Garden planning scheme optimization method and system using digital modeling and collaboration mechanism

Abstract

The application relates to the computer technical field and discloses a garden planning scheme optimization method and system using digital modeling and a cooperation mechanism, aiming to solve the technical problems of chaotic version management, difficult contribution identification, copyright ownership disputes, data tampering risks and insufficient intellectual property protection in multi-party cooperation. The method comprises the following steps: obtaining a three-dimensional digital model of a garden planning scheme and analyzing graphic element data, and storing characteristic graphic elements with intellectual property value on a chain; capturing modification operations of the digital model by a cooperation party and generating block data. The system comprises a model analysis module, a graphic element chain module, an operation capture and block generation module, an intelligent contract execution module, a version verification module and a cooperation interface module. Through the technical scheme, the application can realize reliable cooperation management, quantitative identification of contribution, integrity protection of version data and effective protection of intellectual property.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, specifically relating to a method and system for optimizing landscape planning schemes using digital modeling and collaborative mechanisms. Background Technology

[0002] With the acceleration of urbanization and the increasing demands for ecological environment, the landscaping industry has entered a new stage of large-scale and intensive development. Large-scale landscaping projects involve deep integration of multiple professional fields such as topography, vegetation configuration, landscape architecture, and water systems. Their implementation requires the collaborative participation of multiple stakeholders, including the client, landscape designer, architect, and construction company. In actual projects, all parties typically use computer-aided design software to complete 3D digital modeling of the landscaping plan and use the model data for plan review, construction drawing refinement, and project budget preparation. As the core carrier of information throughout the entire lifecycle of a landscaping project, the digital model runs through all stages from conceptual design to final acceptance, and its accuracy and completeness directly affect project quality and implementation efficiency.

[0003] Among them, the collaborative compilation process of garden planning schemes faces prominent data management challenges. Specifically: (1) Multi-party collaborative modifications lead to redundant model versions, and the design results generated by different participants at different times are difficult to trace and distinguish effectively, which can easily lead to version overwriting and data loss; (2) Due to the lack of a reliable modification record mechanism, the creative contributions of each participant are difficult to quantify and identify, and disputes over the ownership of results and copyrights frequently occur, which in turn affects the reasonable allocation of design fees; (3) Traditional version management adopts a centralized storage model, and the administrator has the highest modification authority over the data. The data is at risk of being tampered with during transmission and storage, and it is difficult for the participants to establish a trust relationship; (4) Landscape pavilions, sculptures and other distinctive graphic elements in garden projects often embody the core creativity of designers, and their intellectual property protection lacks effective technical means. In response to the above technical problems, the existing technology mainly archives data through version management servers and permission control mechanisms, but the inherent defects of the centralized architecture make it impossible to fundamentally solve the trust problem in multi-party collaboration. It is urgent to introduce a new technical architecture to achieve reliable collaborative management. Summary of the Invention

[0004] The purpose of this invention is to provide a method and system for optimizing landscape planning schemes using digital modeling and collaborative mechanisms, which can effectively solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a method and system for optimizing landscape planning schemes using digital modeling and collaborative mechanisms, comprising the following specific steps: S1: Obtain the 3D digital model of the landscape planning scheme, parse the graphic data in the model, and select characteristic graphic elements with intellectual property value for on-chain storage; S2: Capture the collaborators' modifications to the digital model, encapsulate the time, subject, graphic elements, and content of the modifications into an operation record, and generate block data; S3: Based on smart contracts, according to preset contribution calculation rules, the creative contributions of each collaborator are quantitatively evaluated, and a design fee allocation plan is generated. S4: Based on a hash tree structure, perform integrity verification on historical versions of the digital model to ensure the credibility of the collaboration process.

[0006] Furthermore, in step S1, the image data includes the geometric information, material properties, and design parameters of the image element; the distinctive image elements with intellectual property value include at least one of landscape pavilions, sculptures, water features, and special vegetation configurations; the on-chain storage includes generating a unique hash identifier for each image element and permanently recording the image data, its hash identifier, and related ownership information on the blockchain.

[0007] Further, in step S2, the modification operation includes at least one type of element addition, deletion, attribute modification, and position adjustment; the method of capturing modification operations is as follows: when a collaborating party modifies the digital model through the collaboration platform, the relevant information of the modification operation is recorded in real time, and the information is encapsulated into structured data; the method of generating block data is as follows: the hash value of the previous block and the structured data of the current operation are hashed to generate the hash value of the new block, and the new block is added to the end of the blockchain.

[0008] Furthermore, in step S3, the contribution calculation rules include modification type weight coefficient, modification range weight coefficient, and time decay coefficient; the smart contract performs a quantitative evaluation of the contribution of each collaborator according to a preset contribution calculation formula. This calculation formula comprehensively considers the modification operation type, the scope of influence, and the time decay factor, and performs a weighted calculation on each modification operation through a preset weight function to generate a total quantitative value of contribution for each collaborator.

[0009] Furthermore, in step S4, the hash tree adopts a Merkle tree structure; each block contains multiple operation records as leaf nodes, and the hash values ​​of adjacent leaf nodes are paired up and hashed to obtain the parent node, and the process is recursively executed until the root node is obtained; the integrity verification includes extracting all block data in the blockchain, recalculating the root hash value of each block, and comparing it with the original stored root hash value.

[0010] Preferably, the operation type weight function is set according to the following rules: original design operations modify a corresponding preset first weight value, optimization and adjustment operations modify a corresponding preset second weight value, error correction operations modify a corresponding preset third weight value, and reference-seeking operations modify a corresponding preset fourth weight value. A preset proportional relationship is maintained between each weight value to ensure that the impact of different types of operations on the contribution conforms to the preset evaluation logic.

[0011] Preferably, the range weight function is determined based on the number of graphic elements affected by the modification: when the number of graphic elements affected by the modification is within a preset first range, the range weight is a preset first range weight value; when the number of graphic elements affected by the modification is within a preset second range, the range weight is a preset second range weight value; when the number of graphic elements affected by the modification is within a preset third range or higher, the range weight is a preset third range weight value. The boundary of the range is preset according to the actual application scenario.

[0012] Preferably, the time decay factor is set according to a preset time decay rule. The value of the time decay factor directly affects the decay rate of early contributions over time. A larger time decay factor indicates a faster decay rate of early contributions, and vice versa. The specific value of the time decay factor is preset and adjusted according to the periodic characteristics of the collaborative project and the timeliness requirements of the contributions.

[0013] Preferably, the design fee allocation scheme is determined according to the proportion of each collaborator's contribution to the total contribution: the allocation ratio of each collaborator is calculated based on the ratio of its contribution quantification value to the sum of the contribution quantification values ​​of all collaborators, and the calculation result of the allocation ratio is directly used as the basis for the design fee that each collaborator should receive.

[0014] Preferably, the hash algorithm is a hash algorithm with a preset security strength, or adopts the current mainstream secure hash algorithm standard.

[0015] Preferably, it also includes a graphic element intellectual property protection module, which is used to record the creation information, ownership statement and authorization record of the graphic element when it is put on the chain, and to verify its uniqueness through hash identifier; when it is necessary to verify the ownership of the graphic element, the hash value of the current graphic element data is calculated and compared with the hash identifier stored on the chain.

[0016] Preferably, it also includes a distributed storage module, which stores the graph data on the chain in a distributed manner across multiple participating nodes, and ensures that the graph data can still be fully recovered in the event of failure of some nodes.

[0017] Furthermore, this invention also provides a landscape planning scheme optimization system utilizing digital modeling and collaboration mechanisms, comprising: a model parsing module for acquiring a three-dimensional digital model of the landscape planning scheme and parsing its graphic data; a graphic element on-chain module for selecting distinctive graphic elements with intellectual property value and storing them on the blockchain; an operation capture and block generation module for capturing the modification operations of collaborators on the digital model and generating block data; a smart contract execution module for quantitatively evaluating the creative contributions of each collaborator according to contribution calculation rules and generating a design fee allocation scheme; a version verification module for verifying the integrity of historical versions of the digital model based on a hash tree structure; and a collaboration interface module for receiving and parsing operation instructions from each collaborator.

[0018] The garden planning scheme optimization method and system provided by this invention essentially constructs a complete digital garden design collaborative ecosystem. The core of this system lies in the deep integration of traditional garden planning and design with modern blockchain technology. It achieves three-dimensional visualization of garden schemes through digital modeling technology and enables collaborative work among multiple participants through a collaborative mechanism.

[0019] In step S1, acquiring the 3D digital model is the data foundation of the entire system. This 3D digital model can originate from garden design schemes generated by various modeling tools, containing rich graphic data. This graphic data covers the geometric shape information, material surface characteristic parameters, and various parameter settings of various constituent elements in the garden design. In practical applications, distinctive graphic elements with intellectual property value are usually the most innovative and artistic design elements in the garden design scheme, such as unique pavilion shapes, meticulously designed sculptures, creative water feature layouts, and special vegetation configuration schemes. When storing these distinctive graphic elements on the blockchain, the system generates a unique hash identifier for each graphic element. This hash identifier, along with the complete data and ownership information of the graphic element, is recorded on the blockchain, forming an immutable intellectual property certificate.

[0020] In step S2, the capture of modification operations and block generation are key mechanisms for achieving traceability of the collaboration process. When multiple collaborators modify the same digital model through the collaboration platform, the system monitors and records relevant information for each modification operation in real time. This information includes the time point of the modification operation, the collaborating party making the modification, the modified primitive object, and the specific modification content. The system encapsulates this information into structured operation record data and processes the hash value of the previous block with the current operation record data through hash operations to generate the hash value of a new block, thereby appending the new operation record to the end of the blockchain. This chain-like data structure ensures that every modification operation is permanently recorded and cannot be tampered with afterwards.

[0021] In step S3, the quantitative evaluation of contribution is the core technology for solving the problem of benefit distribution in multi-party collaboration. This module automatically executes the calculation process through smart contracts based on preset contribution calculation rules. The contribution calculation rules comprehensively consider three dimensions: modification operation type, scope of influence of modification, and time decay effect. Modification operation type reflects the degree of innovation of the collaborator's contribution. Original design modifications are generally considered to have high innovation value and are therefore given a high weight coefficient; optimization and adjustment modifications improve the solution, but their degree of innovation is relatively low, so they are given a lower weight coefficient; error correction modifications mainly correct problems in the original solution, and their contribution is relatively limited; reference consulting operations mainly provide reference information for other collaborators, and their contribution is relatively the lowest. The scope of influence of modification reflects the amount of work done by the collaborator. The more primitives involved in the modification, the greater the workload usually is. The time decay effect takes into account that early contributions may be gradually diluted by subsequent work as the project progresses, so a time decay factor is introduced, making recent contributions have a higher weight than early contributions. Ultimately, the total contribution of each collaborator is obtained by weighted summation using a preset calculation formula, and the smart contract generates a design fee allocation scheme based on the contribution ratio of each collaborator.

[0022] In step S4, version integrity verification based on a hash tree structure ensures the trustworthiness of the collaboration process. A hash tree, also known as a Merkle tree, is a tree-like data structure where leaf nodes are the hash values ​​of individual operation records, and non-leaf nodes are combined hashes of their child nodes. Multiple operation records within each block serve as leaf nodes, and parent nodes are obtained by pairing them up and performing hash operations, recursively executing this process until the root node is reached. When integrity verification of historical versions of the digital model is required, the system extracts all block data from the blockchain, recalculates the root hash value of each block according to the same Merkle tree structure, and compares it with the originally stored root hash value. If they match, it indicates that the historical version data has not been tampered with; if they do not match, it indicates a data integrity issue, requiring further investigation and processing.

[0023] In the system architecture provided by this invention, the various functional modules collaborate to support the optimization process of the landscape planning scheme. The model parsing module is responsible for acquiring the 3D digital model from the outside and performing in-depth analysis to extract the graphic data; the graphic element on-chain module is responsible for selecting distinctive graphic elements with intellectual property value and storing them on the blockchain; the operation capture and block generation module is responsible for monitoring the modification operations of collaborators in real time and generating corresponding block data; the smart contract execution module is responsible for quantitative evaluation according to preset contribution calculation rules and generating a fee allocation scheme; the version verification module is responsible for verifying the integrity of historical versions; and the collaboration interface module is responsible for receiving and parsing the operation instructions issued by each collaborator through the user interface.

[0024] The technical solution of this invention can be widely applied to multi-party collaborative scenarios in various landscape planning projects, including but not limited to: multi-disciplinary design collaboration in large-scale municipal landscape projects, multi-team collaboration in scenic area planning and design, collaboration between developers and design units in residential community landscape design, and multi-institutional collaboration in the restoration and protection of historical gardens. In these application scenarios, this invention can effectively solve problems such as chaotic version management, difficulty in identifying contributions, and copyright disputes existing in traditional collaboration models, providing technical support for the healthy development of the landscape design industry.

[0025] Compared with the prior art, the present invention has the following beneficial effects: Achieve trusted collaborative management. By introducing blockchain technology into the multi-party collaborative process of landscape planning, each modification generates an immutable block record, effectively solving the problems of version management chaos and data traceability difficulties caused by multi-party collaborative modifications.

[0026] It enables the quantification of contributions. Based on smart contract-based contribution calculation rules, it can objectively quantify the creative contributions of each collaborating party, resolve disputes over ownership of results and copyright, and provide a reliable technical basis for the reasonable allocation of design fees.

[0027] This ensures the integrity of version data. A hash tree structure is used to verify historical versions, ensuring that no historical state of the digital model can be tampered with during the collaboration process, thus strengthening the trust relationship between participants.

[0028] Achieve effective protection of intellectual property rights. Store distinctive graphic elements such as pavilions and sculptures on the blockchain to provide credible ownership proof and technical protection for core creative ideas in garden design. Attached Figure Description

[0029] Figure 1 This is a schematic diagram of the overall technical solution architecture proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the contribution measurement and evaluation algorithm based on smart contracts in this invention; Figure 3 This is a flowchart illustrating the processing framework for the parsing of three-dimensional digital models and the on-chain storage of distinctive primitives in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationship and data flow between the collaborative party's modification operation capture and the generation of blockchain blocks in this invention; Figure 5 This is a schematic diagram illustrating the historical version integrity verification principle based on a hash tree structure in this invention. Detailed Implementation

[0030] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0031] Please refer to Figures 1 to 5 The core of the garden planning scheme optimization method and system proposed in this invention, which utilizes digital modeling and collaborative mechanisms, lies in constructing a complete digital garden design collaborative ecosystem. This system deeply integrates traditional garden planning and design with modern blockchain technology, achieving visual representation of garden schemes through 3D digital modeling technology and enabling collaborative work among multiple participants through a distributed collaboration mechanism. In the entire technical architecture, the data flow chain covers the entire lifecycle process from original model acquisition, primitive extraction, smart contract execution, blockchain storage to version verification. The specific implementation methods of this invention will be described in detail below from two dimensions: static system architecture and dynamic method flow.

[0032] The landscape planning optimization system adopted in this invention employs a distributed architecture design for its hardware platform, consisting of four main categories of hardware units: core computing nodes, storage nodes, collaborative terminal devices, and network communication devices. The core computing nodes are deployed in data centers or cloud service provider computing clusters, undertaking core computing tasks such as model parsing, smart contract execution, and blockchain consensus computation. Each core computing node is equipped with at least eight processors, using server-grade processors based on ARM architecture or high-performance processors based on x86 architecture, with a clock speed of no less than 3 GHz to meet the real-time requirements of large-scale image and data processing and blockchain hash operations. The core computing nodes have a memory configuration of no less than 64 gigabytes, employing dual-channel DDR4 or DDR5 memory modules to ensure data caching efficiency when processing complex landscape models. The core computing nodes are also equipped with enterprise-grade solid-state drives supporting PCIe 4 interfaces, with a single drive capacity of no less than 2.5 TB, serving as a high-speed cache and hot data storage medium.

[0033] The storage nodes employ a distributed storage architecture, configured with dedicated storage server clusters. Each storage node is equipped with an independent storage controller and utilizes redundant disk array technology, featuring at least twelve enterprise-grade hard drives with a capacity of at least eight TB, forming a RAID six storage array to simultaneously meet the requirements for data redundancy and storage capacity. Data synchronization between storage nodes is achieved via a network, using Gigabit Ethernet or higher-speed InfiniBand interconnection to ensure rapid read and write of graph data in the distributed storage environment. Storage nodes are also equipped with independent backup media, including tape libraries or Blu-ray disc libraries, for long-term archiving of cold data.

[0034] The collaborative terminal devices include three types: designer workstations, client / owner query terminals, and construction company visualization terminals. Designer workstations are equipped with professional-grade graphics processors with at least 16GB of video memory, supporting real-time rendering and interactive editing of 3D models. Client / owner query terminals and construction company visualization terminals are equipped with medium-performance graphics processors, supporting 3D model viewing and annotation functions, but not performing complex model editing operations. All collaborative terminal devices connect to the management server via a network interface, using encrypted HTTPS or WSS protocols to ensure the security of collaborative data transmission.

[0035] Network communication equipment comprises three layers: core switches, aggregation switches, and access switches. Core switches utilize high-end technology supporting 40 Gigabit or 100 Gigabit Ethernet, possessing line-speed forwarding capabilities to ensure high-speed interconnection between compute and storage nodes. Aggregation switches are deployed at the top of each rack, connecting access switches and core switches. Access switches are deployed in each collaborating terminal area, providing gigabit Ethernet access services. Link redundancy is achieved between network communication devices using Spanning Tree Protocol (STP) or Equal Cost Multipath Protocol (ECM), ensuring that a single point of failure does not affect the overall network connectivity.

[0036] The system software architecture of this invention is divided into four layers: infrastructure layer, data service layer, business logic layer, and user interaction layer. The infrastructure layer is responsible for providing underlying computing resource scheduling, storage resource allocation, and network communication services. This layer adopts containerized deployment technology, encapsulating each microservice component into Docker containers, and using the Kubernetes container orchestration platform for automated deployment and scaling management. The data service layer is responsible for providing data-related services such as graph data management, blockchain ledger storage, and smart contract runtime environment. This layer is built using a distributed database and blockchain middleware. The business logic layer includes six core modules: model parsing module, graph element on-chain module, operation capture and block generation module, smart contract execution module, version verification module, and collaboration interface module. These modules communicate asynchronously through message queues, achieving a loosely coupled design. The user interaction layer is responsible for providing graphical interfaces and application programming interfaces (APIs) for various user types. This layer supports both web browser and desktop client access methods.

[0037] The model parsing module is the data entry point for the entire system, responsible for extracting metadata from 3D garden model files exported from external modeling software. This module supports parsing various mainstream 3D model file formats, including FBX, OBJ, DAE, and CAD software-specific DWG and DXF formats. Internally, the module integrates a professional-grade 3D model parsing engine capable of accurately identifying various primitive elements in the model, including pavilions, bridges, sculptures, water features, vegetation, paving, and lighting fixtures. When reading the model file, the parsing engine first establishes a scene graph structure in memory, then recursively traverses all nodes in the scene graph, extracting the geometric information, material properties, and design parameters of each primitive node. Geometric information includes a list of 3D vertex coordinates, vertex normals, texture coordinates, and face indices for the primitive. This data is stored in memory in a compact binary format to reduce memory usage for subsequent processing. Material properties include PBR rendering parameters such as diffuse color, specular color, emissive color, transparency, roughness, and metallicity. Design parameters include business-related attributes such as the primitive's dimensions, installation height, orientation angle, and functional classification.

[0038] The image element on-chain module is responsible for writing the selected characteristic image element data into the blockchain, realizing the trusted storage of intellectual property rights. This module exchanges data with the model parsing module through an internal interface. After receiving the image element data list output by the model parsing module, it identifies characteristic image elements with intellectual property value according to preset filtering rules. The filtering rules are based on the type and attributes of the image elements. For example, image elements such as landscape pavilions, sculptures, water features, and special vegetation configurations are judged by default to have intellectual property value. Other types of image elements, such as standard paving and conventional streetlights, are not processed for on-chain storage. For characteristic image elements judged to have intellectual property value, the module executes the on-chain storage process: first, the complete data of the image element is serialized into JSON or binary format; then, the hash calculation interface of the blockchain node is called to generate a unique hash identifier for the image element data; next, the image element data, hash identifier, creation information, ownership statement, and authorization record are packaged into a transaction payload; finally, the transaction is written into the ledger through the consensus mechanism of the blockchain network.

[0039] The operation capture and block generation module is responsible for real-time monitoring of collaborators' modification operations and generating corresponding block data. This module adopts an event-driven architecture, responding immediately when an editing operation is triggered on the collaboration platform. Internally, an operation listener component is deployed. This component is hooked to the core of the collaboration platform's 3D editor. When operations such as adding, deleting, modifying attributes, or adjusting positions of elements are detected, the module extracts relevant information and encapsulates it into structured data. Operation information includes an operation timestamp, collaborator identification, a unique identifier for the manipulated element, an operation type enumeration value, and attribute differences before and after the operation. After this information is encapsulated into an operation record data structure, the module calls the block generation service for processing. The block generation service first obtains the hash value of the previous block, then performs a hash operation between this hash value and the data in the current operation record to generate the hash value of the new block, and finally adds the new block to the end of the blockchain.

[0040] The smart contract execution module is responsible for quantifying and evaluating the creative contributions of each collaborator according to preset contribution calculation rules and generating a design fee allocation scheme. This module runs in a smart contract virtual machine on the blockchain network and employs a deterministic execution model to ensure the verifiability of the calculation results. The contribution calculation rules implemented in the smart contract comprehensively consider three dimensions: the type of modification operation, the scope of the modification's impact, and the time decay effect. When the module receives a contribution calculation request, it first retrieves all modification operation records of all collaborators within a specified time period from the blockchain ledger. Then, it performs a weighted summation of each operation according to the contribution calculation formula to obtain a quantified value of each collaborator's total contribution. Finally, it generates an allocation scheme based on the contribution percentage of each collaborator.

[0041] The version verification module is responsible for verifying the integrity of historical versions of the digital model based on a hash tree structure. This module implements the Merkle tree verification algorithm, which can efficiently detect whether data has been tampered with. During verification, the module first extracts all block data of the version to be verified from the blockchain, then recalculates the root hash value of each block according to the Merkle tree construction rules, and finally compares the calculated root hash value with the originally stored root hash value. The comparison result serves as the basis for determining version integrity.

[0042] The collaboration interface module is responsible for receiving and parsing operation commands issued by each collaborator through the user interface and returning the processing results. This module provides functions such as 3D model visualization, primitive editing tools, collaboration history tracking, contribution query, and cost allocation viewing. The module adopts a front-end / back-end separation architecture; the front-end uses WebGL technology for real-time rendering of the 3D scene, while the back-end uses a RESTful application programming interface to interact with the business logic layer.

[0043] The data storage system of this invention adopts a hybrid storage architecture, storing structured data in a relational database, 3D primitive data in a distributed file system, and blockchain ledger data in a dedicated blockchain database. The relational database uses a horizontally scalable distributed database cluster to store structured data such as user account information, project metadata, primitive indexes, and collaboration record indexes. The distributed file system uses a distributed storage system supporting the POSIX interface to store the 3D geometric data, texture images, and material definition files of the garden model. The blockchain ledger data is stored in the local storage of blockchain nodes, with each node maintaining a complete copy of the ledger, ensuring decentralized storage and traceability of the data.

[0044] Communication between system modules employs both asynchronous message queues and synchronous remote procedure calls. The asynchronous message queue utilizes a message middleware that supports message persistence and transaction semantics to decouple data transfer between modules; for example, the primitive uploading module receives primitive data output by the model parsing module via the message queue. The synchronous remote procedure calls utilize a service framework that supports load balancing and service discovery, used in scenarios requiring real-time responses; for instance, the collaborative interface module uses a synchronous call method when calling the model parsing module to load a model.

[0045] This invention employs a multi-layered fault-tolerance mechanism to ensure high service availability. At the computing level, core computing nodes utilize a primary-backup dual-machine hot standby mode, with the backup node taking over services within seconds in the event of a primary node failure. At the storage level, the distributed storage system employs a multi-replica mechanism, maintaining data replicas across three different storage nodes, ensuring data availability even if any single node fails. At the network level, core switches and aggregation switches are deployed with dual-machine redundancy, automatically switching to the backup link in the event of a link failure. At the application level, each microservice component is deployed as a multi-replica instance, with faulty instances automatically removed and rescheduled through the health check mechanism of the container orchestration platform.

[0046] The system security mechanism comprises four aspects: identity authentication, access control, data encryption, and audit logs. Identity authentication employs a token-based two-factor authentication method, requiring users to provide a username, password, and mobile verification code upon login. Access control is based on a role-based and permission-based model, with different user roles possessing different functional permissions and data access scopes. Data encryption includes two layers: transmission encryption and storage encryption. Transmission encryption uses the TLS protocol, while storage encryption employs transparent data encryption technology. Audit logs record detailed information on all sensitive operations, including operation time, operator, operation type, and operation result, and the log data is written to a dedicated, tamper-proof audit storage.

[0047] In step S1, the system performs the acquisition and parsing of the 3D digital model of the landscape planning scheme. The input to this process receives landscape model files exported from a collaborative platform or external modeling software, and the output is a data structure containing complete graphic element data. The process consists of four stages: model file reading, scene graph parsing, graphic element traversal and extraction, and data structure encapsulation.

[0048] During the model file reading phase, the system first identifies the format type of the input file and then calls the corresponding parser to read the file. The parser internally maintains specialized parsing logic for each file format, correctly handling version differences and extended attributes of different formats. File reading adopts a streaming processing approach; large model files are divided into multiple data blocks and read step-by-step to avoid memory overflow caused by loading all at once. An error handling mechanism during the reading process can identify and skip corrupted data blocks, while simultaneously logging errors for subsequent analysis.

[0049] During the scene graph parsing phase, the system reconstructs a hierarchical data structure in memory based on the scene graph structure information in the model file. The scene graph is organized in a tree structure, with the root node representing the entire garden scene, child nodes representing various objects in the scene, and primitives existing as leaf nodes at the end of the scene graph. During parsing, the system assigns a unique identifier to each scene node and establishes parent-child and sibling reference links. Scene graph parsing is also responsible for identifying the reference relationships between primitives. For example, if a landscape pavilion primitive references a specific material resource, the system will record this reference relationship for subsequent processing.

[0050] During the primitive traversal and extraction phase, the system recursively traverses the leaf nodes of the scene graph, extracting the geometric information, material properties, and design parameters of each primitive. Geometric information extraction first determines the primitive's geometric type, employing different extraction strategies for different types. For mesh primitives, it extracts their vertex position array, vertex normal array, texture coordinate array, and face index array. For parametric surface primitives, it extracts their control point array and surface parameters. For voxel primitives, it extracts their voxel mesh data. Material property extraction first locates the material resources associated with the primitive, then parses the parameters in the material definition file, including base color, transparency, roughness, metallicity, and normal map path. Design parameter extraction is achieved by parsing the primitive's custom attribute fields, which contain business information such as the primitive's dimensions, installation location, and functional classification.

[0051] During the data structure encapsulation phase, the system encapsulates all extracted graph data into a unified data structure format, facilitating processing and transmission by subsequent modules. The encapsulation employs a block-based strategy, distributing the data of large models across multiple data blocks, each containing a certain number of graph elements. These data blocks are linked through an index structure, supporting random access to any graph element without loading the entire model data.

[0052] After acquiring the graph data, the system executes a process for filtering and storing distinctive graph elements on the blockchain. The input to this process is a complete list of graph data, and the output is the transaction hash value written to the blockchain. The process consists of four stages: graph element type determination, intellectual property value assessment, hash identifier generation, and transaction on-chain storage.

[0053] During the element type determination phase, the system identifies the element's type based on its design parameter attributes. The system maintains an internal element type classification rule base, which defines the characteristic attribute value ranges for various types of distinctive elements. For example, the characteristic attributes of a pavilion element include a structure type of pavilion and a primary function of landscape architecture; the characteristic attributes of a sculpture element include a structure type of sculpture and a material type of stone or metal; the characteristic attributes of a water feature element include a structure type of water feature and a water feature type of fountain or cascading water; and the characteristic attributes of a special vegetation configuration element include a vegetation type of rare tree species or a unique design. The system matches the element's attribute values ​​with the classification rules in the rule base and outputs the element type determination result.

[0054] During the intellectual property valuation phase, the system assesses intellectual property value based on the element type determination results and related attributes. The assessment employs a rule-based scoring model, calculating a comprehensive score based on the element's degree of innovation, artistic value, and scarcity. The degree of innovation assessment examines whether the element's design possesses unique creative elements, such as whether it employs unconventional geometric shapes or structural forms. The artistic value assessment examines the element's contribution to the aesthetic expression of the garden, such as whether it can become a visual focal point or a spatial defining element. The scarcity assessment examines the element's uniqueness and irreplaceability within the current project. After comprehensive evaluation, the system marks elements with scores exceeding a threshold as distinctive elements possessing intellectual property value.

[0055] During the hash identifier generation phase, the system generates a unique hash identifier for each featured primitive to be added to the blockchain. The hash calculation employs a hash algorithm with a preset security strength, specifically either SHA-256 or Blake2b. The input data for the hash calculation includes the primitive's complete geometric information, material properties, design parameters, creation timestamp, and creator identifier. The hash calculation uses binary data input to ensure the uniqueness and collision resistance of the hash value. The generated hash identifier is 32 bytes long, represented as a 64-character hexadecimal string.

[0056] During the transaction on-chain phase, the system packages image data, hash identifiers, and related ownership information into a transaction payload and submits it to the blockchain network for consensus processing. The transaction payload's data structure includes a unique image identifier, image type, image data hash, ownership statement, authorization record list, and creation timestamp. The ownership statement contains the creator's identity information, right type, and scope of rights; the authorization record list contains detailed information on historical authorizations, including the authorized object, scope, and time. After the transaction is submitted, the blockchain network selects a block-producing node through a consensus algorithm. The block-producing node packages the transaction into a new block and broadcasts it to other nodes for verification. Once consensus is reached, the transaction is permanently recorded in the blockchain ledger.

[0057] In step S2, the system executes the process of capturing and generating blocks from the collaborating party's modification operations. The input to this process is the edit operation issued by the collaborating party through the collaboration platform, and the output is the newly generated blockchain block. The process consists of four stages: operation event listening, operation information encapsulation, block data calculation, and block append storage.

[0058] During the operation event listening phase, the system's deployed listener components continuously monitor the core editing interfaces of the collaboration platform. The listeners employ a combination of active polling and passive callbacks to ensure no operation event is missed. When an operation event is detected, the listener first parses the event type to determine if it is related to element editing. The system supports four operation types: element addition, element deletion, element attribute modification, and element position adjustment. For non-element editing operations, the listener ignores them; for element editing operations, the listener extracts all relevant information about the operation.

[0059] During the operation information encapsulation phase, the system encapsulates the extracted operation information into structured operation record data. The operation record data structure includes the following fields: a unique operation identifier; a timestamp field recording the precise time of the operation, accurate to the millisecond; a collaborator identifier field recording the unique identity of the user performing the operation, which is associated with account information in the user management system; a graph element identifier field recording the unique identifier of the operated graph element; an operation type field using enumerated values ​​to represent the specific type of operation; an attribute difference field recording the attribute value differences before and after the operation, stored in JSON format for easy subsequent analysis and traceability; and an operation context field recording the environmental information at the time of the operation, including the IP address of the operation terminal, the collaboration platform version number, and the current model version number. After the operation record is encapsulated, the system serializes it, converting it into a byte array for subsequent processing.

[0060] During the block data computation phase, the system executes the core algorithm for block generation to construct the data structure of the new block. The block data structure consists of two parts: a block header and a block body. The block header contains the hash of the previous block, version number, timestamp, difficulty target, random number, and root hash. The block body contains a list of transactions, with each transaction corresponding to an operation record. The block generation computation process is as follows: First, the hash value of the previous block is obtained as the parent hash reference for the new block; then, the serialized data of the operation record is concatenated with the parent hash reference, and the hash value of the concatenated data is calculated to generate the hash value of the new block; next, all hash values ​​in the operation record list are used to construct a Merkle tree using the Merkle tree algorithm, and the root hash value is written into the block header; finally, the block header and block body are combined into a complete block data structure.

[0061] During the block appending phase, the system adds the newly generated block to the end of the blockchain. The block appending operation is implemented by calling the application programming interface (API) of the blockchain nodes, and specifically includes two sub-steps: block verification and block writing. Block verification checks the data structure integrity, hash value correctness, and compliance with consensus rules of the new block. After successful verification, the block is written to the local ledger and broadcast to other nodes in the network. Other nodes, upon receiving the new block, perform the same verification and writing process, ultimately achieving full network synchronization of the blockchain.

[0062] In step S3, the system executes a contribution metric evaluation process based on smart contracts. The input to this process is the history of modification operations recorded in the blockchain ledger, and the output is the metric value of each collaborating party's contribution and a design fee allocation scheme. The process consists of four stages: operation record retrieval, contribution component calculation, weight coefficient application, and allocation scheme generation.

[0063] During the operation record retrieval phase, after receiving the evaluation request, the smart contract first determines the time range and the scope of collaborators to be evaluated. Then, by traversing the transaction index in the blockchain ledger, it retrieves the operation records of all collaborators within the time range. The retrieval results are returned in list form, with each record containing key fields such as operation identifier, operation time, operation type, and number of operated primitives.

[0064] During the contribution component calculation phase, the system calculates contribution component values ​​for three dimensions. The first dimension is the operation type component, with weight coefficients determined based on the operation type field in the operation record. Operation type weights are set according to the following rules: original design modifications correspond to the first weight value, set to 1.0; optimization and adjustment modifications correspond to the second weight value, set to 0.7; error correction modifications correspond to the third weight value, set to 0.4; and reference lookup operations correspond to the fourth weight value, set to 0.2. The proportional relationship between these weight values ​​reflects the relative value of different types of operations to the project's contribution. The second dimension is the influence range component, with weight coefficients determined based on the number of graphic elements affected by the operation. When the number of graphic elements affected by the modification is in the first range (one to five graphic elements), the range weight is the first range weight value, set to 0.5; when the number of graphic elements affected by the modification is in the second range (six to twenty graphic elements), the range weight is the second range weight value, set to 0.8; and when the number of graphic elements affected by the modification is in the third range (more than twenty-one graphic elements), the range weight is the third range weight value, set to 1.0. The third dimension is the time decay component, which introduces a time decay factor to reflect the decay effect of early contributions over time. The time decay factor ranges from zero to one; a larger value indicates a faster decay rate of early contributions. The time decay component is calculated using an exponential decay model, with smaller decay coefficients for operations performed further back in time.

[0065] During the weighting coefficient application phase, the system comprehensively calculates the component values ​​of the three dimensions according to a preset formula to obtain the quantified contribution value of each operation record. The comprehensive calculation formula is as follows:

[0066] in, Indicates the first The total contribution metric of each collaborating party This indicates the total number of operations performed by the collaborating party within the evaluation timeframe. Indicates the first Operation type weight function for each operation Indicates the first The range weighting function of the next operation Indicates the first The time decay weight function for each operation.

[0067] During the allocation scheme generation phase, the system generates an allocation scheme based on the proportion of each collaborator's contribution to the total contribution. The formula for calculating the allocation proportion is:

[0068] in, Indicates the first The allocation ratio among the collaborating parties This represents the total number of collaborating parties. The calculated allocation ratio serves as the basis for each collaborating party's share of the design fees. The generated allocation scheme is output in structured data format and can be directly used in subsequent financial settlement processes.

[0069] In step S4, the system executes a historical version integrity verification process based on a hash tree structure. The input to this process is the version identifier or time point to be verified, and the output is a Boolean verification result and a detailed verification report. The process consists of four stages: block data extraction, Merkle tree reconstruction, root hash comparison, and verification report generation.

[0070] During the block data extraction phase, the system extracts the corresponding block data from the blockchain ledger based on the version information to be verified. Version information is located by retrieving the timestamp field in the block header. The system locates the block containing the target time point and then traces back to extract data from all historical blocks. The extracted data includes the transaction list and block header information for each block, which serve as input for Merkle tree reconstruction.

[0071] During the Merkle tree reconstruction phase, the system recalculates the root hash value of each block based on the extracted transaction data, following the Merkle tree construction rules. Merkle tree construction begins with leaf nodes, each representing the hash value of a transaction record. Adjacent leaf nodes are paired and hashed together to obtain a parent node. If the number of leaf nodes is odd, the last leaf node is paired with itself. The parent node's hash value continues to be paired and hashed according to the same rules, recursively executing this process until only a root node remains. The hash value of the root node is the Merkle root hash of that block.

[0072] During the root hash comparison phase, the system compares the recalculated root hash value of each block with the original root hash value stored in the block header. If all block root hash values ​​match successfully, it means that all historical records before that point in time are intact and have not been tampered with. If there is a mismatch, it means that there is a problem with the data in the corresponding block, which needs to be marked as a verification failure and an alarm triggered.

[0073] During the verification report generation phase, the system organizes the verification results into a structured verification report. The report includes verification object information, verification time, verification result status, the number of successfully matched blocks, a list of failed matches, and a data integrity score for each block. The verification report can be exported in HTML or PDF format for auditing and traceability purposes.

[0074] The system of this invention also includes two enhancement components: a graphic element intellectual property protection module and a distributed storage module, which together support the effective protection of intellectual property rights in landscape design and the high availability of data storage.

[0075] The image element intellectual property protection module is responsible for recording the creation information, ownership declaration, and authorization records of image elements when they are added to the blockchain, and verifying their uniqueness through hash identifiers. When it is necessary to verify the ownership of an image element, the system calculates the hash value of the current image element data and compares it with the hash identifier stored on the blockchain. The ownership verification process first extracts the image element hash identifier stored on the blockchain as a reference value, then performs the same hash calculation on the image element data to be verified to obtain the current hash value, and finally compares the current hash value with the reference value. If the two match, it means that the image element data has not been modified and the ownership information is valid; if the two do not match, it means that the image element data has been modified and the legality of the modification needs to be further verified. The ownership declaration information is stored using an extensible data structure, supporting the addition of multiple rights holder information and rights types. The authorization records support recording detailed information such as the start and end time of the authorization, the scope of authorization, and the conditions of authorization.

[0076] The distributed storage module is responsible for storing the on-chain graph data across multiple participating nodes in a distributed manner. The module employs erasure coding technology to divide the graph data into multiple data blocks and calculates check blocks based on preset redundancy parameters. The encoded data blocks and check blocks are distributed across different storage nodes, ensuring that the original data can be reconstructed from the remaining data blocks even if a certain percentage of nodes fail. For example, with a configuration of four data blocks plus two check blocks, the failure of any two nodes will not result in data loss. The distributed storage module also implements a data recovery mechanism. When a node failure is detected, a background task is triggered to reconstruct the data on the failed node using data from other nodes, and the data is redistributed after the node recovers.

[0077] In a real-world collaborative landscape planning project, the complete workflow of this invention's system is as follows: First, the project initiator creates a new landscape planning project and registers project information and participant accounts in the system. Each participant obtains corresponding access permissions after identity authentication. The project manager uploads the 3D digital model file of the landscape planning scheme through the collaboration interface module. The system automatically executes the model parsing process to extract all graphic and metadata from the model.

[0078] After the image data is parsed, the image element on-chain module assesses the intellectual property value of the image data and selects distinctive image elements with protection value. These distinctive image elements include the project's unique pavilion design, meticulously designed sculptures, creatively unique water feature layouts, and special vegetation configurations. The hash identifiers and related ownership information of these distinctive image elements are written into the blockchain, achieving trusted evidence storage of intellectual property rights.

[0079] Once the collaborative design phase begins, each collaborator edits and modifies the digital model through the collaborative platform. Whenever a collaborator initiates a modification, the operation capture and block generation module records relevant information in real time, including the operation time, collaborator's identity, the modified primitive, and the specific modifications. This operation information is encapsulated into a structured operation record, and a new block is generated through hash calculations and appended to the end of the blockchain.

[0080] Once the project reaches a certain stage, such as the design fee settlement node, the smart contract execution module is triggered to perform a contribution metric evaluation. The module retrieves all modification operation records in the blockchain, performs a weighted calculation according to preset contribution calculation rules, and derives the metric value of each collaborating party's contribution and its proportion of the total contribution. The calculation result generates a design fee allocation scheme, which directly serves as the basis for fee settlement among the collaborating parties.

[0081] Upon project completion, the system provides a version integrity verification function for auditing and tracing the entire collaboration process. Any participant can request integrity verification of the version at a specific point in time. The system recalculates the root hash value using the Merkle tree algorithm and compares it with the original record, outputting a verification report.

[0082] The present invention has designed a comprehensive abnormal situation handling mechanism to deal with various abnormal scenarios that may occur during the collaboration process.

[0083] When network communication interruptions prevent operation records from being uploaded to the blockchain in a timely manner, the system employs a local caching and retransmission mechanism. Operation records are first written to a local cache queue. Once the network is restored, the system automatically submits the operation records in the cache queue to the blockchain network in batches. To avoid duplicate uploads of operation records, the system assigns a unique sequence number to each operation record, and the smart contract checks the uniqueness of the sequence number during processing.

[0084] When multiple collaborators concurrently modify the same graphic element, the system employs an optimistic locking mechanism to handle conflicts. Each graphic element maintains a version number field in the system, and collaborators must include the expected version number when submitting a modification operation. If the current version number does not match the expected version number, it indicates that the graphic element has already been modified by another collaborator. The system rejects the current modification operation and returns a conflict warning. The collaborator needs to obtain the latest graphic element data before making any modifications.

[0085] When a fork occurs in the blockchain network, the system automatically selects the longest chain. The system continuously monitors the main chain status of the blockchain network, and when a fork is detected, it selects the fork containing more proof-of-work as the main chain, discarding the shorter fork. For operation records already recorded on abandoned branches, the system provides a supplementary recording mechanism, allowing users to resubmit these operation records to the main chain for recording.

[0086] When a storage node fails, the distributed storage module automatically triggers a data reconstruction process. The module first detects the failed node, then uses data blocks and checksum blocks from other healthy nodes to reconstruct the original data from the failed node. After reconstruction, the system redistributes the data across the healthy nodes, ensuring that the number of redundant backups remains at a preset level.

[0087] In large-scale municipal landscaping projects, collaboration among various professional teams—including planning designers, landscape architects, architects, structural engineers, mechanical and electrical engineers, and construction companies—is typically involved. These teams collaborate on a unified 3D digital model, and managing model versions and recognizing each team's contributions are traditionally challenging aspects.

[0088] The application of this invention's system in this scenario is as follows: Upon project initiation, the project manager creates the project and configures user accounts for each participating team within the system. The designer imports the initial 3D model of the landscape planning scheme into the system. The system analyzes the model and identifies distinctive graphic elements with intellectual property value, such as urban sculptures, themed pavilions, and unique water features. The hash identifiers and ownership information of these distinctive graphic elements are recorded on the blockchain, forming a credible intellectual property certificate.

[0089] During the design collaboration process, various professional teams modify the model through the collaborative platform. Every modification, including adjustments to the landscape scheme, changes to building locations, and optimizations to structural parameters, is recorded in real time and generates block data. Through contribution calculations using smart contracts, the system can objectively quantify the creative contributions of each team, providing a reliable basis for the allocation of design fees.

[0090] Once the project is completed, any questions regarding previous versions can be traced back through the version integrity verification function. The system verifies the integrity of historical data based on a hash tree structure, ensuring that data generated during the collaboration process has not been tampered with, thus enhancing trust among the participants.

[0091] In residential community landscape design, projects typically involve multiple stakeholders, including real estate development companies, landscape design firms, construction companies, and supervision companies. The real estate development company, as the client, has decision-making power over the design direction and cost control; the landscape design firm is responsible for the conceptual design and construction drawings; the construction company is responsible for the implementation of the landscape project; and the supervision company is responsible for quality control.

[0092] The application of this invention system in this scenario effectively solves the trust issue in collaboration among all parties. The original design scheme of the design unit, as core intellectual property, is stored on the blockchain through the primitive on-chain module, forming an immutable proof of ownership. During subsequent design refinement and construction, any modifications to the design scheme are recorded on the blockchain, forming a complete design change trajectory.

[0093] When a project enters the settlement phase, the smart contract automatically calculates the quantifiable contribution value of each participant based on preset contribution calculation rules. Real estate development companies can intuitively see the contribution distribution at each design stage through the system, and the allocation scheme for design fees is generated based on objective calculation results, reducing human disputes.

[0094] In the application of historical garden restoration and protection, projects involve multiple stakeholders, including cultural heritage protection departments, professional restoration teams, landscape design institutions, and research institutions. Restoration work requires reasonable reconstruction design while preserving historical authenticity, and the professional contributions of each party need to be objectively recognized.

[0095] The application of this invention's system in this scenario is as follows: Using a 3D digital model of the historical garden as the foundational data, the system identifies and stores distinctive cultural relic elements within the model on the blockchain, including historically valuable rock carvings, unique vegetation configurations, and distinctive landscape architecture. The ownership information of these elements is permanently recorded on the blockchain, providing reliable technical support for the protection and inheritance of historical gardens.

[0096] During the restoration design process, the restoration plans and modification suggestions from various professional teams were recorded on the blockchain, including the literature references used for the restoration and the technical parameters of the restoration process. These records formed a complete restoration archive, providing detailed historical data for subsequent garden conservation and research.

[0097] The system of this invention takes into account the performance requirements of actual application scenarios during its design and defines the following key performance indicators.

[0098] In terms of model parsing, the system supports processing large-scale garden models containing no fewer than 100,000 primitives, with a parsing throughput of no less than 5,000 primitives per second. The memory usage during the parsing process does not exceed 16 gigabytes, and the output latency of the parsing results does not exceed three seconds.

[0099] In terms of blockchain transaction processing, the system supports writing at least one thousand transaction records per second, with a transaction confirmation time of no more than ten seconds. The blockchain network adopts a practical Byzantine fault-tolerant consensus algorithm, which can tolerate no more than one-third of faulty or malicious nodes when the number of nodes is four to ten.

[0100] In terms of contribution calculation, the system supports quantitative evaluation of collaboration history containing no more than 100,000 operation records, with a calculation completion time of no more than 30 seconds. The calculation process employs a parallel optimization strategy, making full use of the computing power of multi-core processors.

[0101] Regarding version verification, the system supports integrity verification at any point in time, and the verification response time increases logarithmically with the number of blocks. For a historical record containing 10,000 blocks, the verification completion time is no more than five seconds.

[0102] The system architecture of this invention fully considers future scalability requirements. At the computing resource level, each microservice component supports horizontal scaling, dynamically increasing or decreasing the number of instances based on load. At the storage resource level, the distributed storage module supports online expansion, and the system automatically rebalances data after adding storage nodes. At the functional expansion level, the system adopts a modular plug-in architecture, allowing new functional modules to be loaded into the system as plug-ins without modifying the core architecture.

[0103] The above description is merely an example of specific embodiments of the present invention. Equivalent modifications or alterations made by those skilled in the art under the guidance of the technical solutions of the present invention should all be covered within the protection scope of the present invention.

Claims

1. A method for optimizing landscape planning schemes using digital modeling and collaborative mechanisms, characterized in that, include: Obtain a three-dimensional digital model of the landscape planning scheme, parse the graphic data in the three-dimensional digital model, select distinctive graphic elements with intellectual property value, and store them on the blockchain; The system captures the modification operations performed by collaborators on the 3D digital model, encapsulates the time, subject, primitives, and content of the modification operations into a structured operation record, and generates block data based on blockchain technology. Based on smart contracts and according to preset contribution calculation rules, the creative contributions of each collaborator are quantitatively evaluated, and a design fee allocation plan is generated. Based on a hash tree structure, the integrity of historical versions of the 3D digital model is verified to ensure the data credibility and immutability during the collaboration process.

2. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 1, characterized in that, The image data includes geometric information, material properties, and design parameters of the image element; the distinctive image elements with intellectual property value include at least one of landscape pavilions, sculptures, water features, and special vegetation configurations; the on-chain storage includes generating a unique hash identifier for each distinctive image element and permanently recording the image data, the hash identifier, and related ownership information on the blockchain.

3. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 2, characterized in that, The modification operations include at least one of the following types: adding, deleting, modifying attributes, and adjusting positions of graphic elements; the modification operations are captured by: when a collaborator edits the 3D digital model through the collaboration platform, the relevant information of the modification operations is monitored and recorded in real time, and the relevant information is encapsulated into structured data.

4. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 3, characterized in that, The method for generating block data is as follows: obtain the hash value of the previous block, perform a hash operation between the hash value of the previous block and the structured data of the current operation record to generate the hash value of the new block, and append the new block to the end of the blockchain.

5. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 4, characterized in that, The contribution calculation rules include operation type weight coefficient, influence range weight coefficient, and time decay coefficient; the smart contract performs a weighted calculation on each modification operation based on the operation type weight coefficient, influence range weight coefficient, and time decay coefficient to obtain the total contribution quantification value of each collaborating party.

6. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 5, characterized in that, The operation type weight coefficient is set according to the nature of the modification operation, including original design modification, optimization and adjustment modification, error correction modification and reference consulting operation; the influence range weight coefficient is determined according to the number of graphic elements involved in a single modification; the time decay coefficient is dynamically adjusted according to the interval between the time of the modification operation and the current time.

7. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 6, characterized in that, The design fee allocation scheme is generated based on the proportion of each collaborator's contribution quantification to the total contribution of all collaborators, and the proportion is directly used as the basis for allocating the design fees that each collaborator should receive.

8. The method for optimizing landscape planning schemes using digital modeling and collaboration mechanisms according to claim 7, characterized in that, The hash tree adopts a Merkle tree structure; each block contains multiple operation records as leaf nodes, and the hash values ​​of adjacent leaf nodes are paired up and hashed to obtain the parent node. This process is repeated recursively until the root node is obtained; the integrity verification includes extracting all block data in the blockchain, recalculating the root hash value of each block, and comparing it with the original stored root hash value.

9. A landscape planning scheme optimization system utilizing digital modeling and collaborative mechanisms, characterized in that, include: The model parsing module is used to obtain a 3D digital model of the landscape planning scheme and parse the graphic data within it; The primitive on-chain module is used to select distinctive primitives with intellectual property value and store them on the blockchain; The operation capture and block generation module is used to capture the modification operations of the collaborating party on the three-dimensional digital model and generate block data; The smart contract execution module is used to quantitatively evaluate the creative contributions of each collaborator according to preset contribution calculation rules and generate a design fee allocation scheme. The version verification module is used to verify the integrity of historical versions of the 3D digital model based on a hash tree structure. The collaboration interface module is used to receive and parse operation instructions from each collaborating party.

10. The landscape planning scheme optimization system utilizing digital modeling and collaborative mechanisms according to claim 9, characterized in that, The graph element on-chain module is also used to record the creation information, ownership statement and authorization record of the graph element when it is on-chain, and to verify its uniqueness through hash identifier; the system also includes a distributed storage module, which is used to store the on-chain graph element data in a distributed manner on multiple participating nodes, and to ensure that the graph element data can still be fully recovered in the event that some nodes fail.

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