinates of the bird 3D model in the VR scene, providing data support for subsequent viewpoint navigation.

[0235] In step S34, when the user selects a highlighted node (such as the "Prey C" node) in the ecological relationship network diagram using the VR controller, the system automatically calculates the viewing angle adjustment parameters of the VR environment based on the preset spatial coordinate mapping relationship. These parameters include the target position of the viewpoint (the center coordinates of the 3D model of Prey C), the viewing angle (the angle that clearly displays Prey C and its surrounding environment), and the scaling ratio (the ratio that fully displays Prey C and its associated entities). Subsequently, the simulation system drives the VR device's rendering engine to smoothly transition the viewing angle from the current position to the target position, focusing on the 3D model of Prey C.

[0236] Simultaneously, the simulation system overlays and displays state parameter evolution curves around the 3D model of prey C. The horizontal axis of the curve represents simulation time, and the vertical axis represents state parameter values ​​(such as population size and biomass). The curve's trend visually demonstrates the state changes of prey C caused by editing operations (e.g., after editing plant A, the population fluctuation of prey C due to changes in the number of predators B). Users can zoom and drag the curve using a VR controller to view parameter values ​​at specific time points, gaining a deeper understanding of the details of ecological changes.

[0237] The above scheme uses an ecological constraint rule base for real-time conflict detection, which can promptly detect unreasonable settings that may exist in the user's editing operations (such as parameters exceeding physiological limits or relationships violating ecological laws), and avoid the distortion of simulation results due to model errors; the automatic repair function adjusts parameters through optimization algorithms to restore the model to a stable equilibrium state, reducing the risk of simulation failure due to user operation errors or lack of knowledge.

[0238] By incorporating automatic conflict detection, repair, and dynamic navigation functions, the system reduces the complex manual operations required by users (such as manually finding the cause of the conflict and manually adjusting the viewpoint). Even users lacking in-depth ecological knowledge (such as beginners in landscape design) can easily use the system to conduct ecological simulations and experiments, thus broadening the scope of the system's applicable users.

[0239] In some embodiments, step S6 further includes the following step:

[0240] A time series analysis is performed on the trajectory data of the state parameters of each ecological entity output by the ecological dynamic simulation calculation over time. The time points when the rate of change of the state parameters exceeds the first preset threshold or the system stability index is lower than the second preset threshold are identified as key time points. Based on the intensity data of each interaction relationship in the ecological interaction model, the interaction relationship with the greatest intensity at the key time point is determined as the dominant interaction relationship.

[0241] The perturbation analysis algorithm is used to quantify the causal contribution of different editing operations to the final ecosystem state, specifically including:

[0242] Let the set of edit operations be E = {e1, e2, ..., e}. n After performing all editing operations, the final state vector of the ecosystem is S. final The baseline state vector that has not undergone any editing operations is S. base Each editing operation e is simulated and calculated by controlling variables. i The state change vector ΔS triggered by a single entity i Then its contribution C i Calculated according to the following formula:

[0243] ;

[0244] Where ||·|| represents the norm of the vector. This represents the sum of the norms of the state change vectors triggered individually by all editing operations;

[0245] Based on contribution level C i Editing operations are sorted, and those with a contribution exceeding a preset contribution threshold are identified as primary causal factors.

[0246] The system automatically integrates the editing instructions, the coefficients of the mathematical functions defined in the ecological interaction model, the key time points and their corresponding dominant interaction relationships, and the causal contribution ranking results and the identified main causal factors to generate a structured virtual experiment report.

[0247] In this embodiment, time series analysis is an analysis method for the trajectory data of the evolution of ecological entity state parameters over time. By extracting the change characteristics of the data (such as rate of change, fluctuation amplitude, extreme points), the key laws of ecosystem evolution can be identified.

[0248] Critical time points refer to the time points when the state of an ecosystem undergoes significant changes. Specifically, they are defined as the time points when the rate of change of state parameters exceeds the first preset threshold (e.g., the absolute value of the rate of change ≥ 50% / time step) or the system stability index falls below the second preset threshold (e.g., the stability index ≤ 0.3, where the stability index ranges from 0 to 1, and the closer it is to 1, the more stable it is).

[0249] Dominant interactions refer to the ecological interactions with the greatest intensity at critical time points, and these interactions are the core driving factors leading to ecological changes at those critical time points.

[0250] Perturbation analysis algorithms are algorithms that quantify the impact of editing operations on the state of an ecosystem. The core principle is to separate the independent impact of each editing operation by using the control variable method and calculate its contribution to the final state. It is a key tool for causal relationship analysis.

[0251] Causal contribution is a quantitative indicator that measures the impact of a single editing operation on the final state of the ecosystem. It ranges from 0 to 1, with a larger value indicating a more significant impact. It serves as a basis for identifying major causal factors.

[0252] The virtual experiment report is a structured report automatically generated by the simulation system. It integrates information such as the experimental process (editing instructions), experimental parameters (mathematical function coefficients), key experimental findings (key time points, dominant interaction relationships), and experimental conclusions (ranking of causal contributions, main causal factors), providing users with complete experimental records and analysis results.

[0253] The above scheme uses time series analysis and disturbance analysis to uncover the key mechanisms and main influencing factors of ecological change, providing users with scientific experimental analysis results. The detailed working mechanism of each step is as follows:

[0254] First, the simulation system performs time-series analysis on the state change trajectory data output in step S5. For the state parameters of each ecological entity (such as population size, biomass, and environmental parameter values), the rate of change at each time step is calculated (e.g., ΔN / Δt, where ΔN is the parameter difference between the current time step and the previous time step); at the same time, the system stability index is calculated (e.g., a weighted average of the fluctuations in the population size of all species; the smaller the fluctuation, the higher the stability index).

[0255] Subsequently, the rate of change of the state parameters at each time step is compared with a first preset threshold (the first preset threshold can be user-defined or system default, such as the default first preset threshold being 50% / time step), and the system stability index is compared with a second preset threshold (such as the default second preset threshold being 0.3). If either the "rate of change exceeds the first preset threshold" or the "stability index is lower than the second preset threshold" condition is met, then the time point corresponding to that time step is determined as a critical time point. For example, if the rate of change of the plant population is 60% / week within a certain time step (exceeding the first preset threshold of 50% / week), then that time point is a critical time point; or if the system stability index is 0.2 within a certain time step (lower than the second preset threshold of 0.3), then that time point is also a critical time point.

[0256] Next, for each key time point, the intensity data of all interactions in the ecological interaction model (such as the intensity values ​​corresponding to competition coefficients, symbiotic benefit coefficients, and attack rate coefficients) are extracted. By comparing the magnitudes of these intensity values, the interaction with the highest intensity is identified as the dominant interaction. For example, at a key time point, the intensity value of the competition relationship between plant A and plant B is 0.9, the intensity value of the predation relationship between plant A and insect C is 0.6, and the intensity value of the interaction between soil and environmental factors of plant A is 0.4. Therefore, the dominant interaction is the competition relationship between plant A and plant B, which is the core driving factor leading to ecological changes at this key time point.

[0257] Then, a perturbation analysis algorithm was used to quantify the impact of different editing operations on the final ecosystem state. The specific process is as follows:

[0258] First, define the set of edit operations E = {e1, e2, ..., e}. n In this context, e1, e2, etc., represent individual editing operations performed by the user (e.g., e1 = "Adjust the growth rate of plant A", e2 = "Delete insect C"). After all editing operations are performed, the simulation system obtains the final state vector S of the ecosystem through dynamic simulation. final (Includes the final state parameter values ​​of all ecological entities); Without any editing operations, the system's baseline state vector is S. base (Final state parameter values ​​obtained from the initial ecological model simulation).

[0259] Subsequently, simulation calculations were performed using the controlled variable method: for each editing operation e i Do not perform other editing operations, only execute e. i The corresponding ecosystem state vector S is obtained through simulation. i ; calculate e i The state change vector ΔSi=S triggered by a single entity i -S base (ΔS) i Each element represents the change in the corresponding state parameter.

[0260] Next, calculate e for each editing operation. i Causal contribution C i Causal contribution C i The value range of C is 0-1. i The larger the value, the more likely e is to be used. i For the final state S final The more significant the effect, the better. For example, ΔS of e1 i ΔS with norm of 10 and e2 i If the norm is 5 and Σ||ΔSi||=15, then the C of e1 i=10 / 15≈0.67, and Ci of e2=5 / 15≈0.33, indicating that the influence of e1 on the final state is greater than that of e2.

[0261] Finally, according to C i Sort all editing operations in descending order of size, set a preset contribution threshold (e.g., 0.3), and then set C... i Editing operations exceeding this threshold are identified as primary causal factors, i.e., operations that have a decisive impact on the final state of the ecosystem.

[0262] The simulation system automatically integrates various data related to the experiment to generate a structured virtual experiment report. The core content of the report includes: ① Editing instruction details, such as operation type, operation object, parameter values ​​before modification, and parameter values ​​after modification; ② Ecological interaction model parameters, such as the type of mathematical function and the values ​​of various coefficients; ③ Key time point analysis results, such as the specific time of each key time point, the corresponding state change characteristics, dominant interaction relationships, and their intensity; ④ Causal contribution analysis results, such as the contribution ranking of all editing operations, the identified main causal factors, and their corresponding contribution values; ⑤ Summary of ecosystem state changes, such as the difference between the final state and the baseline state, and key change trends. The report can be displayed through a virtual information panel in the VR environment. Users can use a VR controller to flip through pages, zoom in, and download the report for convenient subsequent analysis and sharing.

[0263] The aforementioned solution enables users to quickly pinpoint moments of significant ecosystem change by identifying key time points. Analysis of dominant interactions reveals the core driving factors of these key changes, addressing the problem of traditional simulation tools that "only know the outcome of the change, not the cause," thus helping users delve deeper into the essence of the phenomenon and understand the inherent laws of the ecosystem. By quantifying the causal contribution of each editing operation using the controlled variable method, the solution accurately identifies the main causal factors that determine the final state, avoiding misjudgments of causal relationships caused by subjective judgment. This is of great significance for scenarios such as ecological experiments and landscape design optimization, allowing users to focus on adjusting key operations based on the contribution results, improving experimental efficiency and design quality.

[0264] In some embodiments, the method further includes:

[0265] Construct a multi-scale observation model, which includes at least a macro-ecosystem scale observation model, a meso-biological community scale observation model, and a micro-biological functional unit scale observation model, and establish a mapping relationship of state parameters for the ecological entities in the digital twin model between different scale observation models;

[0266] In the virtual reality environment, in response to the user's scale switching command for the target ecological entity, based on the mapping relationship of state parameters established between different scale observation models, the observation perspective is smoothly transitioned from the current scale to the target scale, and the focus is automatically placed on the corresponding representation of the target ecological entity in the target scale observation model.

[0267] When the observation perspective is at the scale of the microscopic biological functional unit, the following sub-steps are performed:

[0268] From the ecological dynamic simulation calculation performed in step S5, extract the parameter values ​​corresponding to the environmental factor action types defined by the ecological interaction model established in step S2 that act on the currently focused biological functional unit, and use them as environmental factor parameters; at the same time, extract at least one state variable value of the biological functional unit in the ecological dynamic simulation calculation, and use it as biological state parameters; input the environmental factor parameters and biological state parameters into a preset physiological response function, and calculate one or more microscopic physiological process rate values ​​in real time.

[0269] Depending on the type of microscopic physiological process, the corresponding rendering engine is invoked, and the calculated rate value of the microscopic physiological process or the process state variable calculated from the rate value is used for dynamic visualization:

[0270] For mass transport processes, the microscopic physiological process rate values ​​are used to drive the particle system, simulating the movement path and flux of matter.

[0271] For surface state and energy conversion processes, the rate value of the microscopic physiological process or the process state variable is used as an input parameter to drive the dynamic shader to change the surface visual properties of the three-dimensional model of the biological functional unit in real time.

[0272] In this embodiment, the multi-scale observation model is an ecological observation system covering three dimensions: macro, meso, and micro. Each scale model focuses on different levels of ecological characteristics and achieves linkage between scales through state parameter mapping relationships. The core is to meet users' all-round observation needs from the whole to the part and from the macro to the micro.

[0273] The macro-ecosystem-scale observation model takes the entire campus ecosystem as the observation object, focuses on the overall characteristics of the ecosystem, and uses state parameters such as ecosystem stability, material cycling efficiency, and population structure balance to present the overall evolutionary trend of the ecosystem.

[0274] The meso-scale biological community observation model takes the biological communities (such as tree communities, shrub communities, aquatic biological communities, etc.) on campus as the observation objects, and the state parameters include community species richness, dominant species ratio, community coverage, etc., to show the structure and dynamics of community hierarchy.

[0275] Microscopic biological functional unit scale observation model takes the functional units of an organism (such as the leaves and roots of a plant, the organs of an animal, and the cells of a microorganism) as the object of observation, and the state parameters include cell metabolic rate, material transport flux, energy conversion efficiency, etc., to present the details of microscopic physiological processes.

[0276] State parameter mapping relationships are rules for establishing the association of state parameters between observation models at different scales. For example, they can associate leaf photosynthetic rate (micro-parameter) at the micro-scale with plant biomass growth (meso-parameter) at the meso-scale and ecosystem productivity (macro-parameter) at the macro-scale, so as to achieve mutual derivation and synchronous updating of parameters between scales.

[0277] Physiological response functions are mathematical functions that describe the relationship between the rate of microscopic physiological processes and environmental factor parameters and biological state parameters of biological functional units under specific environmental conditions. For example, the photosynthetic rate function (which calculates the photosynthetic rate based on environmental parameters such as light intensity and CO2 concentration) is essentially a quantification of the dynamic changes in microscopic physiological processes.

[0278] Dynamic shaders are a core component of VR rendering engines. They can adjust the surface visual properties (such as color, gloss, and transparency) of 3D models in real time based on input parameters (such as the rate values ​​of microscopic physiological processes), thereby enabling the visualization of microscopic physiological processes.

[0279] The above scheme achieves full-scale observation from macro-ecosystems to micro-functional units by constructing a multi-scale observation model and establishing inter-scale parameter mapping, while dynamically visualizing micro-physiological processes. The detailed working mechanism of each step is as follows:

[0280] First, observation models at three scales—macro, meso, and micro—are constructed, as follows:

[0281] When constructing the macro model, based on the overall data of the campus ecosystem (such as total species, population distribution range, and total environmental resources), macro state parameters such as ecosystem stability and material cycling efficiency are defined, and the relationship between macro parameters and meso parameters is established (such as the sum of community richness determining the macro parameter of species richness).

[0282] When constructing the meso-level model, for each biological community on campus, meso-level parameters such as species richness and the proportion of dominant species are defined, and a two-way mapping between meso-level parameters and macro- and micro-level parameters is established. The set of meso-level parameters forms the basis of macro-level parameters, and meso-level parameters are generated by the aggregation of micro-level parameters (such as the biomass of a plant community is obtained by aggregating the cumulative amount of photosynthetic products of the leaves of all individual plants in the community).

[0283] When constructing micro-models, for biological functional units (such as plant leaves and roots), micro-parameters such as metabolic rate and material transport flux are defined, and the relationship between micro-parameters and meso-parameters is established (such as the sum of photosynthetic rates of leaves determining the biomass growth of individual plants, which in turn affects the proportion of dominant species in the community).

[0284] Subsequently, for each ecological entity in the digital twin model, a mapping relationship of state parameters is established between the three scale models. For example, for the ecological entity "campus tree community":

[0285] The microscale includes: the photosynthetic rate of leaves (micro-parameter) → the rate of biomass growth of individual trees mapped to the mesoscale (meso-parameter).

[0286] The mesoscale includes: the sum of the growth rates of individual tree biomass → mapped to the macroscale tree community productivity (macro parameter);

[0287] Macroscale includes: tree community productivity → which is inversely correlated with changes in community cover at the mesoscale (mesoscale parameter), and further correlated with adjustments in leaf growth rate at the microscale (microscale parameter).

[0288] In the VR environment, users issue scale switching commands through the VR controller (such as the voice command "switch to microscale" or the button command to select the target scale). After receiving the scale switching command, the simulation system first identifies the current observation scale and the target scale, and then extracts the corresponding characterization data of the ecological entity at the target scale according to the preset state parameter mapping relationship (such as currently focusing on "trees", when switching to the microscale, extracting the three-dimensional model data of the leaves, photosynthetic rate and other micro parameters).

[0289] Subsequently, the system drives the VR rendering engine to achieve a smooth transition in viewing perspective: from the current scale's viewpoint (such as a macroscopic global overview view), it gradually zooms and pans to the target scale's focal point (such as a microscopic view of a blade's surface), avoiding dizziness caused by sudden changes in perspective. At the same time, it automatically hides irrelevant data at the current scale and loads detailed models and parameters for the target scale, ensuring that users can clearly observe the characteristics of ecological entities at the target scale.

[0290] When the observation perspective shifts to the scale of microscopic biological functional units, the simulation system executes the following sub-steps:

[0291] Data extraction: From the dynamic simulation calculation results in step S5, extract the environmental factor parameters (such as light intensity, CO2 concentration, and soil moisture content corresponding to the leaves) that act on the current focused biological functional unit, as well as the biological state parameters of the functional unit (such as chlorophyll content and cell activity of the leaves).

[0292] Physiological response calculation: The extracted environmental factor parameters and biological state parameters are input into a preset physiological response function (such as the photosynthetic rate function P). n = ×P AR ×C hl - ,in P is the photosynthetic efficiency coefficient. AR C represents light intensity. hl Chlorophyll content, (This is the respiratory consumption coefficient), which is used to calculate the rate values ​​of microscopic physiological processes (such as photosynthetic rate, respiratory rate, and water transport rate) in real time.

[0293] Dynamic visualization rendering: Based on the type of microscopic physiological process, the corresponding rendering engine component is invoked.

[0294] Material transport processes (such as water transport from roots to leaves, and nutrient diffusion between cells): The particle system is driven by microscopic physiological process rate values, and the dynamics of material transport are visually displayed through particle movement paths (such as blue particles simulating water flow) and particle density (density is positively correlated with transport flux).

[0295] Surface state and energy conversion processes (such as energy absorption during leaf photosynthesis and energy release during cellular respiration): Physiological process rate values ​​or process state variables (such as the amount of energy conversion corresponding to the photosynthetic rate) are input into the dynamic shader to adjust the surface visual attributes of the 3D model of the biological functional unit in real time. For example, the higher the photosynthetic rate, the brighter the color of the leaf model; the higher the respiration rate, the stronger the gloss of the model surface, quantifying the intensity of microscopic physiological processes through visual changes.

[0296] The above scheme significantly expands the system's observation dimensions and detail rendering capabilities by adding multi-scale observation and microscopic visualization functions. Its beneficial effects are as follows:

[0297] First, it achieves full-scale coverage observation of the ecosystem. The multi-scale observation model covers three dimensions: macro, meso, and micro. Users can freely switch perspectives according to their needs, which can not only grasp the overall evolutionary trend of the ecosystem, but also explore changes in community structure in depth, and observe the details of microscopic physiological processes. This solves the problem that traditional simulation tools can only focus on a single scale and have limited observation dimensions.

[0298] Second, an inter-scale linkage mechanism was established, enhancing the overall integrity of the simulation. Through state parameter mapping relationships, observational data at different scales are interconnected and updated synchronously. For example, changes in microscopic physiological processes can affect the state of mesoscopic communities through parameter mapping, thereby influencing macroscopic ecosystem characteristics. This makes the simulation results more closely resemble the hierarchical relationship patterns of real ecosystems, enhancing the scientific rigor and completeness of the simulation.

[0299] Third, visualizing microscopic physiological processes lowers the barrier to understanding microscopic mechanisms. Through rendering techniques such as particle systems and dynamic shaders, abstract microscopic physiological processes (such as photosynthesis, respiration, and mass transport) are transformed into intuitive visual effects. Users do not need professional knowledge of microbiology to understand the mechanisms and effects of microscopic processes by observing dynamic changes in VR scenes, which is especially suitable for scenarios such as biology teaching and plant research.

[0300] In some embodiments, the method further includes the following steps:

[0301] At the beginning of each simulation time step in step S5, the environmental factor parameters currently acting on the macroscopic ecological entity to which the current focused biological functional unit belongs are obtained, and the environmental factor parameters are used as input parameters of the physiological response function to drive the physiological response function to calculate the current time step.

[0302] Based on the calculation results of the physiological response function, the microscopic physiological process rate value of the currently focused biological functional unit is obtained; then, according to the spatial representation relationship between the biological functional unit and its corresponding macroscopic ecological entity, the microscopic physiological process rate values ​​of all related biological functional units are aggregated, and the net influence ΔB(t) on the core biological state parameters of the macroscopic ecological entity within the current time step is calculated. The formula for calculating the net influence ΔB(t) is as follows:

[0303] ΔB(t)=Σ[R k(t) ×w k ]×Δt;

[0304] Among them, R k(t) w represents the rate of the microphysiological process of the k-th related biological functional unit at the current time step. k Δt is the aggregation weight coefficient of the k-th biological functional unit, and Δt is the simulation time step.

[0305] The net impact ΔB(t) is used as a correction term and fed back to the ecological interaction model to update the instantaneous rate of change equation of the biological state parameter B of the macroscopic ecological entity in the current time step calculation. The update method of the instantaneous rate of change equation of the biological state parameter B is as follows:

[0306] ;

[0307] in, This is the basic rate of change calculated solely based on the inter-organism interactions in the aforementioned ecological interaction model;

[0308] The calculation of the current time step is completed based on the instantaneous rate of change equation of the updated biological state parameter B, and then the iteration of the next time step begins.

[0309] In this embodiment, the net impact ΔB(t) is the comprehensive impact of the microscopic physiological processes of all relevant biological functional units on the core biological state parameters of the macroscopic ecological entity within a certain time step. It reflects the cumulative effect of microscopic processes on macroscopic state and is a key quantitative indicator connecting the microscopic and macroscopic.

[0310] Aggregate weighting coefficient w k It is a weighted value that measures the degree of influence of each biological functional unit on the macro-ecological entity. It is determined based on factors such as the importance of the functional unit (e.g., the weight of photosynthetic functional units in plant leaves is higher than that of non-photosynthetic functional units), the proportion of the number, and the spatial distribution, to ensure the rationality of the calculation of net impact.

[0311] The instantaneous rate of change equation is a mathematical equation that describes the real-time changes in the state parameters of macroscopic ecological entities. By integrating the basic rate of change of inter-organism interactions with the net influence correction term of microscopic physiological processes, it achieves precise quantification of macroscopic state changes.

[0312] The basic rate of change is the rate of change of macroscopic ecological entity biological state parameters when only considering the inter-organism interactions (such as competition, symbiosis, and predation) in the ecological interaction model, without including the influence of microscopic physiological processes.

[0313] This embodiment calculates the net impact of microscopic physiological processes and corrects the state change rate equation of macroscopic ecological entities to achieve dynamic linkage between the microscopic and macroscopic levels, ensuring the consistency and accuracy of the simulation. The detailed working mechanism of each step is as follows:

[0314] Before each simulation time step Δt in step S5 begins, the simulation system first acquires the environmental factor parameters currently acting on the "macro-ecological entity to which the currently focused biological functional unit belongs". For example, if the current focus is on camphor tree leaves (micro-functional unit), and the macro-ecological entity to which it belongs is an individual camphor tree, then the environmental factor parameters such as light intensity, CO2 concentration, and soil moisture in the area where the individual camphor tree is located are acquired.

[0315] These environmental factor parameters are input into a preset physiological response function, and combined with the biological state parameters of the biological functional unit at the current time step (such as leaf chlorophyll content and cell activity), the rate value R of the microscopic physiological process of the biological functional unit at the current time step is calculated in real time. k(t) (such as photosynthetic rate, respiration rate).

[0316] The simulation system identifies all biological functional units related to the macroscopic ecological entity (such as all functional units of a camphor tree, including leaves, roots, and stems), and obtains the microscopic physiological process rate value R for each functional unit. k(t) and its corresponding aggregation weight coefficient wk (such as w of the photosynthetic functional unit of a leaf). k =0.6, w of the root absorption functional unit k =0.3, w of the stem transport functional unit k =0.1).

[0317] According to the formula ΔB(t)=Σ[R k(t) ×w k ]×Δt Calculation of net impact: First, calculate the weighted physiological rate (R) for each functional unit. k(t) ×w k Then, summing all weighted physiological rates yields the comprehensive impact intensity per unit time, which is then multiplied by the time step Δt to obtain the net impact of microscopic physiological processes on the core biological state parameters of macroscopic ecological entities within that time step. For example, at a time step Δt = 1 day, the R of three related functional units... k(t) They are 10, 8, and 5 respectively, w k If the values ​​are 0.6, 0.3, and 0.1 respectively, then Σ[R] k(t) ×w k =10×0.6+8×0.3+5×0.1=6+2.4+0.5=8.9, ΔB(t)= 8.9×1=8.9, that is, the net influence of the microscopic physiological process on the macroscopic state parameter within this time step is 8.9.

[0318] The calculated net impact ΔB(t) is then used as a correction term and fed back into the ecological interaction model to update the instantaneous rate of change equation of the biological state parameter B of the macroscopic ecological entity. The original rate of change equation before the update is: (Considering only inter-organism interactions), the updated equation is: ,in The instantaneous rate of change corresponding to the net impact is superimposed on the baseline rate of change to obtain a complete rate of change equation that includes the influence of microscopic physiological processes.

[0319] Based on the updated instantaneous rate of change equation, dynamic simulation calculations are performed at the current time step to obtain the biological state parameter values ​​of the macroscopic ecological entity after this time step. Then, the process is repeated at the next time step to achieve dynamic linkage and iteration between microscopic physiological processes and macroscopic state changes.

[0320] The above scheme further improves the accuracy and consistency of the simulation by establishing a feedback mechanism between microscopic physiological processes and macroscopic states. Its beneficial effects are as follows:

[0321] First, changes in microscopic physiological processes are fed back to the macroscopic state change rate equation through net impact, so that the simulation of the macroscopic state not only considers the interaction between organisms, but also incorporates the influence of microscopic physiological mechanisms. For example, the increase in photosynthetic efficiency of plants will directly promote the growth of macroscopic biomass, which is consistent with the hierarchical relationship of "microscopic physiology-individual growth-macroscopic ecology" in real ecosystems, and avoids the problem of the disconnect between microscopic and macroscopic in traditional simulations.

[0322] Second, the calculation of net impact is based on the specific rates and weights of micro-physiological processes, which can quantify the specific contribution of micro-processes to macro-state. For example, by accurately calculating the impact of leaf photosynthetic rate on biomass, the growth rate of macro-biomass can be corrected, making the simulation results closer to the growth and development patterns of real organisms, and providing more reliable data support for landscape design and ecological assessment.

[0323] Third, by adjusting the parameters of the microphysiological response function and the aggregation weight coefficient of the biological functional units, the simulation effect of the macroscopic state can be precisely optimized. For example, for different plant species, the weight of the photosynthetic functional unit of the leaf can be adjusted to make it more consistent with the physiological characteristics of the species, thus expanding the system's adaptability to different ecological scenarios.

[0324] In some embodiments, such as Figure 4 As shown, the method further includes:

[0325] S7: In the virtual reality environment, user interaction behavior data is collected in real time through a data acquisition interface. The interaction behavior data includes:

[0326] A sequence of operations to execute editing instructions on nodes or connecting edges in the ecological relationship network graph;

[0327] The duration of time spent in front of the interface displaying the ecological relationship network diagram generated in step S3 or the dynamic visualization of the ecosystem state change process displayed in step S6.

[0328] The viewing focus frequency of a specific type of ecological entity or the cluster area corresponding to that ecological entity in the digital twin model;

[0329] S8: Process the collected interaction behavior data based on the preset user classification model to generate a user profile, wherein the user profile includes at least two of the following: user identity type, ecological knowledge level, and learning preferences.

[0330] S9: Based on the user profile and the preset teaching knowledge graph, a personalized VR campus ecology tour route is generated through a path planning algorithm, wherein the preset teaching knowledge graph is associated with the digital twin model;

[0331] The path planning algorithm optimizes the spatial paths connecting the various ecological entities in the digital twin model based on the following constraints:

[0332] Maximize the number of ecological knowledge points covered by the path that are related to the relationship types defined in the ecological interaction model;

[0333] Minimize the sum of the total spatial length of the path and the estimated learning time required to complete all interactions on the path;

[0334] The matching degree between the types of ecological entities traversed by the path and the types of interactions associated with them and the learning preferences in the user profile is maximized.

[0335] The difference in difficulty level values ​​between adjacent teaching nodes in the path does not exceed a preset first threshold, and the sequence of difficulty level values ​​of the entire path satisfies monotonically non-decreasing or wave-shaped gradual change, wherein the teaching node is a key ecological entity or ecological entity cluster area in the digital twin model.

[0336] S10: During VR tours, dynamic navigation guidance is provided through spatial path rendering technology based on the user's real-time location within the virtual space of the digital twin model.

[0337] like Figure 5 As shown, the dynamic navigation guidance includes:

[0338] S101: Based on the spatial relationship between the user's current location and the planned route, generate and overlay navigation instructions in real time onto the virtual reality scene;

[0339] S102: Based on the user's travel speed and direction, predict and preload the three-dimensional model data of the ecological entities associated with the next navigation point and their relationship data in the ecological interaction model;

[0340] S103: When it is detected that the distance between the user's current location and the planned route exceeds a preset threshold, the path replanning algorithm is triggered to recalculate the guiding path.

[0341] In this embodiment, user interaction behavior data is a record of user operations and behaviors in the VR environment captured in real time by the simulation system through the data acquisition interface. It covers editing operations, dwell time, and viewing focus frequency, and is the core data foundation for building user profiles and generating personalized tour routes.

[0342] User profiles are sets of user characteristics obtained by analyzing user interaction behavior data through user classification models. The core features include user identity type (such as student, teacher, designer, researcher), ecological knowledge level (such as beginner, intermediate, advanced), and learning preferences (such as preference for plant ecology, preference for animal ecology, preference for microphysiology), etc., which are used to accurately match user needs.

[0343] The teaching knowledge graph is a structured knowledge system associated with the digital twin model. It includes ecological knowledge points (such as the principle of competition and the physiological mechanism of photosynthesis), the difficulty level of knowledge points, and the relationship between knowledge points and ecological entities (such as the relationship between the knowledge point of "competition" and plant entities). It provides the knowledge support for generating personalized tour routes.

[0344] Personalized VR campus ecosystem tour routes are customized tour routes generated by path planning algorithms based on user profiles and teaching knowledge graphs. The core is to maximize the user's learning effect and experience while meeting constraints.

[0345] Path planning algorithms are mathematical algorithms used to optimize tour routes. Based on preset constraints (knowledge point coverage, path length and time, learning preference matching, and difficulty gradient), they calculate the spatial paths of each ecological entity in the digital twin model and generate the optimal tour order.

[0346] Teaching nodes are key ecological entities or areas of ecological entity aggregation in a digital twin model that carry teaching knowledge points (such as "tree competition area", "aquatic organism symbiosis area", "leaf microscopic observation point"). Each teaching node corresponds to a specific ecological knowledge point and difficulty level.

[0347] This embodiment generates user profiles by analyzing user interaction behavior and plans personalized tour routes by combining them with teaching knowledge graphs, thereby improving the user's learning experience and knowledge acquisition efficiency. The detailed working mechanism of each step is as follows:

[0348] The simulation system captures three core user interaction behaviors in the VR environment in real time through a preset data acquisition interface, as follows:

[0349] Editing operation data: The sequence of editing operations performed by users on nodes or connecting edges in the ecological relationship network graph, including operation type (delete, modify, add), operation object (such as plant entities, competitive relationship connecting edges), and operation frequency (such as frequently modifying the parameters of a certain type of plant), which is used to determine the user's focus and knowledge needs.

[0350] Dwell time data: The time users spend on the ecological relationship network diagram interface and the dynamic visualization interface of ecosystem state changes. For example, if a user spends 10 minutes on the visualization interface of microphysiological processes, it indicates that they have a strong interest in microphysiological knowledge; if they spend 5 minutes on the competition relationship network diagram interface, it indicates that they are interested in knowledge related to plant competition.

[0351] Perspective Focus Data: The frequency with which users focus their perspective on specific types of ecological entities or clusters in the digital twin model. For example, if a user focuses their perspective on aquatic biological entities multiple times, it indicates that their learning preference is for aquatic ecology; if they frequently focus on microbial community clusters, it indicates that they are interested in micro-ecology.

[0352] The collected interaction behavior data is then input into a pre-defined user classification model (this model is trained based on machine learning algorithms, such as decision tree models and neural network models). Through feature extraction and classification analysis, a user profile is generated. The user profile mainly includes the following information:

[0353] User identity type identification: Based on the complexity of operation and knowledge focus, for example, users who frequently make fine parameter adjustments and focus on scientific research data are identified as "researchers"; users who operate simply and focus on basic knowledge points are identified as "university students"; users who focus on scheme design and ecological layout are identified as "landscape designers".

[0354] Ecological knowledge level assessment: Based on the professionalism of the editing operation, the distribution of the dwell time, and the depth of the perspective, users who can accurately modify the competition coefficient and focus on micro-physiological processes are judged as having "advanced knowledge level"; users who only delete simple nodes and focus on macro-ecology are judged as having "basic knowledge level".

[0355] Learning preference analysis: Based on statistical analysis of the operation object, dwell time, and focus frequency, for example, users who frequently operate on plant entities, spend a long time in the plant ecology interface, and focus on plant community areas are judged to have a learning preference of "plant ecology"; users who pay attention to the editing of predator-prey relationships and focus on animal entities are judged to have a learning preference of "animal ecology".

[0356] Then, based on user profiles and educational knowledge graphs, a personalized tour route is generated using a path planning algorithm. The constraints and optimization logic of the algorithm are as follows:

[0357] Constraint 1: Maximize knowledge point coverage. Ensure that the route covers the largest number of ecological knowledge points related to the ecological interaction model. For example, for users with a preference for "plant ecology", the route should prioritize covering knowledge points related to competition, symbiosis, and photosynthetic physiology.

[0358] Constraint 2: Minimize path and time. Optimize the sum of the total spatial length of the path (reducing unnecessary backtracking) and the expected learning time (average learning time per teaching node × number of nodes) to ensure that users complete the tour within a reasonable time and avoid fatigue.

[0359] Constraint 3: Maximize learning preference matching. The types of ecological entities traversed by the path and the types of interactions associated with them are highly matched with the learning preferences in the user profile. For example, for users with a preference for "microphysiology," the route includes more observation points of micro-functional units.

[0360] Constraint 4: Reasonable difficulty gradient. The difference in difficulty level between adjacent teaching nodes does not exceed a preset threshold (e.g., the difference in level ≤ 1, the difficulty level is divided into 1-5 levels), and the difficulty of the entire path changes monotonically and non-decreasingly (from basic to advanced) or gradually in a wave-like manner (basic-advanced-basic-advanced), avoiding sudden changes in difficulty that may cause difficulties in user understanding.

[0361] Based on the above constraints, the path planning algorithm optimizes and sorts the spatial connection paths of all teaching nodes in the digital twin model to generate the optimal tour order. For example, for student users with "basic knowledge level and plant ecology preference", the route is: macroscopic observation point of plant community (difficulty 1, knowledge point: plant community structure) → plant competition area (difficulty 2, knowledge point: competition relationship) → plant symbiosis area (difficulty 2, knowledge point: symbiotic relationship) → microscopic observation point of leaf photosynthesis (difficulty 3, knowledge point: photosynthetic physiology basis), ensuring that the difficulty gradient is reasonable, the preference is matched, and the knowledge points are fully covered.

[0362] During the VR tour, the simulation system provides three types of dynamic navigation guidance based on the user's real-time location and through spatial path rendering technology:

[0363] Real-time navigation guidance. The simulation system displays navigation indicators (such as arrows and highlighted paths) overlaid on the VR scene. Based on the spatial relationship between the user's current location and the planned route, the system dynamically adjusts the direction of the guidance. For example, if the user deviates from the route, the arrow automatically turns in the correct direction; when approaching a teaching node, the arrow flashes as a reminder.

[0364] Preloading optimization. Based on the user's walking speed and direction, the simulation system predicts the spatial area covered by the user's view frustum or walking path within a preset time period ΔT (e.g., 1 minute), and preloads the 3D models and relational data of ecological entities within that area to avoid lag and loading delays during the tour, thus improving smoothness.

[0365] Path replanning. When the system detects that a user has deviated from the planned route by more than a preset threshold (e.g., 5 meters), it triggers a path replanning algorithm. Based on the user's current location, remaining teaching nodes, and constraints, the system recalculates the optimal guidance path to ensure that the user can return to the planned route or find a better alternative route.

[0366] For example, when a second-year landscape architecture student (identity type: student, knowledge level: intermediate, learning preference: plant ecology and landscape design) uses the simulation system, the system collects interaction data with the student and finds that the student frequently edits plant entity parameters, spends a cumulative 15 minutes on the plant competition and symbiosis interface, and repeatedly focuses on the campus greening planning area. Based on this data, a user profile is generated, and the teaching knowledge graph matches relevant knowledge points such as "plant competition principles, symbiosis applications, greening layout optimization, and plant growth simulation." The path planning algorithm generates a tour route: Macro-area of ​​campus greening planning (difficulty 2, knowledge point: basics of greening layout) → Plant competition demonstration area (difficulty 3, knowledge point: avoiding competition in greening) → Plant symbiosis demonstration area (difficulty 3, knowledge point: application of symbiosis in greening) → Plant growth simulation observation point (difficulty 4, knowledge point: optimization of plant growth parameters) → Greening scheme editing area (difficulty 4, knowledge point: scheme adjustment based on ecological simulation). During the tour, the simulation system provides real-time guidance through highlighted paths, preloads 3D plant models and competitive relationship data for the next node, and automatically replans the path when students deviate from the route to observe actual birds due to curiosity. This guides students back to the original route while retaining "predation relationship between birds and plants" as an optional branch node, taking into account both user interests and core learning objectives.

[0367] The above solution generates user profiles based on user interaction behavior, which accurately reflect the user's identity, knowledge level and learning preferences. Personalized tour routes are tailored to match user needs. For example, the route for researchers focuses on in-depth data and micro-mechanisms, while the route for students focuses on basic knowledge points and intuitive experience. This solves the problem of the "one-size-fits-all" approach of traditional simulation tools and improves the usability for different user groups.

[0368] The path planning algorithm maximizes knowledge coverage and optimizes the difficulty gradient, ensuring that users acquire the most relevant knowledge within a reasonable time. The difficulty increases gradually, which is in line with cognitive laws. This avoids the waste of time and fragmentation of knowledge caused by users blindly exploring in the VR environment, and is especially suitable for teaching and training scenarios.

[0369] Dynamic navigation indicators help users clearly grasp the direction of travel, the pre-loading function avoids loading delays, and the path replanning function adapts to flexible user operations. These three elements work together to ensure a smooth tour process, reducing the impact of technical issues on immersion and allowing users to focus on knowledge learning and ecological exploration. Personalized routes and dynamic navigation enable the simulation system to adapt to users of different ages, knowledge backgrounds, and learning needs. Whether for university teaching, landscape designer training, or data exploration by researchers, it provides precise support, significantly enhancing the system's promotional value and application scenario coverage.

[0370] In some embodiments, step S102 includes:

[0371] Based on the user's location, direction of travel, and instantaneous velocity vector obtained through real-time positioning, and combined with the spatial orientation of the planned route, the spatial area to be covered by the user's view frustum or travel path within a preset time period ΔT is predicted using linear extrapolation or curve fitting algorithms; ecological entities located within the predicted spatial area and belonging to the digital twin model are identified as the potential access target set Z.

[0372] For each ecological entity E in the set of potential access targets Z i Perform ecological network importance assessment and user interest matching assessment;

[0373] The assessment of the importance of the ecological network includes: calculating the ecological entity E. i The normalized degree centrality and normalized betweenness centrality are calculated from the ecological relationship network graph, and the normalized degree centrality and normalized betweenness centrality are weighted and summed to obtain the ecological entity E. i The corresponding network importance score S net(i) ;

[0374] The user interest matching assessment includes: constructing ecological entity E i The attribute feature vector is obtained, which includes at least its ecology type encoding and the encoding of the associated dominant interaction type; the cosine similarity between this vector and the learning preference feature vector extracted from the current user profile is calculated to obtain the interest matching score S. pref(i) ;

[0375] Calculate ecological entity E using the following formula. i Comprehensive preloading priority

[0376] P riority（i） =w i· S net(i) +(1−w i )·S pref(i) ;

[0377] Among them, the fusion weight coefficient w i w is a variable that is dynamically adjusted based on the real-time available graphics memory usage of the computing device running the virtual reality environment. When the available graphics memory usage exceeds a first usage threshold, w is set. i The first value is used; when the available graphics memory usage is lower than the second threshold, w is set. i The second value, w i The value range is (0,1), and the first value is greater than the second value;

[0378] Based on the calculated comprehensive preloading priority, all ecological entities in the potential access target set Z are sorted in descending order, and the following hierarchical loading strategy is executed according to the sorting results:

[0379] For ecological entities ranked in the top R1% of priority, load the complete 3D model, first-resolution texture, real-time state parameters and direct relationship data in the ecological relationship network corresponding to the ecological entity.

[0380] For ecological entities ranked in the middle R2%, load the simplified 3D model, second-resolution texture, and key state parameters of the ecological entity.

[0381] For ecological entities ranked in the bottom 3% of priority, only the spatial location identifier and name label of the ecological entity are loaded.

[0382] The first resolution is higher than the second resolution. R1, R2, and R3 are preset percentage parameters, and R1+R2+R3=100.

[0383] In this embodiment, the view frustum is the geometric representation of the user's field of view in the VR environment. It forms a cone-shaped region with the user's viewpoint as the vertex, and only the scene content within this region is rendered and displayed. Its core function is to predict the spatial region that the user is about to observe.

[0384] The potential access target set Z is the set of ecological entities that a user may visit within a preset time period predicted by the simulation system. It is determined based on the user's travel trajectory and the coverage area of ​​the view frustum, and is the core object of preloading optimization.

[0385] Network Importance Score net(i) It is a quantitative indicator that measures the importance of an ecological entity in an ecological relationship network graph. It is obtained by weighted summation of normalized degree centrality (the proportion of nodes connected by edges) and normalized betweenness centrality (the proportion of nodes acting as intermediaries in paths), reflecting the coreness of an entity in the ecological network.

[0386] Interest Match Rating S pref(i)It is a quantitative indicator that measures the fit between ecological entities and user learning preferences. It is obtained by calculating the cosine similarity between the ecological entity attribute feature vector and the user learning preference feature vector. The higher the similarity, the higher the score.

[0387] The comprehensive preloading priority is a ranking criterion for ecological entities based on network importance score, interest matching score and fusion weight coefficient. The core is to balance system performance and user experience, and prioritize loading important content that matches user interests.

[0388] The tiered loading strategy employs different loading precision schemes for ecological entities in the potential access target set based on comprehensive preloading priority. Its core is to ensure the loading quality of key content and the smoothness of the overall browsing experience under limited computing resources.

[0389] The above solution improves browsing smoothness while ensuring system performance by accurately predicting user access goals, quantifying priorities, and adopting a tiered loading strategy. The detailed working mechanism of each step is as follows:

[0390] The simulation system, based on the user's real-time location data (X, Y, Z coordinates), travel direction vector (e.g., towards the northeast), and instantaneous velocity vector (e.g., 0.5 m / s), combined with the spatial orientation of the planned route, uses linear extrapolation or curve fitting algorithms to predict the user's trajectory within a preset time period ΔT (e.g., ΔT = 1 minute, adjustable according to device performance). Based on the predicted trajectory, the system determines the spatial area covered by the user's view frustum or travel path (e.g., if the user travels northeast for 1 minute, the view frustum covers the "tree competition area" and "shrub symbiosis area"). All ecological entities located within this area and belonging to the digital twin model are identified as the potential access target set Z (e.g., set Z = {tree A, tree B, shrub C, shrub D}).

[0391] For each ecological entity E in the potential access target set Z i The system performs an ecological network importance assessment and a user interest matching assessment. Then, based on the assessment results, it performs a weighted fusion calculation to obtain the comprehensive preloading priority of each ecological entity. The weights of the weighted fusion are dynamically adjusted according to the real-time available graphics memory usage of the computing device running the VR environment.

[0392] When the available graphics memory usage is higher than the first usage threshold (e.g., 80%), it indicates that the device performance is sufficient, and w is set. i The first value (e.g., 0.7) is used to prioritize the loading of ecological entities with high network importance (ensuring that core ecological entities do not experience lag).

[0393] When the available graphics memory usage falls below the second threshold (e.g., 50%), it indicates that the device is under performance strain. Set w iThe second value (e.g., 0.3) prioritizes loading ecological entities that users are interested in (to improve user experience).

[0394] w i The value range is (0,1), and the first value is greater than the second value.

[0395] Based on the overall preloading priority, the ecological entities in the potential access target set Z are sorted in descending order, and a hierarchical loading strategy is executed:

[0396] High priority (top R1%, e.g., R1=30%): Load the complete 3D model (high-precision mesh), first-resolution texture (e.g., 4K texture), real-time status parameters (e.g., current biomass, physiological rate), and direct relationship data (e.g., all related interactions) to ensure that the core and entities of interest are clearly displayed and the data is complete.

[0397] Medium priority (middle R2%, e.g., R2=50%): Load simplified 3D models (low-precision mesh, reduced face count), second-resolution textures (e.g., 2K textures) and key state parameters (e.g., core biomass, main interaction types) to balance performance and presentation.

[0398] Low priority (last R3%, e.g., R3=20%): Only loads spatial location identifiers (e.g., coordinate points) and name labels, without loading the complete model and detailed data. It is only used to inform the user of the ecological entities that exist in the area, saving memory resources.

[0399] R1, R2, and R3 are preset percentage parameters (R1+R2+R3=100), which can be adjusted according to actual needs; the first resolution is higher than the second resolution to ensure better visual effects for high-priority entities.

[0400] The above solution, by predicting user access goals and quantifying priorities, allocates limited computing resources preferentially to core and user-interested ecosystem entities, avoiding issues such as "lag due to full loading" or "poor experience due to oversimplification." It is particularly suitable for configuring different VR devices, enhancing system compatibility. (The fusion weight coefficient w) i The dynamic adjustment ensures the loading quality of core entities in the ecological network (without missing key ecological nodes) while prioritizing user learning preferences (improving the display effect of content of interest), making the preloading strategy both in line with the scientific nature of ecological simulation and meet the personalized needs of users.

[0401] A differentiated loading scheme with high, medium, and low priorities allocates resources of varying precision to ecosystem entities of different importance and levels of interest. This saves memory, ensures smooth gameplay, and maximizes the display quality of core content, resolving the inherent trade-off between high precision and high smoothness in VR ecosystem simulations through dynamic adjustment of resources.i With tiered loading, the system can adapt to VR devices with different performance levels (from high-end professional equipment to mid-range consumer-grade devices), avoiding excessive differences in experience caused by differences in device performance. At the same time, it reduces problems such as loading failures, lag, and crashes, and improves the system's operational stability.

[0402] In the second aspect, such as Figure 6 As shown, the present invention provides a virtual reality landscape simulation system 20 integrating dynamic navigation, the system being used to perform the method as described in the first aspect of the present invention, the system comprising:

[0403] The model building module 201 is used to build a digital twin model of the campus ecosystem. The digital twin model contains multiple ecological entities with quantified state parameters. The ecological entities include at least two of the following: individual plants, soil regions, water units, individual animals, and microbial communities.

[0404] Interaction modeling module 202 is used to establish an ecological interaction model based on the digital twin model. The ecological interaction model defines the interaction relationship between any two ecological entities through mathematical functions, wherein:

[0405] The interaction relationship has a preset relationship type, which includes inter-biological interaction type and environmental factor interaction type. The inter-biological interaction type includes any one of competition, symbiosis, predation, parasitism, and co-evolution. The environmental factor interaction type is used to characterize the influence of abiotic ecological entities on biotic ecological entities.

[0406] For each type of interaction relationship, the strength of the interaction is quantified by the corresponding mathematical function. The sign of the output value of the mathematical function indicates the direction of the interaction, with positive values ​​indicating a promoting effect and negative values ​​indicating an inhibiting effect.

[0407] The network graph generation module 203 is used to generate a visualized ecological relationship network graph based on the ecological interaction model, wherein:

[0408] Each ecological entity is represented as a node in the ecological relationship network graph;

[0409] Each pair of interacting ecological entities is represented by a connecting edge;

[0410] The visual feature mapping of connected edges corresponds to the relationship type and intensity of the interaction.

[0411] The interaction and instruction conversion module 204 is used to receive editing instructions from the user on the ecological relationship network graph through the virtual reality interaction device. The editing instructions include operations on nodes and connecting edges. The module converts the editing instructions into modification instructions for the ecological interaction model. The modification instructions include any one or more of the following: deleting the ecological entity corresponding to the node, modifying the state parameters of the ecological entity, and modifying the interaction relationship between the ecological entities.

[0412] The dynamic simulation calculation engine 205 is used to respond to the editing command and perform ecological dynamic simulation calculations based on the modified ecological interaction model to obtain the process of ecosystem state change caused by the editing operation.

[0413] The virtual reality rendering and display module 206 is used to generate and display dynamic visualization effects in a virtual reality environment based on the process of ecological system state change.

[0414] Finally, it should be noted that although the above embodiments have been described in the description and drawings of this invention, this should not limit the scope of patent protection of this invention. Any technical solutions that are based on the essential concept of this invention, utilize the content described in the description and drawings of this invention to make equivalent structural or procedural substitutions or modifications, as well as the direct or indirect application of the technical solutions of the above embodiments to other related technical fields, are all included within the scope of patent protection of this invention.

Claims

1. A virtual reality landscape simulation method integrating dynamic navigation, characterized in that, Includes the following steps: S1: Construct a digital twin model of the campus ecosystem. The digital twin model contains multiple ecological entities with quantified state parameters. The ecological entities include at least two of the following: individual plants, soil regions, water units, individual animals, and microbial communities. S2: Based on the digital twin model, an ecological interaction model is established. This model defines the interaction relationship between any two ecological entities using mathematical functions, where: The interaction relationship has a preset relationship type, which includes inter-biological interaction type and environmental factor interaction type. The inter-biological interaction type includes any one of competition, symbiosis, predation, parasitism, and co-evolution. The environmental factor interaction type is used to characterize the influence of abiotic ecological entities on biotic ecological entities. For each type of interaction relationship, the strength of the interaction is quantified by the corresponding mathematical function. The sign of the output value of the mathematical function indicates the direction of the interaction, with positive values ​​indicating a promoting effect and negative values ​​indicating an inhibiting effect. S3: Based on the aforementioned ecological interaction model, generate a visualized ecological relationship network diagram, wherein: Each ecological entity is represented as a node in the ecological relationship network graph; Each pair of interacting ecological entities is represented by a connecting edge; The visual feature mapping of connected edges corresponds to the relationship type and intensity of the interaction. S4: Receive editing instructions from the user on the ecological relationship network graph via a virtual reality interactive device. These editing instructions include operations on nodes and connecting edges. Convert the editing instructions into modification instructions for the ecological interaction model. These modification instructions include deleting the ecological entities corresponding to the nodes. Modify the state parameters of an ecological entity, or modify one or more of the interaction relationships between ecological entities; S5: In response to the editing instruction, based on the modified ecological interaction model, perform ecological dynamic simulation calculations to obtain the process of ecosystem state change caused by the editing operation; S6: In a virtual reality environment, based on the process of changes in the state of the ecosystem, generate and display dynamic visualization effects.

2. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 1, characterized in that, The mathematical function is a dynamic equation based on ecological principles. Its specific form is determined according to the interaction relationship and the state parameter types of the two ecological entities involved. The two ecological entities are denoted as the first ecological entity and the second ecological entity, respectively. For the aforementioned inter-biological interaction type, when the state parameters of both ecological entities involved in the interaction relationship include parameters characterizing biological population size or biomass, the mathematical function is a first-class function used to quantify the influence intensity of the rate of change of state parameters between biological ecological entities, specifically including: If the relationship type is competition, the following formula is used to describe the competition relationship: ； ； in, and These are the state parameters of the first ecological entity and the second ecological entity, respectively. and These are the inherent growth parameters of the first and second ecological entities, respectively. and These are the carrying capacity parameters for the first and second ecological entities, respectively. This represents the competition coefficient between the second ecological entity and the first ecological entity. This represents the competition coefficient between the first ecological entity and the second ecological entity. If the relationship type is symbiotic, the symbiotic relationship is described by the following formula: ； ； in, This represents the symbiotic benefit coefficient of the second ecological entity to the first ecological entity. >0; This represents the symbiotic benefit coefficient of the first ecological entity to the second ecological entity. Greater than 0; If the relationship type is predation, the predation relationship is described by the following formula: ； ； in, These are the state parameters of the ecological entity that acts as prey. For the state parameters of the ecological entity as a predator; The inherent growth parameter of the prey; This is the predator's attack rate coefficient, used to quantify the intensity of predation. This refers to the energy conversion efficiency parameter. For predator loss rate parameters; If the relationship type is parasitic, the parasitic relationship is described by the following formula: ； ； in, These are the state parameters of the ecological entity that serves as the host. These are the state parameters of the ecological entity as a parasite; and These represent the host's intrinsic growth parameter and carrying capacity parameter, respectively; γ is the parasitism rate coefficient, used to quantify the intensity of parasitism; θ is the parasite's energy acquisition efficiency parameter; and μ is the parasite's loss rate parameter. If the relationship type is co-evolution, the following formula is used to describe the co-evolutionary relationship: ； ； in, and These are the adaptive trait state parameters for the first and second ecological entities, respectively. and These are the theoretical optimal values ​​for the adaptive traits; and The intrinsic rate parameter for the evolution of each trait toward the optimal value; This represents the synergy coefficient between the second ecological entity and the changes in the traits of the first ecological entity. >0; This represents the synergy coefficient between the first ecological entity and the changes in the traits of the second ecological entity. >0; For the aforementioned type of environmental factor interaction, when the interaction involves a biotic ecological entity and an abiotic ecological entity, the mathematical function is a second-type function used to quantify the influence of the state parameters of the abiotic ecological entity on the rate of change of the state parameters of the biotic ecological entity, expressed by the following formula: ； Wherein, B represents the state parameter of the biological ecological entity; S represents the state parameter of the non-biological ecological entity; This represents the rate of change of the state parameters of the biological ecological entity; It is a function that describes the dynamics of the biological ecological entity itself; It is an environmental impact factor function with S as the independent variable, used to quantify the impact intensity of the non-biological ecological entities.

3. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 2, characterized in that, Step S5 specifically includes: S51: Based on the modified ecological interaction model, construct a set of differential equations describing the changes of state parameters of each ecological entity over time, wherein the form of each equation is determined by the mathematical function. S52: Set the simulation time step Δt, and update the initial conditions or coefficient parameters of the differential equation system based on the ecological entity state parameters or interaction relationship parameters modified by the editing instructions; S53: The updated differential equation system is solved step by step using a numerical iterative method. The state parameter values ​​of each ecological entity are calculated after each time step, thereby obtaining the continuous trajectory of the evolution of the state of the ecosystem over simulation time from the editing time.

4. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 1, characterized in that, Step S4 further includes converting the editing instructions into modification instructions for the ecological interaction model, and then performing model consistency verification and adaptive update operations, specifically including: In a virtual reality environment, a modified ecological interaction model is subjected to real-time conflict detection based on a predefined ecological constraint rule base, and the detected ecological logical inconsistencies are fed back to the user through visual or auditory warning signals. In response to the user's confirmation of the repair instruction, conflict resolution options are presented to the user through a virtual control panel. Based on the user's selection, an optimization model is solved with ecological stability index as the objective function and the ecological constraint rules as the constraint conditions. The system automatically calculates and implements adjustment schemes for the state parameters of relevant ecological entities and the strength of their interactions, so that the ecological interaction model is restored to a stable equilibrium state that conforms to ecological constraints. The adjustment details are also displayed synchronously through a virtual information panel. Based on the connections in the modified ecological interaction model, all ecological entities and their interactions that are directly or indirectly affected by the current editing operation are derived through a graph traversal algorithm, generating impact propagation path data. In the ecological relationship network graph, the corresponding nodes and connecting edges are dynamically visually encoded according to the impact propagation path data. At the same time, a spatial coordinate mapping relationship is established between each ecological entity identifier in the impact propagation path data and the corresponding 3D model in the virtual reality 3D scene. When a user's selection command for a highlighted node in the ecological relationship network diagram is captured through a virtual reality interactive device, the observation perspective of the virtual reality environment is automatically navigated and focused on the corresponding three-dimensional model of the ecological entity in the three-dimensional scene according to the spatial coordinate mapping relationship, and the following information is superimposed and displayed around the three-dimensional model of the ecological entity: the evolution curve of the state parameters corresponding to the ecological entity in the process of the change of the state of the ecosystem.

5. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 1, characterized in that, Step S6 also includes the following steps: A time series analysis is performed on the trajectory data of the state parameters of each ecological entity output by the ecological dynamic simulation calculation over time. The time points when the rate of change of the state parameters exceeds the first preset threshold or the system stability index is lower than the second preset threshold are identified as key time points. Based on the intensity data of each interaction relationship in the ecological interaction model, the interaction relationship with the greatest intensity at the key time point is determined as the dominant interaction relationship. The perturbation analysis algorithm is used to quantify the causal contribution of different editing operations to the final ecosystem state, specifically including: Let the set of edit operations be E = {e1, e2, ..., e}. n After performing all editing operations, the final state vector of the ecosystem is S. final The baseline state vector that has not undergone any editing operations is S. base Each editing operation e is simulated and calculated by controlling variables. i The state change vector ΔS triggered by a single entity i Then its contribution C i Calculated according to the following formula: ； Where ||·|| represents the norm of the vector. This represents the sum of the norms of the state change vectors triggered individually by all editing operations; Based on contribution level C i Editing operations are sorted, and those with a contribution exceeding a preset contribution threshold are identified as primary causal factors. The system automatically integrates the editing instructions, the coefficients of the mathematical functions defined in the ecological interaction model, the key time points and their corresponding dominant interaction relationships, as well as the causal contribution ranking results and the identified main causal factors to generate a structured virtual experiment report.

6. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 1, characterized in that, The method further includes: Construct a multi-scale observation model, which includes at least a macro-ecosystem scale observation model, a meso-biological community scale observation model, and a micro-biological functional unit scale observation model, and establish a mapping relationship of state parameters for the ecological entities in the digital twin model between different scale observation models; In the virtual reality environment, in response to the user's scale switching command for the target ecological entity, based on the mapping relationship of state parameters established between different scale observation models, the observation perspective is smoothly transitioned from the current scale to the target scale, and the focus is automatically placed on the corresponding representation of the target ecological entity in the target scale observation model. When the observation perspective is at the scale of the microscopic biological functional unit, the following sub-steps are performed: From the ecological dynamic simulation calculation performed in step S5, extract the parameter values ​​corresponding to the environmental factor action types defined by the ecological interaction model established in step S2 that act on the currently focused biological functional unit, and use them as environmental factor parameters; at the same time, extract at least one state variable value of the biological functional unit in the ecological dynamic simulation calculation, and use it as biological state parameters; input the environmental factor parameters and biological state parameters into a preset physiological response function, and calculate one or more microscopic physiological process rate values ​​in real time. Depending on the type of microscopic physiological process, the corresponding rendering engine is invoked, and the calculated rate value of the microscopic physiological process or the process state variable calculated from the rate value is used for dynamic visualization: For mass transport processes, the microscopic physiological process rate values ​​are used to drive the particle system, simulating the movement path and flux of matter. For surface state and energy conversion processes, the rate value of the microscopic physiological process or the process state variable is used as an input parameter to drive the dynamic shader to change the surface visual properties of the three-dimensional model of the biological functional unit in real time.

7. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 6, characterized in that, The method further includes the following steps: At the beginning of each simulation time step in step S5, the environmental factor parameters currently acting on the macroscopic ecological entity to which the current focused biological functional unit belongs are obtained, and the environmental factor parameters are used as input parameters of the physiological response function to drive the physiological response function to calculate the current time step. Based on the calculation results of the physiological response function, the microscopic physiological process rate value of the currently focused biological functional unit is obtained; then, according to the spatial representation relationship between the biological functional unit and its corresponding macroscopic ecological entity, the microscopic physiological process rate values ​​of all related biological functional units are aggregated, and the net influence ΔB(t) on the core biological state parameters of the macroscopic ecological entity within the current time step is calculated. The formula for calculating the net influence ΔB(t) is as follows: ΔB(t)=Σ[R k(t) ×w k ]×Δt; Among them, R k(t) w represents the rate of the microphysiological process of the k-th related biological functional unit at the current time step. k Δt is the aggregation weight coefficient of the k-th biological functional unit, and Δt is the simulation time step. The net impact ΔB(t) is used as a correction term and fed back to the ecological interaction model to update the instantaneous rate of change equation of the biological state parameter B of the macroscopic ecological entity in the current time step calculation. The update method of the instantaneous rate of change equation of the biological state parameter B is as follows: ； in, This is the basic rate of change calculated solely based on the inter-organism interactions in the aforementioned ecological interaction model; The calculation of the current time step is completed based on the instantaneous rate of change equation of the updated biological state parameter B, and then the iteration of the next time step begins.

8. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 1, characterized in that, The method further includes: S7: In the virtual reality environment, user interaction behavior data is collected in real time through a data acquisition interface. The interaction behavior data includes: A sequence of operations to execute editing instructions on nodes or connecting edges in the ecological relationship network graph; The duration of time spent in front of the interface displaying the ecological relationship network diagram generated in step S3 or the dynamic visualization of the ecosystem state change process displayed in step S6. The viewing focus frequency of a specific type of ecological entity or the cluster area corresponding to that ecological entity in the digital twin model; S8: Process the collected interaction behavior data based on the preset user classification model to generate a user profile, wherein the user profile includes at least two of the following: user identity type, ecological knowledge level, and learning preferences. S9: Based on the user profile and the preset teaching knowledge graph, a personalized VR campus ecology tour route is generated through a path planning algorithm, wherein the preset teaching knowledge graph is associated with the digital twin model; The path planning algorithm optimizes the spatial paths connecting the various ecological entities in the digital twin model based on the following constraints: Maximize the number of ecological knowledge points covered by the path that are related to the relationship types defined in the ecological interaction model; Minimize the sum of the total spatial length of the path and the estimated learning time required to complete all interactions on the path; The matching degree between the types of ecological entities traversed by the path and the types of interactions associated with them and the learning preferences in the user profile is maximized. The difference in difficulty level values ​​between adjacent teaching nodes in the path does not exceed a preset first threshold, and the sequence of difficulty level values ​​of the entire path satisfies monotonically non-decreasing or wave-shaped gradual change, wherein the teaching node is a key ecological entity or ecological entity cluster area in the digital twin model. S10: During VR tours, based on the user's real-time location within the virtual space of the digital twin model, dynamic navigation guidance is provided through spatial path rendering technology. This dynamic navigation guidance includes: S101: Based on the spatial relationship between the user's current location and the planned route, navigation instructions are generated in real time and superimposed on the virtual reality scene; S102: Based on the user's travel speed and direction, predict and preload the three-dimensional model data of the ecological entities associated with the next navigation point and their relationship data in the ecological interaction model; S103: When it is detected that the distance between the user's current location and the planned route exceeds a preset threshold, the path replanning algorithm is triggered to recalculate the guiding path.

9. The virtual reality landscape simulation method integrating dynamic navigation as described in claim 8, characterized in that, Step S102 includes: Based on the user's location, direction of travel, and instantaneous velocity vector obtained through real-time positioning, and combined with the spatial orientation of the planned route, the spatial area to be covered by the user's view frustum or travel path within a preset time period ΔT is predicted using linear extrapolation or curve fitting algorithms; ecological entities located within the predicted spatial area and belonging to the digital twin model are identified as the potential access target set Z. For each ecological entity E in the set of potential access targets Z i Perform ecological network importance assessment and user interest matching assessment; The assessment of the importance of the ecological network includes: calculating the ecological entity E. i The normalized degree centrality and normalized betweenness centrality are calculated from the ecological relationship network graph, and the normalized degree centrality and normalized betweenness centrality are weighted and summed to obtain the ecological entity E. i The corresponding network importance score S net(i) ; The user interest matching assessment includes: constructing ecological entity E i The attribute feature vector is obtained, which includes at least its ecology type encoding and the encoding of the associated dominant interaction type; the cosine similarity between this vector and the learning preference feature vector extracted from the current user profile is calculated to obtain the interest matching score S. pref(i) ; Calculate ecological entity E using the following formula. i Comprehensive preloading priority P riority（i） =w i· S net(i) +(1−w i )·S pref(i) ; Among them, the fusion weight coefficient w i w is a variable that is dynamically adjusted based on the real-time available graphics memory usage of the computing device running the virtual reality environment. When the available graphics memory usage exceeds a first usage threshold, w is set. i The first value is used; when the available graphics memory usage is lower than the second threshold, w is set. i The second value, w i The value range is (0,1), and the first value is greater than the second value; Based on the calculated comprehensive preloading priority, all ecological entities in the potential access target set Z are sorted in descending order, and the following hierarchical loading strategy is executed according to the sorting results: For ecological entities ranked in the top R1% of priority, load the complete 3D model, first-resolution texture, real-time state parameters and direct relationship data in the ecological relationship network corresponding to the ecological entity. For ecological entities ranked in the middle R2%, load the simplified 3D model, second-resolution texture, and key state parameters of the ecological entity. For ecological entities ranked in the bottom 3% of priority, only the spatial location identifier and name label of the ecological entity are loaded. The first resolution is higher than the second resolution. R1, R2, and R3 are preset percentage parameters, and R1+R2+R3=100.

10. A virtual reality landscape simulation system integrating dynamic navigation, characterized in that, The system is configured to perform the method as described in any one of claims 1-9, the system comprising: The model building module is used to build a digital twin model of the campus ecosystem. The digital twin model contains multiple ecological entities with quantified state parameters. The ecological entities include at least two of the following: individual plants, soil regions, water units, individual animals, and microbial communities. An interaction modeling module is used to establish an ecological interaction model based on the digital twin model. This ecological interaction model defines the interaction relationship between any two ecological entities through mathematical functions, wherein: The interaction relationship has a preset relationship type, which includes inter-biological interaction type and environmental factor interaction type. The inter-biological interaction type includes any one of competition, symbiosis, predation, parasitism, and co-evolution. The environmental factor interaction type is used to characterize the influence of abiotic ecological entities on biotic ecological entities. For each type of interaction relationship, the strength of the interaction is quantified by the corresponding mathematical function. The sign of the output value of the mathematical function indicates the direction of the interaction, with positive values ​​indicating a promoting effect and negative values ​​indicating an inhibiting effect. The network graph generation module is used to generate a visualized ecological relationship network graph based on the ecological interaction model, wherein: Each ecological entity is represented as a node in the ecological relationship network graph; Each pair of interacting ecological entities is represented by a connecting edge; The visual feature mapping of connected edges corresponds to the relationship type and intensity of the interaction. The interaction and instruction conversion module is used to receive editing instructions from the user on the ecological relationship network graph through a virtual reality interaction device. The editing instructions include operations on nodes and connecting edges. The module converts the editing instructions into modification instructions for the ecological interaction model. The modification instructions include any one or more of the following: deleting the ecological entity corresponding to the node, modifying the state parameters of the ecological entity, and modifying the interaction relationship between the ecological entities. A dynamic simulation calculation engine is used to respond to the editing command and perform dynamic ecological simulation calculations based on the modified ecological interaction model to obtain the process of ecosystem state change caused by the editing operation. The virtual reality rendering and display module is used to generate and display dynamic visualization effects in a virtual reality environment based on the process of ecosystem state change.

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