Blockchain-based virtual ecological bill online management method and device

Abstract

The application provides a kind of virtual ecological bill online management method and device based on blockchain.The method comprises: obtaining the ecological resource distribution data of target area, and quantitatively processing based on the preset ecological product total value accounting specification to extract ecological attribute feature vector;According to the ecological attribute feature vector, the digital twin resource model of the target area is constructed, and the corresponding virtual ecological initial state data is generated using the model;Virtual ecological initial state data is converted into virtual ecological bill with cross-node circulation attribute, and uploaded to the block chain distributed ledger for evidence;Call the smart contract deployed on the blockchain to track the status of virtual ecological bill, control its online circulation between multiple nodes and record the circulation state.The application can improve the evidence credibility and tracking reliability of ecological data cross-domain circulation to a higher degree.

Description

Technical Field

[0001] This application relates to the fields of blockchain and digital asset management technology, and more specifically, to a method and apparatus for online management of virtual ecological invoices based on blockchain. Background Technology

[0002] With the continuous evolution of the mechanism for realizing the value of ecological products and the deepening of the "three assets and three transformations" concept, the assetization and digital management of ecological resources have become key to breaking through the bottlenecks of regional economic transformation and development. Under the Gross Ecological Product (GEP) accounting system, the quantitative assessment and cross-regional circulation of vast and complex ecological resources place higher demands on the online management, asset status tracking, and distributed collaboration of digital ecological invoices.

[0003] Existing ecological resource management and invoicing systems typically employ a centralized database architecture to coordinate various ecological monitoring and circulation data. This approach first uses a central server to connect with monitoring nodes across different regions to collect ecological resource data. Subsequently, the system performs static ledger registration and overall value estimation of the collected data according to fixed business logic scripts. Finally, a centralized approval workflow engine controls the cross-departmental distribution and inter-node transfer of ecological value certificates.

[0004] However, this centralized management solution has significant technical flaws. Due to the wide range of ecological data sources and the complexity of circulation nodes, the single-point architecture of the centralized database is prone to problems such as easy data tampering and broken circulation and evidence storage links when data is exchanged among multiple parties. At the same time, static ledgers and conventional processing scripts are unable to perform high-fidelity twin mapping between multi-dimensional ecological attribute characteristics and dynamically evolving digital resources, and lack distributed automated trust circulation and tracking mechanisms. This makes it difficult to reliably track the status of vouchers circulating across nodes and achieve dynamic adaptive closed-loop circulation, failing to meet the requirements of highly reliable evidence storage and transparent interaction in the management of ecological resource vouchers. Summary of the Invention

[0005] This application provides a blockchain-based online management method and apparatus for virtual ecological tickets, to at least alleviate the aforementioned technical problems.

[0006] A blockchain-based online management method for virtual ecosystem tickets includes the following steps: Acquire ecological resource distribution data of the target area, and quantify the ecological resource distribution data based on the preset ecological product gross value accounting standard to extract ecological attribute feature vectors. The ecological attribute feature vectors are used to characterize the ecological attribute quantification results formed by the ecological resource distribution data under the preset ecological product gross value accounting standard. A digital twin resource model for the target area is constructed based on the ecological attribute feature vector, and virtual ecological initial state data corresponding to the ecological resource distribution data is generated using the digital twin resource model. The initial state data of the virtual ecosystem is converted into virtual ecosystem tickets with cross-node circulation attributes, and the virtual ecosystem tickets are uploaded to the blockchain distributed ledger for storage, so that the blockchain distributed ledger records the correspondence between the virtual ecosystem tickets and the initial state data of the virtual ecosystem. The system invokes smart contracts deployed on the blockchain distributed ledger to track the status of virtual ecosystem tickets, controls the online circulation of virtual ecosystem tickets among multiple nodes based on the triggering conditions of the smart contracts, and records the online circulation status of virtual ecosystem tickets among multiple nodes based on the blockchain distributed ledger.

[0007] Optionally, before the step of obtaining ecological resource distribution data for the target area, the following steps are also included: Feature identification is performed on target monitoring resources within the target area to obtain data on the idle status of the target monitoring resources and information on the nodes to which the resources belong. Based on the data on the idle status of resources and the information on the nodes to which resources belong, the target monitoring resources are tagged to determine the set of target monitoring resources that meet the conditions for data extraction. The resource distribution records corresponding to the set of target monitoring resources are then used as the ecological resource distribution data of the target area.

[0008] Optionally, the step of quantifying the ecological resource distribution data based on the preset ecological product gross value accounting standards and extracting ecological attribute feature vectors includes: Identify the system function types in the ecological resource distribution data, and map the system function types to the pre-defined ecological product total value accounting specifications corresponding to the adjustment service accounting dimension, supply service accounting dimension, or cultural service accounting dimension; Under the accounting dimensions of adjustment services, supply services, or cultural services, physical quantity accounting processing is performed on the ecological resource distribution data to generate physical quantity data of ecological products. The physical quantity data of ecological products are standardized and converted to obtain ecological attribute feature vectors.

[0009] Optionally, the steps of constructing a digital twin resource model of the corresponding target area based on the ecological attribute feature vector include: Obtain the geospatial topology of the target area, inject ecological attribute feature vectors into the geospatial topology, and obtain the geospatial topology with injected ecological attribute feature vectors. Establish a mapping logic for transforming resources into digital objects, and encapsulate the geospatial topological relationships injected with ecological attribute feature vectors according to the mapping logic to generate a digital twin resource model.

[0010] Optionally, the steps of generating virtual initial state data of the ecosystem corresponding to the ecological resource distribution data using a digital twin resource model include: Extract data unique identifiers, feature evaluation weights corresponding to ecological attribute feature vectors, and life cycle parameters corresponding to ecological resource distribution data from the digital twin resource model; Data unique identifiers, feature evaluation weights corresponding to ecological attribute feature vectors, and life cycle parameters corresponding to ecological resource distribution data are aggregated to generate virtual ecological initial state data. This virtual ecological initial state data carries data unique identifiers, feature evaluation weights corresponding to ecological attribute feature vectors, and life cycle parameters corresponding to ecological resource distribution data.

[0011] Optionally, the step of converting the initial state data of the virtual ecosystem into virtual ecosystem tickets with cross-node circulation attributes includes: Obtain data exchange guidance parameters from the external system environment, and correct the feature evaluation weights corresponding to the ecological attribute feature vectors in the initial state data of the virtual ecosystem based on the data exchange guidance parameters to obtain the basic feature information of the invoice; Assign permission share weights to the initial state data of the virtual ecosystem, associate the permission share weights with the basic characteristic information of the invoice, and obtain the virtual ecosystem invoice. The virtual ecosystem invoice carries the permission share weights, the basic characteristic information of the invoice, and the unique data identifier code corresponding to the initial state data of the virtual ecosystem.

[0012] Optionally, the steps of invoking a smart contract deployed on a blockchain distributed ledger to track the state of virtual ecosystem tickets include: Listen for access requests in the blockchain distributed ledger and identify the account permission level corresponding to the access request; The system retrieves the current ownership node status of virtual ecological tickets based on account permission levels, and synchronously updates the operation logs of virtual ecological tickets based on access requests and account permission levels, so that the current ownership node status of virtual ecological tickets and the operation logs of virtual ecological tickets together constitute the status tracking record of virtual ecological tickets.

[0013] Optionally, the steps for controlling the online circulation of virtual ecosystem tickets among multiple nodes based on the triggering conditions of the smart contract include: Identify the network identity attributes of participating nodes in the multi-party node flow, and compare the network identity attributes with the admission rules in the smart contract; If the comparison results are consistent, perform the current ownership node state transfer operation for the virtual ecological ticket; After the current ownership node of the virtual ecological ticket completes its state transfer operation, a state synchronization instruction is triggered, which in turn updates the distributed consistency of the virtual ecological ticket in the blockchain distributed ledger.

[0014] Alternatively, blockchain-based online management methods for virtual ecosystem tickets also include: Ecological environment change parameters of the target area are collected in real time through preset hardware monitoring nodes; Ecological environment change parameters are fed back into the digital twin resource model. The ecological attribute feature vector in the digital twin resource model is dynamically adjusted using the ecological environment change parameters. The basic feature information of the virtual ecological ticket is then updated based on the dynamically adjusted ecological attribute feature vector.

[0015] A blockchain-based online management device for virtual ecological invoices includes: The ecological attribute feature vector extraction module is used to acquire ecological resource distribution data of the target area, and to quantify the ecological resource distribution data based on the preset ecological product gross value accounting standard to extract ecological attribute feature vectors. The ecological attribute feature vectors are used to characterize the ecological attribute quantification results formed by the ecological resource distribution data under the preset ecological product gross value accounting standard. The virtual ecological initial state data generation module is used to construct a digital twin resource model of the target area based on the ecological attribute feature vector, and to generate virtual ecological initial state data corresponding to the ecological resource distribution data and the ecological attribute feature vector using the digital twin resource model. The virtual ecosystem ticket notarization module is used to convert the initial state data of the virtual ecosystem into virtual ecosystem tickets with cross-node circulation attributes, and upload the virtual ecosystem tickets to the blockchain distributed ledger for notarization, so that the blockchain distributed ledger records the correspondence between the virtual ecosystem tickets and the initial state data of the virtual ecosystem. The Virtual Ecosystem Invoice Circulation Management Module is used to call smart contracts deployed on the blockchain distributed ledger to track the status of virtual ecosystem invoices, control the online circulation of virtual ecosystem invoices among multiple nodes according to the trigger conditions of the smart contracts, and record the online circulation status of virtual ecosystem invoices among multiple nodes based on the blockchain distributed ledger.

[0016] The technical advantages of the technical solution provided in this application are: This application presents a blockchain-based online management method and apparatus for virtual ecological invoices. Addressing the technical shortcomings of traditional centralized management schemes, such as susceptibility to data tampering, inaccurate mapping of multi-dimensional ecological attributes, and the lack of a distributed, automated circulation tracking mechanism, this method acquires ecological resource distribution data for a target area and extracts ecological attribute feature vectors through quantitative processing based on pre-defined ecological product total value accounting standards. This solves the problem of the difficulty in standardizing and quantifying the vast ecological resources in traditional schemes. Compared to traditional simple static ledger summaries, this application introduces rigorous accounting standards to extract quantitative results, laying a more solid data foundation for subsequent asset digitization.

[0017] Based on the aforementioned ecological attribute feature vectors, a digital twin resource model for the corresponding target area is constructed, and this model is used to generate corresponding virtual ecological initial state data, solving the problem of the disconnect between physical and digital state mapping under the traditional management architecture. Traditional solutions rely on fixed scripts and struggle to recreate ecological evolution, while this application utilizes digital twin technology to achieve a high-fidelity representation of ecological distribution data, enabling the generated virtual ecological initial state data to better reflect the dynamic and complex characteristics of real-world resources.

[0018] The initial state data of the virtual ecosystem is converted into virtual ecosystem tickets with cross-node circulation attributes and uploaded to a blockchain distributed ledger for storage, thus recording the correspondence between the two. This solves the problems of evidence breakage and single point of tampering that are prone to occur in traditional centralized databases. Compared with centralized server storage, the immutability and decentralization of the blockchain distributed ledger endow ecosystem tickets with higher data transparency and cross-node trust.

[0019] Furthermore, by invoking smart contracts deployed on the blockchain's distributed ledger to track the status of virtual ecosystem tickets, and controlling their online circulation among multiple nodes and recording their circulation status based on trigger conditions, this solves the problems of low efficiency and lack of transparency in traditional centralized approval workflow engines. Compared to traditional interventionist circulation mechanisms, the smart contract-based circulation method can autonomously complete distributed consistency verification of the status, ensuring that each cross-node circulation of tickets has a high degree of reliable traceability, significantly improving the reliability and security of ecosystem digital credentials during circulation and management. Attached Figure Description

[0020] Figure 1 This is a schematic diagram illustrating an application scenario of an online management method for virtual ecological invoices based on blockchain, as described in an embodiment of this application.

[0021] Figure 2 This application provides an embodiment of an online management method for virtual ecological invoices based on blockchain.

[0022] Figure 3This application provides an embodiment of a blockchain-based online management device for virtual ecological invoices.

[0023] Figure 4 This is an electronic device according to an embodiment of the present application. Detailed Implementation

[0024] like Figure 1 The diagram shown is a schematic representation of an online management scenario for virtual ecological invoices based on blockchain, according to an embodiment of this application. Figure 2 The image shows an embodiment of a blockchain-based online management method for virtual ecological tickets, which includes the following steps: The ecological resource distribution data of the target area is obtained, and the ecological resource distribution data is quantified based on the preset ecological product gross value accounting standard to extract ecological attribute feature vectors. The ecological attribute feature vectors are used to characterize the ecological attribute quantification results formed by the ecological resource distribution data under the preset ecological product gross value accounting standard. A digital twin resource model corresponding to the target area is constructed based on the ecological attribute feature vector, and virtual ecological initial state data corresponding to the ecological resource distribution data is generated using the digital twin resource model. The initial state data of the virtual ecosystem is converted into virtual ecosystem tickets with cross-node circulation attributes, and the virtual ecosystem tickets are uploaded to the blockchain distributed ledger for storage, so that the blockchain distributed ledger records the correspondence between the virtual ecosystem tickets and the initial state data of the virtual ecosystem. The system invokes a smart contract deployed on the blockchain distributed ledger to track the status of the virtual ecological tickets, controls the online circulation of the virtual ecological tickets among multiple nodes according to the triggering conditions of the smart contract, and records the online circulation status of the virtual ecological tickets among the multiple nodes based on the blockchain distributed ledger.

[0025] Optionally, before the step of obtaining the ecological resource distribution data of the target area, the method further includes: Feature identification is performed on the target monitoring resources within the target area to obtain resource idle status data and resource ownership node information corresponding to the target monitoring resources; The target monitoring resources are tagged based on the resource idle status data and the resource ownership node information to determine the target monitoring resource set that meets the data extraction conditions, and the resource distribution record corresponding to the target monitoring resource set is used as the ecological resource distribution data of the target area.

[0026] Preferably, before acquiring ecological resource distribution data for the target area, it is necessary to first define clear data collection boundaries and object scope to address the problems of poor data quality and insufficient evidence credibility caused by mixed data sources and ambiguous ownership in traditional centralized database schemes. To this end, in the preprocessing stage of data acquisition, this application implements a pre-processing procedure based on multi-dimensional state identification and ownership association for the registered ecological resources within the target area. The technical starting point of this procedure is the feature identification of target monitoring resources within the target area. Here, target monitoring resources refer to various natural and semi-natural elements scattered within the target area, confirmed through preliminary investigation, that possess ecological regulation, material supply, or cultural service functions. Examples include forest stands at different successional stages (such as middle-aged forests and near-mature forests), wetlands with water conservation functions, grasslands undertaking biodiversity maintenance tasks, or landscape landforms that have not yet been developed for tourism. By identifying the features of these target monitoring resources, unlike the simple statistics on resource area or quantity in traditional schemes, this approach delves into the dimensions of resource existence and ownership to obtain corresponding data on resource idle status and resource ownership nodes.

[0027] Preferably, in the specific implementation of the above feature recognition, the process of obtaining resource idle status data is essentially a digital calibration of the gap between the current development and utilization intensity and the potential carrying capacity of the target monitored resource. This process involves multi-dimensional time series comparison of the target monitored resource, calculating the difference between the theoretical ecological output capacity of the target monitored resource within a preset accounting period and the ecological output actually seized or occupied by human activities during the same period, thereby forming a quantitative idle metric. For example, for a wetland with water conservation function, its resource idle status data does not simply refer to the area of ​​the wetland, but rather to the data scalar corresponding to the remaining unused water conservation function supply after deducting the portion granted usage rights through water withdrawal permits or other means from the total water conservation function supply of the wetland under current hydrological and meteorological conditions. For example, the unused conservation water volume (such as tens of thousands of cubic meters of conservation surplus per year). Similarly, for a landscape resource, this data is reflected as the remaining recreational carrying capacity parameter after deducting the current normalized tourist carrying capacity from the environmental capacity. These data on the idle status of resources objectively reflect the available potential of the monitored resources, rather than simply recording their physical existence.

[0028] Preferably, while acquiring data on resource idle status, this application simultaneously acquires resource ownership node information. This acquisition is not a traditional administrative division registration, but rather, within the context of a distributed network, anchoring the network identity of the entity holding the regulatory and revenue rights to the target monitored resource. Specifically, resource ownership node information refers to a unique identifier within a blockchain node network composed of multiple governance entities, identifying the node with direct jurisdiction or ownership affiliation over a specific target monitored resource. This node identity includes the node's encrypted public key derivative identifier within the network and its hierarchical affiliation identifier within the multi-level governance system. For example, for a bamboo forest resource belonging to a collective and actually managed by a grassroots management unit, its resource ownership node information will explicitly point to a unique network node identifier representing that grassroots management unit, rather than a general administrative region name. This mechanism, which precisely maps physical world management affiliations to unique node identities within the network, lays the underlying technical foundation for subsequently establishing the responsible entities for cross-node transfers.

[0029] Preferably, after acquiring the resource idle status data and resource ownership node information of the target monitoring resources, this application does not directly send all the above data into the accounting pipeline. Instead, it performs a preprocessing operation based on two-dimensional label joint screening, that is, labeling the target monitoring resources according to the resource idle status data and resource ownership node information. The core of this labeling process is to construct a two-factor judgment logic, which incorporates the idle status dimension and the ownership clarity dimension into the joint evaluation framework. In terms of technical implementation, this labeling process will assign an idle level label and an ownership certainty label to each target monitoring resource in parallel. The idle level label is a classification identifier assigned based on the quantified idle metric value in the resource idle status data and compared with the preset idle threshold interval distribution (such as dividing the idle rate into high idle interval, medium idle interval, and low idle interval). The attribution determination label is a binary or hierarchical determination label assigned based on whether the node identity identifier in the resource attribution node information is unique and whether the node identity identifier is consistent with the jurisdictional affiliation of the target monitored resource.

[0030] Preferably, based on the results of the aforementioned tagging process, this application performs further screening and aggregation operations to determine the set of target monitoring resources that meet the conditions for data extraction. The determination logic of this process is not based on a single-dimensional threshold, but rather on a joint correlation determination of idle level tags and ownership certainty tags. Only target monitoring resources that simultaneously meet the following conditions—idle level tags indicating that their idle status data has an idle metric value exceeding a set idle rate threshold (e.g., an idle rate exceeding a preset 30%), and ownership certainty tags verifying that the node identity in their resource ownership node information is clear and undisputed—will be included in the target monitoring resource set. This operation excludes resources that, although owned, are nearly fully utilized and lack redistribution value, as well as resources that, while objectively idle, are temporarily unsuitable for digital extraction due to ongoing ownership disputes. This establishes a quality screening mechanism at the data source, ensuring that the data foundation entering subsequent circulation stages possesses basic physical availability and ownership integrity.

[0031] Preferably, after determining the target monitoring resource set, this application does not directly use the target monitoring resource set as the input for subsequent processing modules. Instead, it transforms the format of each record contained within the target monitoring resource set into a standardized resource distribution record specifically used to describe the geographical distribution and attribute layout of the target monitoring resources. The aggregated whole of these resource distribution records serves as the ecological resource distribution data for the target area. This resource distribution record integrates and encapsulates the spatial geographic coordinates, attached idle level labels, ownership determination labels, and index identifiers pointing to the physical attribute data of each selected target monitoring resource, according to timestamps and spatial partitions. This ecological resource distribution data, in terms of structure and composition, is strictly distinguished from the total original record in traditional schemes, which contains invalid, unclear ownership, and overused information. Instead, it is a data set that has been refined, standardized, and directly usable for calculation. This dataset provides a clear and reliable data processing object for subsequent extraction of ecological attribute feature vectors based on the preset ecological product total value accounting standards, as well as for constructing a high-fidelity digital twin resource model with clear ownership and idle status associations. This enables the entire virtual ecological invoice management process to be built on a resource data foundation with precise screening and clear responsibilities.

[0032] Optionally, the step of quantifying the ecological resource distribution data based on the preset ecological product gross value accounting standard and extracting the ecological attribute feature vector includes: Identify the system function type in the ecological resource distribution data, and map the system function type to the adjustment service accounting dimension, supply service accounting dimension or cultural service accounting dimension corresponding to the preset ecological product total value accounting specification; Under the accounting dimensions of regulation services, supply services, or cultural services, physical quantity accounting processing is performed on the ecological resource distribution data to generate physical quantity data of ecological products. The ecological attribute feature vector is obtained by standardizing and converting the physical quantity data of the ecological products.

[0033] Preferably, in the initial stage of quantifying ecological resource distribution data based on preset ecological product gross value accounting standards to extract ecological attribute feature vectors, this application first performs a deep analysis of the inherent structure of the ecological resource distribution data to identify the system function types in the ecological resource distribution data. Here, the system function type does not refer to the administrative division or single physical form classification of resource categories in traditional schemes, but rather, based on ecological principles, it determines the dominant functional role actually played by the target monitoring resource corresponding to each resource distribution record in the ecological resource distribution data during the process of material cycling, energy flow, and information transmission in the ecosystem. Unlike the conventional method in existing technologies that only extracts shallow features such as area and quantity, this identification action in this application requires traversing the physical attribute data described in each resource distribution record and comparing it with a series of preset ecological function discrimination templates. These ecological function discrimination templates define multi-parameter threshold combinations for dozens of ecological processes such as water conservation, soil retention, windbreak and sand fixation, flood regulation, carbon sequestration and oxygen release, and climate regulation. For example, if a resource distribution record's spatial geographic coordinates and physical attributes such as vegetation index, soil moisture content, slope, and canopy closure indicate that the target monitoring resource is located in a catchment area and has high water-holding capacity, then after comparison with the water conservation function discrimination template, the system function type identified for this resource distribution record is water conservation function. Similarly, if a resource distribution record's physical attributes indicate that the target monitoring resource is a mature timber forest and the biomass growth model parameters of this resource distribution record reach a preset threshold, then after comparison with the timber supply function discrimination template, the system function type identified for this resource distribution record is timber supply function. This identification process provides a computationally calculable classification basis for subsequently incorporating scattered resource distribution records into a scientific accounting framework.

[0034] Preferably, after obtaining the system function type corresponding to each resource distribution record, this application performs a technical transformation step that maps the above-mentioned ecological function classification to standardized accounting dimensions. That is, the system function type of each resource distribution record is mapped to the pre-set accounting dimensions for regulatory services, supply services, or cultural services corresponding to the ecological product gross value accounting standard. The technical essence of this mapping action is to establish a strict correspondence logic in program design between the physical function of the target monitored resource and the statistical caliber of the accounting framework, solving the problem of data non-standardization input caused by the disconnect between functional description and accounting category in traditional schemes. This mapping process is based on a function-dimensional correspondence mapping table built in memory, which strictly follows the definition of the attribution relationship between the three major service categories and dozens of specific functions in the "Ecological Product Gross Value Accounting Standard (Trial)". Specifically, system function types identified as water conservation, soil conservation, windbreak and sand fixation, flood regulation, carbon sequestration and oxygen release, climate regulation, and pest and disease control are mapped to the regulatory service accounting dimension; system function types identified as timber supply, freshwater supply, fishery product supply, and wild biological raw material supply are mapped to the supply service accounting dimension; and system function types identified as natural landscape recreation and nature education are mapped to the cultural service accounting dimension. Through this mapping, resource distribution records originally described using ecological function language are transformed into standardized data fragments categorized into regulatory service, supply service, or cultural service accounting dimensions, with data items within each standardized data fragment achieving homogenization in accounting caliber.

[0035] Preferably, after mapping each resource distribution record's system function type to the accounting dimensions of regulatory services, supply services, or cultural services, and after splitting the ecological resource distribution data into their respective standardized data segments, this application then independently and in parallel performs physical quantity accounting processing on each standardized data segment within each accounting dimension to generate ecological product physical quantity data. This physical quantity accounting processing refers to calculating the output at the material level for each physical attribute data within the standardized data segments belonging to that accounting dimension, guided by the physical output model defined for that accounting dimension. The resulting calculation is the ecological product physical quantity data. This ecological product physical quantity data differs from traditional solutions that lack physical dimensions or merely use area as a general representation of output; instead, it strictly uses objective physical dimensions such as mass, volume, energy, moles, or information content to express the output. For example, in the regulatory service accounting dimension, for the standardized data fragment mapped to the water conservation function category, physical quantity accounting processing extracts the multi-layered soil moisture characteristic parameters, precipitation interception parameters, and evapotranspiration parameters recorded in the standardized data fragment that are required for the subsequent construction of the digital twin resource model. A water balance difference model is then run to calculate the water conservation quantity expressed in volumetric dimensions (e.g., the number of cubic meters of water resources conserved annually). In the carbon sequestration and oxygen release function category within the regulatory service accounting dimension, for the corresponding standardized data fragment, biomass increment model parameters and photosynthetic efficiency model parameters are extracted to calculate the carbon dioxide fixation quantity and oxygen release quantity expressed in mass dimensions. In the supply service accounting dimension, for the standardized data fragment of the timber supply function category, physical quantity accounting processing extracts the forest stock growth model parameters to calculate the annual timber output quantity expressed in volumetric dimensions. Within the cultural services accounting dimension, for the standardized data segmentation of natural landscape recreation functions, environmental capacity calculation model parameters and landscape accessibility maps are retrieved to calculate the physical volume of recreational carrying capacity expressed in a composite dimension of person-times and time. This operation ensures that each ecological product obtains a clearly defined and objectively measurable physical output value.

[0036] Preferably, after generating ecological product physical quantity data with different dimensions and vastly different numerical distribution ranges under each accounting dimension, this application does not directly send these ecological product physical quantity data with different physical dimensions into the subsequent virtual ecological invoice generation stage. Instead, it performs a standardization conversion step to convert the ecological product physical quantity data into ecological attribute feature vectors that can be uniformly compared and calculated. The technical essence of this standardization conversion process is to eliminate the differences in dimensions and absolute numerical scales in the ecological product physical quantity data, transforming the ecological product physical quantity data into a set of dimensionless standardized indices that can reflect the relative abundance or scarcity of each ecological product within its respective accounting dimension. Specifically, for each ecological product physical quantity data, the standardization conversion process first obtains a national or regional statistical benchmark value with the same data type as the ecological product physical quantity data within its respective accounting dimension. This statistical benchmark value can be the average physical quantity per unit area of ​​the same type of ecosystem or the average physical quantity of the same level of accounting unit. Then, the value of the physical quantity data of the ecological product is divided by the obtained statistical benchmark value to obtain a dimensionless ratio. Next, in order to eliminate the problem of inconsistent ratio distribution range caused by different data types of ecological product physical quantity data, a logarithmic normalization compression process is performed on the ratio. For example, after taking the logarithm to the base of the natural constant or ten, a linear truncation mapping is performed to smoothly compress the value into a uniform preset range, thereby generating a standardized value. The standardized values ​​generated by the above standardization conversion of all ecological product physical quantity data items are arranged in an ordered manner according to a pre-fixed dimension and functional order to form a one-dimensional ordered array. This array is the ecological attribute feature vector. Each element position in the ecological attribute feature vector corresponds to a specific ecological product type, and the value at that position represents the standardized quantitative result of the physical output level of that ecological product in the target area relative to the reference benchmark.

[0037] Preferably, the extracted ecological attribute feature vector differs structurally from traditional methods that primarily construct vectors based on monetized values ​​or artificially assigned weights. Traditional methods often pre-weight and mix results from different accounting dimensions, resulting in the original physical quantity information being overwritten by subjective weights. Furthermore, when entering subsequent digital twin resource models or circulation processes, the direct coupling relationship with the original ecosystem's physical processes is lost. In contrast, the ecological attribute feature vector obtained through standardized conversion in this application has a one-to-one, traceable mapping relationship between each element and a piece of ecological product physical quantity data generated through physical quantity accounting processing, thus fully preserving the objective characteristics of physical output. This ecological attribute feature vector characterizes the ecological attribute quantification results formed by ecological resource distribution data under a preset ecological product total value accounting standard. It provides a set of feature parameters with clear physical meaning, unified dimensions, regular structure, and direct indexability and injection into the model topology of the digital twin resource model for constructing the corresponding target area in subsequent steps. This quantitative processing path, which involves identifying system function types, mapping accounting dimensions, processing physical quantities to generate physical quantity data of ecological products, and standardizing conversions, ensures that the values ​​in each ecological attribute feature vector can be found in the physical world through actual measurements or model deductions.

[0038] Optionally, the step of constructing a digital twin resource model corresponding to the target area based on the ecological attribute feature vector includes: Obtain the geospatial topology of the target area, and inject the ecological attribute feature vector into the geospatial topology to obtain the geospatial topology injected with the ecological attribute feature vector; A mapping logic for transforming resources into digital objects is established. Based on the mapping logic, the geospatial topological relationship injected with the ecological attribute feature vector is encapsulated and processed to generate the digital twin resource model.

[0039] Preferably, in the process of constructing a digital twin resource model of the corresponding target area based on ecological attribute feature vectors, this application first performs the acquisition and structuring of the geospatial topological relationships of the target area. The geospatial topological relationships here do not refer to planar map data containing only coordinates and boundaries as in traditional schemes, but rather a data map organized using a node-edge structure. This data map fully records the spatial adjacency, ecological connectivity, and hierarchical nesting among various target monitoring resources within the target area. Specifically, each node in the geospatial topological relationship represents a basic ecological unit constituting the target area, such as a forest patch, a river corridor, or a wetland patch. The node data carries the spatial location attributes, boundary contour attributes, and attribution determination labels inherited from the resource distribution records corresponding to the basic ecological unit. Edges in the geospatial topological relationship are used to connect two nodes with spatial adjacency or energy and material exchange relationships. Each edge carries a structured label indicating the adjacency type, adjacency length, or material flow direction. Traditional methods often treat the calculation results of each region as isolated values ​​when processing ecological attribute data, thus severing the spatial continuity of material cycling and energy flow within the ecosystem. This application, however, constructs a topological data map that explicitly expresses the spatial interaction structure, providing a structured container with spatial constraints and ecological process correlations for subsequently injecting ecological attribute feature vectors into this geospatial topological relationship.

[0040] Preferably, after obtaining the geospatial topological relationship and completing its structured construction, this application performs a key operation of injecting ecological attribute feature vectors into the geospatial topological relationship, thereby obtaining a geospatial topological relationship injected with ecological attribute feature vectors. Technically, this injection operation is a pairing and binding process between multi-dimensional features and spatial topological nodes. Its processing objects are the ecological attribute feature vectors obtained through standardization in the previous steps, and the node sequence in the geospatial topological relationship. This injection operation first parses the ecological attribute feature vectors to obtain the index mapping relationship between the position of each element in the ecological attribute feature vector and the ecological product type; then, it traverses each node in the geospatial topological relationship, and based on the system function type identified in the previous steps on the basic ecological unit represented by the node, it searches for the element position in the ecological attribute feature vector that matches the system function type, extracts the corresponding standardized value, and binds this standardized value as an attribute field to the node's data structure. Through this pairing and binding, each node in the geospatial topological relationship not only possesses spatial location attributes and topological connection information, but also obtains the quantitative feature values ​​of the various ecological functions carried by the node. Unlike traditional approaches where attribute data and spatial data are isolated from each other and require indirect association through loose identifiers, this injection operation binds fields directly in memory, integrating the numerical values ​​of each dimension of the ecological attribute feature vector with the spatial topology of the geospatial topology into a holistic data structure. This establishes a one-to-one direct coupling relationship between the ecological function distribution information in the physical world and the spatial structure information in the digital world, without the need for secondary parsing.

[0041] Preferably, after obtaining the geospatial topology injected with ecological attribute feature vectors, this application does not directly treat the geospatial topology injected with ecological attribute feature vectors as a whole as a digital twin, but instead enters a core technology design stage, namely, establishing a mapping logic for the transformation of resources into digital objects. The essence of this mapping logic is a set of programmatic conversion rules used to transform continuously distributed ecosystems in the physical world into a set of discrete digital objects in the digital world that are independently addressable, state-updable, and computationally associative. In traditional centralized schemes, ecological resources in digital systems typically exist only as a database record or a raster cell, lacking behavioral logic and lifecycle mechanisms corresponding to their physical entities. The mapping logic for the transformation of resources into digital objects designed in this application defines a multi-level objectification conversion framework. Under this multi-level objectification conversion framework, the mapping logic for the transformation of resources into digital objects first identifies each node contained in the geospatial topology injected with ecological attribute feature vectors, generating an independent primary digital object for each node. The attribute fields of this primary digital object fully inherit the spatial location attributes, topological connection edge information, system function type, attribution deterministic label, and standardized values ​​corresponding to the system function type from the injected ecological attribute feature vector of the basic ecological unit corresponding to the node. Furthermore, the mapping logic for transforming resources into digital objects configures input / output interfaces for this primary digital object based on the material flow direction marked by the node in the edge structure. These input / output interfaces are represented in data structure as reference pointers to other primary digital objects that have a material flow relationship with this primary digital object, as well as the format protocol for passing parameters. This elevates the primary digital object from a static data record into a behavioral entity with external interaction channels.

[0042] Preferably, within the multi-level objectification framework of the above-mentioned resource-to-digital object mapping logic, this application further defines a conversion rule for aggregating primary digital objects into composite digital objects to fully express the synergistic effect and spatial correlation characteristics of the target monitoring resources. Specifically, the resource-to-digital object mapping logic traverses the node clusters connected by edges in the geospatial topology relationship injected with ecological attribute feature vectors, aggregating multiple primary digital objects in the node clusters that form a functional synergistic cluster due to ecological flow connectivity into a composite digital object. This composite digital object is not a simple flattening of its internal primary digital objects, but rather maintains the interaction time-series indicators and state synchronization records between the primary digital objects by adding a coordination data layer inside. This coordination data layer continuously records the time-series logs generated when the primary digital objects interact through their input and output interfaces, and updates the aggregated state parameters exposed to the outside world of the entire composite digital object based on these time-series logs. For example, primary digital objects representing three basic ecological units—forest, shrubland, and wetland—within a catchment area can be coupled into a composite digital object with coordinated water conservation functions. Through the above-mentioned step-by-step transformation from nodes to primary digital objects and then to composite digital objects, the mapping logic of resource-to-digital object transformation establishes a complete digital abstraction system that is faithful to the spatial organization law of ecological processes, providing clear objectified primitives for subsequent encapsulation processing.

[0043] Preferably, after establishing the mapping logic for transforming resources into digital objects and generating various primary and composite digital objects accordingly, this application performs encapsulation processing on the geospatial topological relationships injected with ecological attribute feature vectors to generate a digital twin resource model. This encapsulation processing, technically speaking, is a runtime containerization and interface standardization operation performed on all digital objects and their interaction relationships that have undergone objectification transformation through the mapping logic for transforming resources into digital objects. The encapsulation process constructs a model runtime container, which includes the geospatial topological relationships injected with ecological attribute feature vectors, as well as all primary and composite digital objects generated according to the mapping logic for transforming resources into digital objects, and registers them with a unified addressing index. Simultaneously, the encapsulation process defines three standardized interaction protocol interfaces for the model's runtime container: the first is a state update interface, which receives externally input ecological environment change parameters and routes these parameters to the corresponding attribute fields of the primary or composite digital objects associated within the model runtime container, triggering a state update; the second is a feature query interface, which provides external query services for data such as the spatial location attributes, ecological attribute feature vectors, and topological connection edge information of any digital object within the model runtime container; the third is a lifecycle event interface, which publishes event notifications such as digital object state migration and changes in edge connection relationships. The digital twin resource model generated through this encapsulation process appears externally as a closed software component with a standardized interactive interface, but internally it fully encapsulates the spatial topology, ecological attribute quantification features, and collaborative relationships of digital objects established based on ecological process connectivity for all target monitoring resources within the target area.

[0044] Preferably, the digital twin resource model generated by the above processing differs in technical architecture from the static 3D models in traditional solutions, which are primarily for visualization. Traditional digital models typically contain only geometric rendering data such as polygons and textures, lacking a computable coupling channel with the actual physical logic of ecosystem operation. This makes it difficult for the model to dynamically adjust to changes in the actual ecological environment during subsequent document circulation. In contrast, the digital twin resource model in this application, through its sequential acquisition of geospatial topological relationships, injection of ecological attribute feature vectors into geospatial topological relationships, design of the mapping logic for transforming resources into digital objects, and encapsulation of the mapping logic based on resource-to-digital-object transformation, ensures that each digital object within it simultaneously carries spatial positioning information, multi-dimensional ecological function quantification indicators, a clear ownership attribute (inherited from ownership deterministic labels), and an inter-object interaction channel defined based on ecological connectivity. This structural design makes the digital twin resource model not only a static reproduction of the target area, but also a computable and autonomously responsive digital mirror mapping body. It provides a complete, semantically clear and state-evolvable digital foundation for the subsequent generation of virtual ecological initial state data corresponding to ecological resource distribution data using the digital twin resource model.

[0045] Optionally, the step of generating virtual initial ecological state data corresponding to the ecological resource distribution data using the digital twin resource model includes: Extract the data unique identifier, the feature evaluation weight corresponding to the ecological attribute feature vector, and the life cycle parameter corresponding to the ecological resource distribution data from the digital twin resource model; The data unique identifier, the feature evaluation weight corresponding to the ecological attribute feature vector, and the life cycle parameter corresponding to the ecological resource distribution data are aggregated to generate the virtual ecological initial state data, so that the virtual ecological initial state data carries the data unique identifier, the feature evaluation weight corresponding to the ecological attribute feature vector, and the life cycle parameter corresponding to the ecological resource distribution data.

[0046] Preferably, the technical process of generating initial virtual ecosystem state data based on the already constructed digital twin resource model begins with extracting a unique data identifier from the digital twin resource model. This unique identifier is not a serial number randomly assigned by the central server or without any actual physical connection, as in traditional schemes. Instead, it is a fixed-length character sequence calculated from the attribute fields and spatial topology of each digital object within the digital twin resource model using a pre-defined irreversible digest algorithm. Its technical function is to provide the initially generated virtual ecosystem state data with a digital fingerprint that is uniquely distinguishable across the entire network and can be used to verify the binding relationship between the initial virtual ecosystem state data and the original digital twin resource model. Specifically, the extraction of the data unique identifier involves iterating through the object identifiers, geospatial hash codes of spatial location attributes, and the timestamp of the digital twin resource model's generation time for all primary and composite digital objects in the model. These multi-source fields are concatenated into a byte sequence in a fixed order, and then a cryptographic hash operation (such as using a secure hash algorithm) is performed on this byte sequence to generate a fixed-length hash value, which serves as the data unique identifier. Through this extraction, the data unique identifier is fully embedded with the content snapshot and timestamp characteristics of the digital twin resource model at a specific moment. This ensures that any subsequent modification to the primary or composite digital objects or their attributes within the digital twin resource model will result in a recalculated hash value that differs from the data unique identifier, thus providing objective technical evidence for verifying the content integrity of the initial state data of the virtual ecosystem.

[0047] Preferably, while extracting the unique identifier of the data, this application simultaneously extracts the feature evaluation weights corresponding to the ecological attribute feature vectors from the digital twin resource model. The feature evaluation weights here are a multi-dimensional weight array, where each weight element corresponds one-to-one with a standardized value in the ecological attribute feature vector, used to quantify the relative importance of the ecological product type corresponding to that standardized value in the overall ecological endowment of the target area. The generation of the feature evaluation weights does not rely on externally set fixed values, but is automatically generated and stored in the attribute layer within the digital twin resource model during its construction process, based on the spatial range covered by each primary and composite digital object, the deviation distribution of the available ecological product quantity data, and the synergistic contribution of ecological functions represented by ecological flow connectivity edges. The extraction operation reads out the complete feature evaluation weight array generated by the hierarchical weight derivation operation through the feature query interface of the digital twin resource model. Unlike traditional approaches that treat ecological indicators as equally weighted mixtures or assign weights based solely on experience, the feature evaluation weights extracted from the digital twin resource model in this application, because they carry information on ecological spatial structure and ecological function synergy, can more objectively reflect the structural differences of different types of ecological products in local ecosystems. This provides a set of weight scales that can reflect the regional ecological structure characteristics for the subsequent generated virtual ecological initial state data.

[0048] Preferably, while extracting the unique identifier and feature evaluation weights of the data, this application also simultaneously extracts lifecycle parameters corresponding to the ecological resource distribution data from the digital twin resource model. Here, lifecycle parameters refer to a set of time-series control data used to calibrate the expected effective service life of each target monitoring resource in the target monitoring resource set corresponding to the initial state data of the virtual ecosystem, from the current moment, under the laws of natural succession, and its decay pattern over time. Traditional centralized schemes often treat ecological resources as static, unchanging stock, ignoring the dynamic changes in their supply capacity caused by natural succession, seasonal cycles, or disturbance recovery processes, leading to a gradual disconnect between state information and the physical world during circulation. In this application, the structure of the lifecycle parameter is defined as a tuple containing a baseline time scale, a decay function type identifier, and a list of corresponding decay function coefficients. The generation basis of this lifecycle parameter comes from the succession model parameters and ecological flow dynamics equation parameters injected into each primary digital object within the digital twin resource model during its construction. The extraction operation utilizes the feature query interface of the digital twin resource model to summarize and fuse the lifecycle fields of each primary digital object belonging to the target monitoring resource set (for example, taking the bit lifetime values ​​of similar target monitoring resources in each primary digital object and fitting a unified decay curve coefficient), thereby forming a set of summarized lifecycle parameters corresponding to the entire target monitoring resource set. This extraction result provides a clear and effective temporal boundary for the proposed virtual ecosystem initial state data, enabling subsequent processing to distinguish the duration of different batches of virtual ecosystem initial state data in the time dimension.

[0049] Preferably, after obtaining the three key data components—the unique identifier, the feature evaluation weights corresponding to the ecological attribute feature vectors, and the lifecycle parameters corresponding to the ecological resource distribution data—this application performs a data aggregation operation that integrates these three data components to generate virtual ecological initial state data. This data aggregation is not simply packaging and compressing three data files; rather, it follows a predefined data body format containing multiple structured data fields. The unique identifier is filled into the identifier field of this data body format. Each weight element in the feature evaluation weight array is uniformly filled into the weight array field of this data body format according to the correspondence between the weight element and the standardized values ​​in the ecological attribute feature vectors. The baseline time scale, decay function type identifier, and decay function coefficient list from the lifecycle parameters are sequentially filled into the lifecycle parameter field of this data body format. Simultaneously, several pointer fields are reserved in this data body format. These pointer fields are initially set to null pointers during the data aggregation process, and are used to establish a connection with dynamic information such as the assigned permission share weights when the virtual ecological initial state data is subsequently converted into virtual ecological tickets with cross-node circulation attributes. The virtual ecosystem initial state data generated through this data aggregation operation is, in terms of data structure, a composite data record with self-descriptive capabilities. There are clear logical relationships between the various data domains within this virtual ecosystem initial state data, and the whole data carries a unique data identifier, feature evaluation weights corresponding to ecological attribute feature vectors, and life cycle parameters corresponding to ecological resource distribution data.

[0050] Preferably, in the overall structural design of the initial state data of the virtual ecosystem, the unique data identifier, feature evaluation weights, and lifecycle parameters are not isolated from each other, but rather form a joint feature system based on a common data source—the digital twin resource model. Specifically, the unique data identifier makes the initial state data of the virtual ecosystem a non-repudiable data object uniquely addressable in a distributed network, bound to a digital mirror of the physical world; the feature evaluation weights imbue the initial state data with spatially differentiated information about ecological functions, providing a structured reference baseline for subsequent differentiated correction or adaptation of the initial state data under different external system environments or accounting standards; and the lifecycle parameters define clear timeline control variables for the circulation timeliness and dynamic update management of the initial state data of the virtual ecosystem. This design differs from traditional approaches that store ecological statistics, spatial data, and ownership data in separate database tables, leading to the risk of broken connections due to loose foreign key relationships. Instead, it unifies and solidifies multidimensional information describing the physical state, spatial structure, ownership relationships, and temporal evolution of ecological resources into a single, clearly defined virtual ecological initial state data through a complete data aggregation process. This ensures that even after the virtual ecological initial state data is removed from the digital twin resource model and enters the blockchain distributed ledger and multi-party cross-node online circulation environment, it can still independently and completely represent the structured initial state information of its corresponding physical ecological resources.

[0051] Preferably, the initial virtual ecosystem state data generated during the aforementioned data aggregation process plays a crucial connecting role in subsequent technical processing. On one hand, the feature evaluation weights carried in this initial virtual ecosystem state data directly constitute the object of correction in subsequent steps for obtaining data exchange guidance parameters from the external system environment and performing weight corrections to obtain the basic feature information of the invoice; that is, the basic feature information of the invoice is generated based on the correction of the feature evaluation weights. On the other hand, the unique data identifier carried in this initial virtual ecosystem state data is directly inherited as the unique data identifier of the virtual ecosystem invoice during the subsequent generation of the virtual ecosystem invoice, thus establishing an immutable traceability link based on cryptographic digests between the initial virtual ecosystem state data and the virtual ecosystem invoice. Furthermore, the lifecycle parameter provides the smart contract deployed on the blockchain distributed ledger with a basis for verifying the timeliness of the virtual ecosystem invoice during subsequent state tracking and flow control—when the smart contract detects that the current time has exceeded the effective range of the baseline time scale and decay function defined by the lifecycle parameter, it will trigger preset state transition or flow restriction rules. Through this structured information carrier design, the initial state data of the virtual ecosystem is not only a static data snapshot, but also an intelligent data carrier that embeds three core semantics: feature evaluation weights, data unique identifiers, and lifecycle parameters. This provides a data connection layer that is complete in information, semantically consistent, and can be directly used as input for subsequent processing actions in the transition from the digital description of physical resources to the distributed circulation of blockchain in the entire online management method of virtual ecosystem invoices.

[0052] Optionally, the step of converting the initial state data of the virtual ecosystem into virtual ecosystem tickets with cross-node circulation attributes includes: Obtain data exchange guidance parameters from the external system environment, and correct the feature evaluation weights corresponding to the ecological attribute feature vectors in the initial state data of the virtual ecosystem according to the data exchange guidance parameters to obtain the basic feature information of the invoice; Assign permission share weights to the initial state data of the virtual ecosystem, associate the permission share weights with the basic feature information of the invoice to obtain the virtual ecosystem invoice, and make the virtual ecosystem invoice carry the permission share weights, the basic feature information of the invoice, and the data unique identifier code corresponding to the initial state data of the virtual ecosystem.

[0053] Preferably, in the initial stage of converting the initial state data of the virtual ecosystem into virtual ecological tickets with cross-node circulation attributes, this application first performs the acquisition of data exchange guidance parameters in the external system environment. These data exchange guidance parameters do not refer to a static configuration table maintained by a single approval flow engine under a centralized database architecture, but rather a set of structured parameters dynamically collected from multiple external digital systems associated with the ecological resource management of the target area. These parameters describe the differences in data caliber and adaptation rules between different accounting systems or circulation interfaces. In traditional centralized solutions, when it is necessary to connect the ecological statistical records in the local ledger with external systems, inconsistencies in the accounting baseline year, physical quantity statistical unit conversion coefficients, and spatial partition granularity used by each party often lead to semantic ambiguity or numerical conflicts when data flows across systems. In this application, the data exchange guidance parameter acquisition technology is implemented by a programmatic thread executing this step sending query requests to the external systems (e.g., water resource quota management platforms, carbon sequestration registration systems, natural resource cadastral databases, etc.) through a preset network interface, and receiving metadata description documents returned by these external systems. This metadata description document includes the current accounting benchmark timestamps used by each external system, a table of physical quantity dimension conversion coefficients, spatial projection coordinate reference system identifiers, statistical granularity level definitions, and data validity period verification rules. This process of acquiring data exchange guidance parameters enables subsequent corrections to the initial state data of the virtual ecosystem to be based on real-time awareness of the data specifications of the external interaction environment, rather than using fixed conversion parameters.

[0054] Preferably, after obtaining the data exchange guidance parameters, this application performs an adaptive correction to the feature evaluation weights carried within the initial state data of the virtual ecosystem to generate basic feature information for the invoice. The technical processing object of this correction operation is the feature evaluation weights written into the weight array field of the initial state data of the virtual ecosystem through data aggregation in the previous steps. The technical logic of this correction operation lies in using the physical quantity dimension conversion coefficient table and statistical granularity hierarchy definition carried in the data exchange guidance parameters to convert the regional ecological structure weight scale represented by the feature evaluation weights, derived from the hierarchical weight derivation calculation within the digital twin resource model, into a comparable feature vector that conforms to the data caliber of the external docking system. In its specific implementation, the correction operation first parses the physical quantity conversion coefficient table in the data exchange guidance parameters. This table defines the conversion coefficients between the original physical quantities (such as cubic meters, tons, and person-times) of ecological product physical quantity data and the equivalent statistical units adopted by the external system. Then, it iterates through each weight element in the feature evaluation weight array, searching for the corresponding conversion coefficient in the physical quantity conversion coefficient table based on the ecological product type of that weight element. The weight element is then multiplied and weighted by the found conversion coefficient. Simultaneously, based on the statistical granularity level definition in the data exchange guidance parameters, the correction operation performs aggregation or allocation operations on weight elements in the feature evaluation weight array that need to be merged or split due to inconsistent spatial granularity. For example, feature evaluation weight elements for multiple fine-grained basic ecological units are merged into a single weight element for a coarse-grained basic ecological unit by area weighting. After this dual adjustment based on dimensional conversion and granularity alignment, the corrected feature evaluation weight array becomes the basic feature information of the invoice. The basic feature information of the ticket is isomorphic to the feature evaluation weight in terms of data structure, but its value has been adapted to the data semantic space of the external system, thus laying the foundation for the consistent interpretation of virtual ecological tickets by different external systems when they are transferred across nodes.

[0055] Preferably, after obtaining the basic characteristic information of the invoice through correction, this application proceeds to the processing stage of allocating permission share weights to the initial state data of the virtual ecosystem. Here, the permission share weight is an array derived from the resource ownership node information, used to quantitatively define the proportional allocation of management permission shares of each participating node to the target monitoring resources associated with the virtual ecosystem in the distributed circulation network. Traditional centralized approval flow engines typically use a fixed one-to-one ownership model when processing voucher issuance, which cannot express multi-level or multi-subject composite management permission structures in a single voucher, leading to the need for offline coordination to divide rights in cross-departmental collaborative management scenarios. In this application, the operation of allocating permission share weights first extracts the ownership deterministic tags, which were pre-determined and written into the resource distribution record during the tagging stage, from the target monitoring resource set corresponding to the initial state data of the virtual ecosystem. Based on the network node identity identifiers and their hierarchical ownership relationships recorded in the ownership deterministic tags, a preset permission configuration strategy table is queried. This permission configuration policy table defines the default share allocation rules among managing nodes under different resource types and multi-level node co-management modes (for example, the primary management unit holds the main management permission, while the superior coordinating node holds the supervision and review permission; these two types of permissions are encoded with different weight values ​​in the share array). The allocation operation generates a permission share weight array with a length corresponding to the number of associated nodes, based on the queried share allocation rules. Each element in this permission share weight array uniquely corresponds to a participating node in the network. This permission share weight array provides a programmable digital expression of permissions for subsequent implementation of share-based access control and flow decisions in smart contracts.

[0056] Preferably, after generating the basic feature information and authority share weights of the invoice, this application performs an operation to associate the authority share weights with the basic feature information of the invoice to ultimately obtain the virtual ecosystem invoice. The technical essence of this association configuration operation is to bind and fill the fields and pointers of the authority share weights, the basic feature information of the invoice, and the unique identifier originally carried by the initial virtual ecosystem state data according to a predefined composite invoice data structure template. Specifically, the association configuration operation creates an invoice data container, which consists of four parts: an invoice header field, a feature field, an authority field, and a traceability identifier field. The operation first writes the entire basic feature information of the invoice into the feature field of the invoice data container; then, after pairing each weight element in the authority share weight array with its corresponding network node identity identifier, it writes it into the authority field of the invoice data container; finally, it completely copies and writes the unique identifier carried in the initial virtual ecosystem state data into the traceability identifier field of the invoice data container. At this point, the null pointer field reserved during the initial virtual ecosystem state data generation stage is filled with index offsets pointing to the authority field and feature field in the invoice data container, thereby establishing a structured association between the initial virtual ecosystem state data and the invoice data container. The virtual ecosystem ticket generated through this association configuration carries three key semantics: permission share weight, basic ticket feature information, and data unique identifier corresponding to the initial state data of the virtual ecosystem. This makes it a cross-node circulating data unit that can be independently addressed, has distinguishable permissions, and adaptable features.

[0057] Preferably, the virtual ecosystem ticket formed during the above-mentioned association configuration process differs in its technical architecture from the traditional approach of storing the voucher content, permission information, and traceability information in three loosely linked database tables. In traditional approaches, once a voucher enters cross-system circulation, multiple external join queries are required to restore the complete semantics of the voucher, which can easily lead to a break in the traceability link due to updates or deletions of intermediate tables. This application, however, uses a one-time association configuration to tightly couple the basic feature information, permission share weight, and data unique identifier code within the same ticket data container. Furthermore, the data unique identifier code stored in the traceability identifier field within this ticket data container is a fixed-length character sequence bound to a content snapshot of a digital twin resource model through cryptographic hashing. Any subsequent modification to the feature field or permission field in this virtual ecosystem ticket will result in a discrepancy between the recalculated data unique identifier code and the original data unique identifier code stored in the traceability identifier field of the ticket data container, which can then be directly detected by smart contracts deployed on the blockchain distributed ledger during the verification process. This tightly coupled data structure and embedded cryptographic digest design enables virtual ecological tickets to have self-verification capabilities during cross-node circulation, providing a reliable data foundation for state tracking and flow control among multiple nodes at the underlying level.

[0058] Preferably, the virtual ecosystem ticket generated through the above processing can directly adapt its internal structure to the key-value storage model or object storage model of the blockchain distributed ledger when subsequently uploaded to it for notarization. When recording the correspondence between the virtual ecosystem ticket and the initial state data of the virtual ecosystem, the blockchain distributed ledger uses the unique data identifier as the primary index key and stores the serialized byte stream of the ticket data container as the value. Simultaneously, when the smart contract reads the virtual ecosystem ticket for state tracking and online flow control, it can directly parse the permission share weight array of each participating node from the permission field of the ticket data container to implement share-based access control logic; parse the basic feature information of the ticket from the feature field of the ticket data container to adapt to the data standards of different external systems; and read the unique data identifier from the traceability identifier field of the ticket data container to reverse-look up the original state of the initial state data of the virtual ecosystem corresponding to the virtual ecosystem ticket in the digital twin resource model. This structured transformation path from initial virtual ecosystem data to virtual ecosystem tickets establishes a clear data inheritance and semantic enhancement channel between the physical ecological attribute information carried by the initial virtual ecosystem data and the permission adaptation information and environmental adaptation information required for cross-node circulation. This provides a structured data carrier for the entire blockchain-based online management method of virtual ecosystem tickets to achieve trusted, adaptable, and traceable online circulation in a distributed environment.

[0059] Optionally, the step of invoking a smart contract deployed on the blockchain distributed ledger to perform state tracking of the virtual ecosystem ticket includes: Listen to access requests in the blockchain distributed ledger and identify the account permission level corresponding to the access request; The current ownership node status of the virtual ecological ticket is retrieved according to the account permission level, and the operation log of the virtual ecological ticket is updated synchronously according to the access request and the account permission level, so that the current ownership node status of the virtual ecological ticket and the operation log of the virtual ecological ticket together constitute the status tracking record of the virtual ecological ticket.

[0060] Preferably, when invoking a smart contract deployed on the blockchain distributed ledger to track the state of virtual ecosystem tickets, the smart contract first listens for and captures access requests in the blockchain distributed ledger. Here, an access request refers to a call transaction initiated by any participating node that has joined the blockchain network, pointing to the smart contract address, through its locally maintained blockchain node. The input data of this call transaction carries operation instruction codes and the participating node's digital signature. Unlike traditional centralized approval flow engines that obtain access requests by polling a central database or message queue, the smart contract in this application runs in an event-driven manner. Its listening logic relies on the event subscription mechanism provided by the underlying blockchain platform. During initialization, the smart contract registers to listen for call events to the smart contract address. When any participating node initiates a call transaction to the smart contract, the blockchain's virtual execution environment delivers the content of the call transaction as an access request to the smart contract's entry function before the call transaction is packaged into a block by consensus. This monitoring mechanism ensures that any query, status query, or transfer of virtual ecosystem tickets will be intercepted by the smart contract as an undeniable access request. The access request itself is included in the immutable record sequence of the blockchain distributed ledger, fundamentally preventing the possibility of the access behavior being denied afterward or the operation log being tampered with.

[0061] Preferably, after the smart contract captures an access request, it immediately parses the account identity information carried in the access request to identify the account permission level corresponding to the access request. Here, the account permission level is not a static permission identifier assigned by the user role table maintained by the central server in the traditional scheme, but a dynamic permission evaluation result comprehensively determined based on the permission share weight array stored in the permission field of the internal ticket data container of the virtual ecosystem ticket, and the account's identity registration information in the blockchain network. The smart contract first recovers the public key of the request initiator's account from the digital signature of the access request, and then derives its account address as the unique identifier of the account. Next, the smart contract reads the target virtual ecosystem ticket pointed to by the calling transaction, parses the permission share weight array from the permission field of the ticket data container of the target virtual ecosystem ticket, and searches for the weight element that matches the account address in the permission share weight array according to the recovered account address. If a match is found, the weight element value is compared with a preset permission level threshold range to assign the corresponding permission level to the account. For example, if the weight element value meets the upper limit of the range, a transferable permission level is granted; if it meets the middle range, query and log viewing permissions are granted; otherwise, only a limited evidence viewing permission level is granted. If no match is found, the default minimum permission level is granted. This processing logic, which directly maps the weight element values ​​in the permission share weight array to the operation permission level, ensures that the smart contract's permission determination for access requests does not rely on off-chain queries and is executed entirely within the on-chain closure, guaranteeing the consistency and transparency of permission judgments.

[0062] Preferably, after identifying the account permission level corresponding to the access request, the smart contract retrieves the current ownership node status of the virtual ecosystem ticket based on the account permission level, and performs a synchronous update of the operation log of the virtual ecosystem ticket according to the operation command code in the access request and the account permission level. Here, the current ownership node status refers to the node identity identifier recorded in a dedicated status field within the ticket data container of the virtual ecosystem ticket in the blockchain distributed ledger, indicating the current jurisdiction or resident node of the virtual ecosystem ticket. The smart contract executes conditional branches based on the account permission level: if the account permission level meets the query requirements, it directly reads the current ownership node status field in the ticket data container and uses it as the search result; if the account permission level meets the transfer initiation requirements and the operation command code is a transfer command, it also reads the current ownership node status field as a pre-stored snapshot before executing subsequent transfer logic. Regardless of the operation type, the smart contract generates an operation log entry consisting of the request timestamp, initiator account address, operation command code, account permission level, and the retrieved current ownership node status. This operation log entry is then appended to an on-chain operation log differential list associated with the virtual ecosystem ticket, thus completing the synchronous update of the operation log. This operation log differential list uses an append-only structure; any written historical operation log entries cannot be deleted or overwritten. Its update process and the retrieval of the current ownership node status are executed atomically within the same call transaction.

[0063] Preferably, while synchronously updating the operation log, if the operation command code of the access request and its corresponding account permission level are accompanied by a change in the current ownership node state of the virtual ecosystem ticket, the smart contract will modify the current ownership node state field in the ticket data container after executing the transfer logic, updating it to the node identity identifier of the target transfer participating node. This update operation and the aforementioned operation log append operation are encapsulated in the same smart contract call transaction, thereby ensuring that the current ownership node state of the virtual ecosystem ticket and the operation log recording the state change process always maintain transaction-level causal consistency. The old ownership node state value before the update, the new ownership node state value after the update, and the access request information that triggered the change are all completely recorded in the synchronously written operation log entries. This design, which forcibly couples state changes and operation log recording into the same atomic transaction, differs from the practice of separating state updates and log recording in traditional centralized databases, which is prone to incomplete operation logs or inconsistent states due to system failures. It provides a solid consistency foundation for state tracking in distributed networks.

[0064] Preferably, the current ownership node status of the virtual ecosystem ticket maintained by the aforementioned smart contract, along with the operation log of the virtual ecosystem ticket, together constitute the state tracking record of the virtual ecosystem ticket. Technically, this state tracking record is a logically unified but physically potentially spanning multiple blocks on-chain data structure, and its query interface is exposed externally through a read-only function of the smart contract. Any participating node with the corresponding account permission level can invoke this read-only function by inputting a unique data identifier to obtain the complete circulation history consisting of a series of operation log entries arranged in chronological order, as well as the latest current ownership node status. This joint query mode based on operation logs and the current ownership node status allows any participating node, upon receiving a virtual ecosystem ticket in circulation, to independently verify the authenticity and continuity of the virtual ecosystem ticket's circulation by replaying the operation logs and verifying the consistency of the state hashes of each batch, without relying on a centralized authentication server or offline database backup.

[0065] Preferably, from an overall technical perspective, the on-chain state tracking mechanism of this application is completely different from the technical solutions in traditional centralized certificate management systems where operation logs and states are separated, making them highly susceptible to tampering by privileged accounts or selective erasure of historical records. Since the logic of the smart contract and the differential list of the operation log are each solidified into immutable states on the blockchain distributed ledger in the form of bytecode or data structures, any addition to the differential list of the operation log or modification of the current state of the node must be verified by a majority of participating nodes in the distributed consensus network and written into a new block. This means that each entry in the operation log naturally carries a block timestamp and transaction index, forming a highly reliable chain of temporal evidence. When a regulatory audit node needs to retrospectively review the cross-node transfers of a virtual ecosystem ticket, it only needs to synchronize complete block data to obtain a state snapshot completely consistent with any historical moment by replaying the state transition sequence of the smart contract. This technically achieves blind-spot-free and non-repudiable online state tracking throughout the entire lifecycle of the virtual ecosystem ticket.

[0066] Optionally, the step of controlling the online circulation of the virtual ecosystem ticket among multiple nodes according to the triggering conditions of the smart contract includes: Identify the network identity attributes of the participating nodes in the multi-party nodes, and compare the network identity attributes with the access rules in the smart contract; If the comparison results are consistent, the current ownership node state transfer operation of the virtual ecological ticket is executed; After the current ownership node state transfer operation of the virtual ecological ticket is completed, a state synchronization instruction is triggered, and the virtual ecological ticket is updated in the distributed consistency of the blockchain distributed ledger based on the state synchronization instruction.

[0067] Preferably, when a smart contract deployed on a blockchain distributed ledger controls the online circulation of virtual ecological tickets among multiple nodes according to its internally preset trigger conditions, the smart contract first identifies the network identity attributes of the participating nodes in the circulation process. Here, a participating node refers to a blockchain network node designated as the recipient or target of the virtual ecological ticket transfer during this online circulation process. The network identity attribute is not simply a network address, but a structured identity tuple composed of the participating node's encrypted public key-derived identifier, the node's registered account address in the blockchain network, and the node's hierarchical affiliation identifier within the resource ownership node information system. When identifying this network identity attribute, the smart contract parses the target participating node's account address from the transaction data carried in the access request that triggered the circulation, then queries the target participating node's pre-registered identity declaration contract or identity certificate on the blockchain to obtain its encrypted public key-derived identifier and hierarchical affiliation identifier, ultimately assembling the participating node's network identity attribute. Unlike traditional centralized approval flow engines that directly use preset usernames or department codes for routing, this application obtains network identity attributes through an on-chain identity declaration contract. This ensures that the identity information of the target node can be verified by the entire network consensus and is tamper-proof, avoiding the technical problem of virtual ecosystem tickets being lost or intercepted during the flow due to fake nodes impersonating the recipient.

[0068] Preferably, after identifying the network identity attributes of the participating nodes, the smart contract compares these attributes with the pre-coded admission rules within the smart contract. These admission rules are not a static routing table or whitelist used in traditional systems, but rather a set of programmable conditions composed of Boolean logic operations. This set of programmable conditions is compiled into the smart contract's bytecode and embedded in the blockchain's distributed ledger, allowing any participating node to audit it. The admission rule's judgment logic first parses the permission share weight array from the permission domain of the virtual ecosystem ticket's ticket data container. It then checks if a weight element in this array matches the account address of the participating node and verifies whether the weight element's value falls within a predefined weight threshold range for accepting transfers (e.g., the weight element value must be higher than the transfer threshold). Simultaneously, the admission rule also reads the lifecycle parameter from the virtual ecosystem ticket's ticket data container and verifies whether the current blockchain timestamp is still within the effective range of the baseline time scale and decay function defined by the lifecycle parameter. If it exceeds this range, the transfer is rejected. Furthermore, the admission rules also verify whether the hierarchical affiliation identifier of the participating node in the transfer has a preset legal transfer path with the node indicated by the current affiliation node status of the virtual ecosystem ticket (e.g., only transfers to same-level or lower-level nodes are allowed). Only when all the above Boolean conditions are true is the comparison result determined to be consistent. This design, which incorporates multiple dimensions such as permission share weight, lifecycle parameters, and hierarchical affiliation identifier into the transfer admission logic, enables smart contracts to autonomously complete a transfer compliance verification from three dimensions—permission, timeliness, and topology—without the intervention of a central arbitrator.

[0069] Preferably, when the comparison result between the network identity attribute and the access rules in the smart contract is consistent, the smart contract immediately executes the current ownership node state transfer operation of the virtual ecosystem ticket in an atomic transaction. The technical essence of this current ownership node state transfer operation is to modify the current ownership node state in the dedicated state field stored in the ticket data container, replacing the original governing ownership node identity identifier with the node identity identifier of the participating node. The execution process of this state transfer operation strictly depends on the smart contract's storage and write instructions. The smart contract first reads the current ownership node state field value in the ticket data container as a snapshot of the old ownership node state, then generates a new current ownership node state value and writes it to the same field, overwriting the old value. Simultaneously, the smart contract records this state transfer event as an operation log entry, appending it to the on-chain operation log differential list associated with the virtual ecosystem ticket. This operation log entry simultaneously includes the old ownership node state snapshot, the new current ownership node state value, the network identity attribute of the participating node, and the transaction hash that triggered the transfer. The state transition operation and the operation log append operation are encapsulated within the atomic execution boundary of the same blockchain call transaction, thereby ensuring that the current ownership node state change of the virtual ecological ticket and its operation log record always maintain an inseparable causal relationship. Any change to this state will leave an irrefutable historical trace on the distributed ledger.

[0070] Preferably, after the current ownership node state transfer operation of the virtual ecosystem ticket is completed—that is, after the current ownership node state field in the ticket data container has been updated to the node identity identifier of the participating node—the smart contract immediately triggers a state synchronization instruction at the end of the current call transaction. Technically, this state synchronization instruction is a contract event log generated by the smart contract code. This contract event log, with its preset structure format, encodes parameters such as the unique identifier of the data involved in this state transfer operation, the new current ownership node state value, and the updated block height into an event notification, which is then written into the receipt tree of the current block. This state synchronization instruction is not a simple memory broadcast; instead, it utilizes the event subscription mechanism of the underlying blockchain platform, enabling all participating nodes in the network that have subscribed to this smart contract event to automatically detect the change in the ownership status of the virtual ecosystem ticket by parsing the contract event log in the block after receiving the new block data on their locally maintained blockchain nodes. This trigger-broadcast-detection link provides a distributed information distribution foundation for multiple nodes to synchronously recognize the latest ownership status of virtual ecological tickets, avoiding the single point bottleneck and message loss risk when relying on a central message queue to send notifications one by one in traditional solutions.

[0071] Preferably, based on state synchronization instructions, this application drives the distributed consistency update of virtual ecological tickets in the blockchain distributed ledger. This distributed consistency update process relies on the consensus protocol of the blockchain network. When a transaction containing state transition operations and state synchronization instructions is packaged into a new block, and this new block is verified by a majority of participating nodes in the blockchain network and appended to the end of their respective local blockchain distributed ledger copies, the updated current ownership node state recorded in this block becomes the new consensus result regarding the ownership status of the virtual ecological ticket in the entire distributed network. When each participating node queries the status of the virtual ecological ticket locally, the latest current ownership node state it reads will point to this new ownership node confirmed by consensus. This mechanism of achieving eventual consistency based on block confirmation fundamentally solves the problem in traditional centralized database replication schemes where different nodes reach contradictory conclusions about the ownership status of the same certificate due to master-slave latency and network partitioning, enabling the circulation result of virtual ecological tickets among multiple nodes to achieve mathematically verifiable global uniqueness.

[0072] Preferably, the aforementioned online transfer control process based on smart contract triggering conditions and blockchain consensus constitutes a complete automated transfer chain in terms of technical architecture, from admission determination to atomic state transfer, and then to event triggering and distributed confirmation. Any participating node can independently verify whether each state transfer operation of the current node strictly follows the admission rules encoded in the smart contract, and whether the transfer operation has obtained final confirmation from the blockchain network, by replaying the transaction sequence corresponding to the smart contract. This transfer mechanism, which combines on-chain permission verification and consensus confirmation, enables the online transfer of virtual ecological tickets among multiple nodes to break free from dependence on centralized transfer schedulers or manual approval processes, achieving autonomous, reliable, and fully auditable cross-node state migration in a trustless multi-party environment.

[0073] Optionally, the blockchain-based online management method for virtual ecological invoices further includes: Ecological environment change parameters of the target area are collected in real time through preset hardware monitoring nodes; The ecological environment change parameters are fed back into the digital twin resource model. The ecological attribute feature vector in the digital twin resource model is dynamically adjusted using the ecological environment change parameters. The basic feature information of the virtual ecological ticket is updated according to the dynamically adjusted ecological attribute feature vector.

[0074] Preferably, during the online circulation of virtual ecological tickets among multiple nodes, this application uses pre-set hardware monitoring nodes to collect ecological environment change parameters of the target area in real time, thereby forming a continuous perception channel for changes in the ecological state of the physical world. These hardware monitoring nodes are not telemetry terminals used in traditional solutions for periodically uploading static ledger data, but rather a set of embedded environmental data acquisition devices deployed in various basic ecological units within the target area. Examples include a soil moisture sensor matrix deployed in forest patches, Doppler flow meters installed on river corridor cross-sections, a photosynthetically active radiation and eddy covariance flux observation tower erected above wetlands, and an infrared array counting terminal for monitoring landscape bearing pressure. Each of these hardware monitoring nodes carries a device registration identifier code bound to a corresponding primary or composite digital object in the digital twin resource model, and samples the physical environmental parameters of the space it covers at a pre-set acquisition frequency (e.g., once every few minutes to once every few hours). The data acquisition process is driven by a microcontroller unit and network communication module built into the hardware monitoring nodes. The raw analog electrical signals are converted into structured ecological environment change parameter data messages through analog-to-digital conversion, sensor calibration curve correction, and edge denoising. These messages carry device registration identifiers, sampling timestamps, and at least one physical environmental parameter value (e.g., soil volumetric water content, runoff velocity, carbon dioxide flux density, number of visitors, etc.). Unlike traditional centralized data collection systems that rely on manual reporting or random satellite inversion, this application achieves multi-dimensional, near real-time low-level data capture of the target area's ecosystem physical state by densely deploying hardware monitoring node arrays in geographic space and registering them with the digital twin resource model. This provides a directly injectable physical world feedback information flow for the state evolution of the digital twin resource model.

[0075] Preferably, after the hardware monitoring nodes generate ecological environment change parameter data packets, this application feeds back the ecological environment change parameters to the digital twin resource model. The technical essence of this feedback operation is an asynchronous data transmission and routing injection process from the physical edge to the digital kernel. Its processing object is the ecological environment change parameter data packets continuously generated by each hardware monitoring node, and its processing target is the state update interface defined by the digital twin resource model during the encapsulation and processing phase. Specifically, each hardware monitoring node sends the ecological environment change parameter data packets to a message broker service deployed at the edge of the blockchain network via communication protocols such as narrowband IoT or low-power wide-area networks. This message broker service verifies the legality of the device registration identifier code and the temporal consistency of the sampling timestamp in the received ecological environment change parameter data packets. After successful verification, it encapsulates the physical environment parameter values ​​in the data packets into parameter update tuples conforming to the input format of the state update interface of the digital twin resource model. These parameter update tuples include the device registration identifier code, the corresponding physical environment parameter type identifier, and the updated physical environment parameter values. Subsequently, the proxy service uses the state update interface exposed by the digital twin resource model as input, routing the parameter update tuple to the model runtime container. Inside the model runtime container, the addressing index, based on the device registration identifier in the parameter update tuple, locates the corresponding attribute field of the primary or composite digital object it is bound to, and writes the updated physical environment parameter value into that attribute field, thus completing a feedback injection of ecological environment change parameters from the hardware monitoring node to the digital twin resource model.

[0076] Preferably, after ecological environment change parameters are continuously injected into the digital twin resource model through the state update interface, the various physical attribute data maintained within the model's runtime container will change accordingly. This application then utilizes these updated physical attribute data to dynamically adjust the ecological attribute feature vectors in the digital twin resource model. The technical logic of this dynamic adjustment operation is to use the updated physical attribute data in the digital twin resource model as input to re-drive the physical quantity accounting and standardization conversion logic encapsulated within the digital twin resource model during its construction phase, in order to generate a set of updated ecological attribute feature vectors reflecting the current state of the physical world. Specifically, when the digital twin resource model receives a batch of parameter update tuples, its internal coordination data layer will trigger a local update calculation for the primary digital object or the composite digital object to which it belongs, based on the system function type of the primary digital object associated with the updated physical attribute field. This local update calculation sequentially generates updated physical quantity data of ecological products through physical quantity verification, then standardizes and converts these data to generate updated standardized values. Finally, the model runtime container replaces or merges these updated standardized values ​​into the ecological attribute feature vectors already existing within the digital twin resource model, according to the dimensions and functional order specified in the original ecological attribute feature vectors. This results in dynamically adjusted ecological attribute feature vectors. This dynamic adjustment mechanism ensures that the ecological attribute feature vectors within the digital twin resource model are no longer static arrays reflecting only an initial snapshot, but rather time-series state variables capable of tracking the evolution of ecological processes in the physical world.

[0077] Preferably, after the ecological attribute feature vector in the digital twin resource model is dynamically adjusted and updated, this application updates the basic feature information of the virtual ecological tickets that have been circulated on the blockchain based on the dynamically adjusted ecological attribute feature vector. The technical processing object of this update operation is the basic feature information of the tickets stored in the feature domain of the ticket data container of the virtual ecological tickets stored on the blockchain distributed ledger. The processing goal is to synchronize the content of the basic feature information of the tickets to a new version consistent with the current physical ecological state. Specifically, this update operation is executed by a state synchronization smart contract deployed on the blockchain distributed ledger and having obtained specific triggering permissions. When the digital twin resource model detects that its internal ecological attribute feature vector has changed and the change exceeds a preset vector distance threshold (e.g., the change exceeds a fraction of the original vector magnitude), the model running container publishes an ecological attribute feature vector update event through its lifecycle event interface. This update event carries the dynamically adjusted ecological attribute feature vector. An off-chain coordination process or oracle network captures the ecological attribute feature vector update event. Following the same data exchange guidance parameter correction logic as the virtual ecological ticket generation phase, it performs dimensional adaptation and granular alignment on each updated standardized value in the dynamically adjusted ecological attribute feature vector, generating updated ticket basic feature information. It then constructs a call transaction pointing to the state synchronization smart contract and sends it to the blockchain network. After verifying the caller's permissions and the cryptographic proof of the event's origin, the state synchronization smart contract writes the updated ticket basic feature information carried in the call transaction into the feature field of the target virtual ecological ticket's ticket data container, overwriting the old ticket basic feature information.

[0078] Preferably, the aforementioned update operations on the basic characteristic information of the virtual ecological ticket share the same operation log differential list recording mechanism on the blockchain distributed ledger as the state tracking and circulation control of the virtual ecological ticket. Each time the basic characteristic information of the ticket is updated, the state synchronization smart contract synchronously generates an operation log entry. This operation log entry includes the update timestamp, the digest hash value of the basic characteristic information of the ticket before and after the update, and the hash index of the ecological attribute feature vector update event that triggered the update. This operation log entry is then appended to the operation log differential list associated with the virtual ecological ticket. This design, which includes the update event of the basic characteristic information of the ticket in the state tracking record, allows any participating node, when querying the complete state of the virtual ecological ticket through the smart contract, not only to know the current ownership node state flow history of the virtual ecological ticket among the participating nodes, but also to trace the various version iterations of the basic characteristic information carried by the virtual ecological ticket throughout its circulation cycle in response to changes in the ecological environment. This traceable evolution chain of basic characteristics of invoices solves a common but overlooked problem in traditional solutions at the technical level: after the issuance of ecological invoices, the attribute information representing the function of ecological products gradually becomes distorted over time, but there is a lack of verifiable update mechanisms and change history.

[0079] In summary, by employing the complete technical path described above—real-time data collection by hardware monitoring nodes, feedback to the digital twin resource model, dynamic adjustment of ecological attribute feature vectors, and subsequent updating of the basic feature information of virtual ecological tickets—this application constructs a dynamically coupled channel with continuously isomorphic states among the physical ecosystem, the digital twin resource model, and the virtual ecological tickets on the blockchain distributed ledger. Unlike traditional centralized solutions where physical monitoring data, digital model records, and voucher circulation data operate in isolated technology stacks and are only occasionally synchronized via offline files or batch scripts, leading to information delays and inconsistencies, this application designs this channel as a seamless pipeline based on hardware event triggering, digital model calculations, and on-chain state update instructions. When physical changes occur in the target area's ecological environment, such as reduced rainfall leading to decreased water conservation capacity or increased carbon sequestration capacity due to forest growth, these changes, perceived by the hardware monitoring nodes, will be automatically transmitted sequentially to the ecological attribute feature vectors of the digital twin resource model and the basic feature information of the virtual ecological tickets on the blockchain distributed ledger. This mechanism ensures from the data source that the quantitative results of the ecological attributes reflected by the virtual ecological tickets circulating among multiple nodes are highly aligned with the actual physical ecological state of the target area in terms of time series, making it traceable and verifiable. This provides technical support for achieving data-consistent online management based on real ecological dynamics in a distributed trust environment.

[0080] like Figure 3 As shown in the figure, an online management device for virtual ecological invoices based on blockchain is provided in this application embodiment, which includes: An ecological attribute feature vector extraction module is used to acquire ecological resource distribution data of a target area, and to quantify the ecological resource distribution data based on a preset ecological product gross value accounting standard to extract ecological attribute feature vectors. The ecological attribute feature vectors are used to characterize the ecological attribute quantification results formed by the ecological resource distribution data under the preset ecological product gross value accounting standard. The virtual ecological initial state data generation module is used to construct a digital twin resource model corresponding to the target area based on the ecological attribute feature vector, and to generate virtual ecological initial state data corresponding to the ecological resource distribution data and the ecological attribute feature vector using the digital twin resource model; The virtual ecosystem ticket notarization module is used to convert the initial state data of the virtual ecosystem into virtual ecosystem tickets with cross-node circulation attributes, and upload the virtual ecosystem tickets to the blockchain distributed ledger for notarization, so that the blockchain distributed ledger records the correspondence between the virtual ecosystem tickets and the initial state data of the virtual ecosystem; The virtual ecosystem ticket circulation management module is used to call the smart contract deployed on the blockchain distributed ledger to track the status of the virtual ecosystem tickets, control the online circulation of the virtual ecosystem tickets among multiple nodes according to the triggering conditions of the smart contract, and record the online circulation status of the virtual ecosystem tickets among the multiple nodes based on the blockchain distributed ledger.

[0081] like Figure 4 As shown, an electronic device according to an embodiment of this application includes: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the program stored in the memory to implement the functions of each module of the device described in the embodiment of this application, or to implement the steps of the method described.

[0082] Figures 2-4 For an exemplary description, please refer to the above. Figure 1 This will not be elaborated upon here.

Claims

1. A blockchain-based online management method of virtual ecological tickets, characterized in that, Includes the following steps: The ecological resource distribution data of the target area is obtained, and the ecological resource distribution data is quantified based on the preset ecological product gross value accounting standard to extract ecological attribute feature vectors. The ecological attribute feature vectors are used to characterize the ecological attribute quantification results formed by the ecological resource distribution data under the preset ecological product gross value accounting standard. A digital twin resource model corresponding to the target area is constructed based on the ecological attribute feature vector, and virtual ecological initial state data corresponding to the ecological resource distribution data is generated using the digital twin resource model. The initial state data of the virtual ecosystem is converted into virtual ecosystem tickets with cross-node circulation attributes, and the virtual ecosystem tickets are uploaded to the blockchain distributed ledger for storage, so that the blockchain distributed ledger records the correspondence between the virtual ecosystem tickets and the initial state data of the virtual ecosystem. The system invokes a smart contract deployed on the blockchain distributed ledger to track the status of the virtual ecological tickets, controls the online circulation of the virtual ecological tickets among multiple nodes according to the triggering conditions of the smart contract, and records the online circulation status of the virtual ecological tickets among the multiple nodes based on the blockchain distributed ledger. 2.The blockchain-based virtual ecological ticket online management method of claim 1, wherein, Before the step of acquiring the ecological resource distribution data of the target area, the method further includes: Feature identification is performed on the target monitoring resources within the target area to obtain resource idle status data and resource ownership node information corresponding to the target monitoring resources; The target monitoring resources are tagged based on the resource idle status data and the resource ownership node information to determine the target monitoring resource set that meets the data extraction conditions, and the resource distribution record corresponding to the target monitoring resource set is used as the ecological resource distribution data of the target area.

3. The online management method for virtual ecological tickets based on blockchain as described in claim 1, characterized in that, The step of quantifying the ecological resource distribution data based on the preset ecological product gross value accounting standard and extracting ecological attribute feature vectors includes: Identify the system function type in the ecological resource distribution data, and map the system function type to the adjustment service accounting dimension, supply service accounting dimension or cultural service accounting dimension corresponding to the preset ecological product total value accounting specification; Under the accounting dimensions of regulation services, supply services, or cultural services, physical quantity accounting processing is performed on the ecological resource distribution data to generate physical quantity data of ecological products. The ecological attribute feature vector is obtained by standardizing and converting the physical quantity data of the ecological products.

4. The online management method for virtual ecological tickets based on blockchain as described in claim 1, characterized in that, The step of constructing a digital twin resource model corresponding to the target area based on the ecological attribute feature vector includes: Obtain the geospatial topology of the target area, and inject the ecological attribute feature vector into the geospatial topology to obtain the geospatial topology injected with the ecological attribute feature vector; A mapping logic for transforming resources into digital objects is established. Based on the mapping logic, the geospatial topological relationship injected with the ecological attribute feature vector is encapsulated and processed to generate the digital twin resource model.

5. The online management method for virtual ecological tickets based on blockchain as described in claim 1, characterized in that, The step of generating virtual initial state data of the ecosystem corresponding to the ecological resource distribution data using the digital twin resource model includes: Extract the data unique identifier, the feature evaluation weight corresponding to the ecological attribute feature vector, and the life cycle parameter corresponding to the ecological resource distribution data from the digital twin resource model; The data unique identifier, the feature evaluation weight corresponding to the ecological attribute feature vector, and the life cycle parameter corresponding to the ecological resource distribution data are aggregated to generate the virtual ecological initial state data, so that the virtual ecological initial state data carries the data unique identifier, the feature evaluation weight corresponding to the ecological attribute feature vector, and the life cycle parameter corresponding to the ecological resource distribution data.

6. The online management method for virtual ecological tickets based on blockchain as described in claim 1, characterized in that, The step of converting the initial state data of the virtual ecosystem into virtual ecosystem tickets with cross-node circulation attributes includes: Obtain data exchange guidance parameters from the external system environment, and correct the feature evaluation weights corresponding to the ecological attribute feature vectors in the initial state data of the virtual ecosystem according to the data exchange guidance parameters to obtain the basic feature information of the invoice; Assign permission share weights to the initial state data of the virtual ecosystem, associate the permission share weights with the basic feature information of the invoice to obtain the virtual ecosystem invoice, and make the virtual ecosystem invoice carry the permission share weights, the basic feature information of the invoice, and the data unique identifier code corresponding to the initial state data of the virtual ecosystem.

7. The online management method for virtual ecological tickets based on blockchain as described in claim 1, characterized in that, The step of invoking a smart contract deployed on the blockchain distributed ledger to perform state tracking of the virtual ecosystem ticket includes: Listen to access requests in the blockchain distributed ledger and identify the account permission level corresponding to the access request; The current ownership node status of the virtual ecological ticket is retrieved according to the account permission level, and the operation log of the virtual ecological ticket is updated synchronously according to the access request and the account permission level, so that the current ownership node status of the virtual ecological ticket and the operation log of the virtual ecological ticket together constitute the status tracking record of the virtual ecological ticket.

8. The online management method for virtual ecological tickets based on blockchain as described in claim 1, characterized in that, The step of controlling the online circulation of the virtual ecological tickets among multiple nodes according to the triggering conditions of the smart contract includes: Identify the network identity attributes of the participating nodes in the multi-party nodes, and compare the network identity attributes with the access rules in the smart contract; If the comparison results are consistent, the current ownership node state transfer operation of the virtual ecological ticket is executed; After the current ownership node state transfer operation of the virtual ecological ticket is completed, a state synchronization instruction is triggered, and the virtual ecological ticket is updated in the distributed consistency of the blockchain distributed ledger based on the state synchronization instruction.

9. The online management method for virtual ecological tickets based on blockchain as described in any one of claims 1-8, characterized in that, The blockchain-based online management method for virtual ecological invoices also includes: Ecological environment change parameters of the target area are collected in real time through preset hardware monitoring nodes; The ecological environment change parameters are fed back into the digital twin resource model. The ecological attribute feature vector in the digital twin resource model is dynamically adjusted using the ecological environment change parameters. The basic feature information of the virtual ecological ticket is updated according to the dynamically adjusted ecological attribute feature vector.

10. A blockchain-based online management device for virtual ecological invoices, characterized in that, include: An ecological attribute feature vector extraction module is used to acquire ecological resource distribution data of a target area, and to quantify the ecological resource distribution data based on a preset ecological product gross value accounting standard to extract ecological attribute feature vectors. The ecological attribute feature vectors are used to characterize the ecological attribute quantification results formed by the ecological resource distribution data under the preset ecological product gross value accounting standard. The virtual ecological initial state data generation module is used to construct a digital twin resource model corresponding to the target area based on the ecological attribute feature vector, and to generate virtual ecological initial state data corresponding to the ecological resource distribution data and the ecological attribute feature vector using the digital twin resource model; The virtual ecosystem ticket notarization module is used to convert the initial state data of the virtual ecosystem into virtual ecosystem tickets with cross-node circulation attributes, and upload the virtual ecosystem tickets to the blockchain distributed ledger for notarization, so that the blockchain distributed ledger records the correspondence between the virtual ecosystem tickets and the initial state data of the virtual ecosystem; The virtual ecosystem ticket circulation management module is used to call the smart contract deployed on the blockchain distributed ledger to track the status of the virtual ecosystem tickets, control the online circulation of the virtual ecosystem tickets among multiple nodes according to the triggering conditions of the smart contract, and record the online circulation status of the virtual ecosystem tickets among the multiple nodes based on the blockchain distributed ledger.

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