Incremental update and synchronization method for spatial geographic data based on distributed ledger

Abstract

This invention belongs to the field of data management technology and discloses a method for incremental updating and synchronization of spatial geographic data based on a distributed ledger. The method includes the following steps: In a distributed ledger network of spatial geographic data, incremental data from a data source chain is captured according to a geocoding index, and a hash and zero-knowledge proof of the incremental data are generated; based on the incremental data, the corresponding hash, and the zero-knowledge proof, an encrypted cross-chain transaction is generated using a multi-level encryption mechanism and sent to several target chains; according to a hybrid consensus mechanism, the encrypted cross-chain transaction is zero-knowledge verified using several target chains; after the zero-knowledge verification is successful, the incremental data is synchronized to all spatial geographic data on the target chains. This invention solves the problems of low data synchronization efficiency, high full-transmission cost, insufficient data privacy protection, risks associated with sharing, lack of conflict resolution mechanisms, insufficient cross-chain communication security, and rigid permission management in existing technologies.

Description

Technical Field

[0001] This invention belongs to the field of data management technology, specifically relating to a method for incremental updating and synchronization of spatial geographic data based on distributed ledger. Background Technology

[0002] With the rapid development of applications such as smart cities, digital twins, and environmental monitoring, higher demands are being placed on the real-time performance, accuracy, and sharing of spatial geographic data (such as vector maps, remote sensing images, and 3D models). Traditional spatial data management methods, such as centralized databases, have drawbacks such as single points of failure, susceptibility to data tampering, and difficulty in achieving secure sharing across departments / regions.

[0003] Distributed ledger technologies (such as blockchain) offer new approaches to the management of geospatial data due to their decentralized, immutable, and traceable characteristics. However, applying distributed ledgers to geospatial data, especially in scenarios involving multi-chain collaboration, still faces numerous challenges:

[0004] The shortcomings of existing technology:

[0005] 1) Inefficient data synchronization and high cost of full data transmission: Traditional centralized databases or simple distributed systems often need to transmit large amounts of duplicate or unchanged data during data synchronization. For spatial geographic data, which is typically large in volume, full synchronization not only consumes a lot of network bandwidth but also slows down the synchronization process, affecting real-time performance.

[0006] 2) Insufficient data privacy protection and risks associated with sharing: When sharing spatial geographic data across blockchains, directly transmitting raw data may lead to the leakage of sensitive information (such as precise location, details of specific areas, etc.), violating privacy protection regulations or business confidentiality requirements. Existing technologies lack sophisticated privacy protection measures.

[0007] 3) Lack of conflict resolution mechanisms: In multi-chain collaboration scenarios, different chains or nodes may independently update data in the same area, which can easily lead to conflicts. Traditional synchronization mechanisms may lack effective conflict detection and adjudication mechanisms, or rely on centralized arbitration, which is inefficient and may introduce single points of failure.

[0008] 4) Insufficient security and vulnerability to attacks in cross-chain communication: Some existing cross-chain solutions may directly transmit plaintext data or have insufficient encryption strength, making them easy to be eavesdropped on or tampered with during transmission. There is a lack of effective verification of the integrity and reliability of the source of cross-chain transactions themselves.

[0009] 5) Rigid permission management, which is difficult to adapt to dynamic collaboration needs: Permission management in traditional systems or some blockchain systems is often static and difficult to adjust quickly and flexibly according to changes in the collaboration scenario (such as temporarily adding new participants or adjusting data access levels). Summary of the Invention

[0010] To address the problems of low data synchronization efficiency, high cost of full transmission, insufficient data privacy protection, risks associated with sharing, lack of conflict resolution mechanisms, insufficient security of cross-chain communication, and rigid access control in existing technologies, this invention aims to provide a method for incremental updating and synchronization of spatial geographic data based on a distributed ledger.

[0011] The technical solution adopted in this invention is as follows:

[0012] A method for incremental updating and synchronization of spatial geographic data based on distributed ledger includes the following steps:

[0013] In a distributed ledger network of spatial geographic data, incremental data from the data source chain is captured based on the geocoded index, and hashes and zero-knowledge proofs of the incremental data are generated.

[0014] Based on incremental data, corresponding hashes, and zero-knowledge proofs, a multi-level encryption mechanism is used to generate encrypted cross-chain transactions, which are then sent to several target chains of the distributed ledger network.

[0015] Based on the hybrid consensus mechanism, several target chains are used to perform zero-knowledge verification on encrypted cross-chain transactions. After the zero-knowledge verification is passed, the incremental data is synchronized to all spatial geographic data of the target chains.

[0016] Furthermore, in the distributed ledger network of spatial geographic data, incremental data from the data source chain is captured based on the geocoding index, and hashes and zero-knowledge proofs of the incremental data are generated, including the following steps:

[0017] Collect a number of raw spatial geographic data, store the raw spatial geographic data in a distributed ledger network, and build a geocoded index for the raw spatial geographic data;

[0018] Based on the geocoding index, monitor the data status of the original spatial geographic data in the data source chain. When several data statuses change, capture the incremental data of the original spatial geographic data.

[0019] Perform local consistency verification on the incremental data. After the local consistency verification passes, use a hash calculation function to generate the hash of the incremental data.

[0020] Using a space-constrained zero-knowledge proof algorithm, we construct and generate zero-knowledge proofs for incremental data, and associate the zero-knowledge proofs and hashes with the corresponding incremental data.

[0021] Furthermore, the multi-level encryption mechanism is equipped with multi-level encryption keys, including a root key set in the data source chain, an intermediate key set in the smart contract, and a leaf node key set in the target chain.

[0022] Furthermore, based on the incremental data, the corresponding hash, and zero-knowledge proofs, a multi-level encryption mechanism is used to generate encrypted cross-chain transactions, which are then sent to several target chains of the distributed ledger network, including the following steps:

[0023] Based on the business requirements or preset rules for incremental data, identify the target chain identifiers to which the update needs to be synchronized and the smart contract addresses to process this update, and extract the intermediate keys of the corresponding smart contracts.

[0024] Using the root key of the data source chain, the hash and zero-knowledge proof of the incremental data are encrypted to obtain the encrypted digest. Then, the intermediate key is used to encrypt the incremental data to obtain the encrypted incremental data.

[0025] Based on the target chain identifier, smart contract address, encrypted digest, and encrypted incremental data, construct an encrypted cross-chain transaction and send the encrypted cross-chain transaction to the cross-chain bridge of the distributed ledger network;

[0026] In the cross-chain bridge, the encrypted digest of the encrypted cross-chain transaction is decrypted based on the root key, and an incremental data hash list is generated based on all the decrypted hashes obtained, and a corresponding synchronization request is generated.

[0027] On the target chain, a smart contract is used to listen to the cross-chain bridge, receive the synchronization request from the cross-chain bridge, and look up the list of incremental data hashes included in the synchronization request to locate the encrypted cross-chain transaction corresponding to the cross-chain bridge.

[0028] The encrypted cross-chain transactions are initially verified. Once the initial verification is passed, the encrypted cross-chain transactions are received in the cross-chain bridge on the target link.

[0029] Furthermore, based on the hybrid consensus mechanism, zero-knowledge verification is performed on the encrypted cross-chain transactions using several target chains. After the zero-knowledge verification is passed, the incremental data is synchronized to all spatial geographic data of the target chains, including the following steps:

[0030] Based on the hybrid consensus mechanism, zero-knowledge verification is performed on encrypted cross-chain transactions on several target chains to obtain zero-knowledge verification results;

[0031] If the zero-knowledge verification result is false, the zero-knowledge verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the data incremental update and synchronization process ends.

[0032] If the zero-knowledge verification result is true, then the zero-knowledge verification passes, and a dynamic permission model is used to perform dynamic permission verification on each target chain to obtain the dynamic permission verification result.

[0033] If the dynamic permission verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the incremental data update and synchronization process is terminated.

[0034] If the dynamic permission verification result is successful, the encrypted incremental data in the encrypted cross-chain transaction is decrypted on the target chain according to the intermediate key to obtain the decrypted incremental data, and then proceed to the next step.

[0035] Extract the leaf node key of each target chain, decrypt the first encrypted spatial geographic data stored in the target chain, and obtain the decrypted spatial geographic data.

[0036] Based on the decrypted incremental data, the decrypted spatial geographic data of each target chain is synchronized to obtain synchronized spatial geographic data.

[0037] Based on the leaf node key, the synchronized spatial geographic data is encrypted, and the resulting second encrypted spatial geographic data is stored in the target chain.

[0038] Furthermore, the dynamic permission model includes node role dimensions, decryption behavior dimensions, and illegal operation dimensions;

[0039] Using a dynamic permission model, dynamic permission verification is performed on each target chain to obtain the dynamic permission verification result, including the following steps:

[0040] Extract the target chain reputation score and basic information of the decryption nodes in the target chain that receive encrypted cross-chain transactions;

[0041] Using a dynamic permission model, node role characteristics, decryption behavior characteristics, and illegal operation characteristics are extracted from basic information;

[0042] Dynamic permission verification is performed based on the target chain reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics to obtain dynamic permission verification results.

[0043] Furthermore, based on the hybrid consensus mechanism, zero-knowledge verification is performed on the encrypted cross-chain transactions on several target chains to obtain the zero-knowledge verification results, including the following steps:

[0044] Based on the multi-level decryption mechanism, the root key of the data source chain is extracted using each target chain, and the encrypted digest in the encrypted cross-chain transaction is decrypted based on the root key to obtain the decrypted zero-knowledge proof.

[0045] Configure a verification circuit on each target chain, and configure the space constraints and property constraints corresponding to the decrypted zero-knowledge proof for each verification circuit.

[0046] In each target chain, a verification circuit configured with spatial constraints is used to perform zero-knowledge verification on the decrypted zero-knowledge proof to obtain the zero-knowledge verification result.

[0047] According to the hybrid consensus mechanism, the zero-knowledge verification results of all target chains are verified by hybrid consensus. If the hybrid consensus verification passes, the zero-knowledge verification result is output and the incremental data synchronization step is initiated.

[0048] If the hybrid consensus verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the incremental data update and synchronization process is terminated.

[0049] Furthermore, based on the decrypted incremental data, the decrypted spatial geographic data of each target chain is synchronized to obtain synchronized spatial geographic data, including the following steps:

[0050] Extract the local version of the decrypted spatial geographic data and the basis version of the decrypted incremental data for each target chain, and compare the local version and the basis version.

[0051] If the comparison matches, the decrypted spatial geographic data of each target chain is synchronized based on the decrypted incremental data to obtain synchronized spatial geographic data, and the synchronization ends; otherwise, proceed to the next step.

[0052] According to the dispute resolution mechanism, cross-chain arbitration is conducted on the decrypted incremental data and decrypted spatial geographic data, the cross-chain arbitration result is executed, and the incremental data capture step is returned.

[0053] Furthermore, based on the dispute resolution mechanism, cross-chain arbitration is conducted on the decrypted incremental data and decrypted spatial geographic data. The obtained cross-chain arbitration result is executed, and the incremental data capture step is returned, including the following steps:

[0054] According to the dispute resolution mechanism, the conflict between the decrypted incremental data and the decrypted spatial geographic data is resolved. If the conflict is resolved successfully, the process returns to the incremental data synchronization step; otherwise, it proceeds to the next step.

[0055] Extract disputed information from the decrypted incremental data and decrypted spatial geographic data, and send the disputed information to the dispute arbitration system of the distributed ledger network through a cross-chain mechanism;

[0056] Using the dispute arbitration model of the dispute arbitration system, cross-chain arbitration decisions are made on dispute information to obtain cross-chain arbitration results;

[0057] Based on the cross-chain arbitration ruling, the corresponding cross-chain arbitration execution strategy is generated using the dispute arbitration system's execution strategy generation model.

[0058] The corresponding cross-chain arbitration execution strategy is executed on the distributed ledger network, and the spatial geographic data synchronization steps are returned.

[0059] Furthermore, the dynamic permission model is built based on the N-GAN-Attention-MLP algorithm, the dispute arbitration model is built based on the LSTM algorithm, and the execution strategy generation model is built based on the HMARL algorithm.

[0060] The beneficial effects of this invention are as follows:

[0061] This invention provides a method for incremental updating and synchronization of spatial geographic data based on a distributed ledger. By accurately capturing and synchronizing only incremental data, it significantly reduces the amount of data that needs to be transmitted and processed, greatly shortening synchronization time and reducing network bandwidth consumption and computing resource overhead. It is particularly suitable for spatial geographic data, which is typically large in volume, enabling faster and more economical updates and sharing of spatial geographic data. Utilizing zero-knowledge proof technology, the validity and integrity of incremental data can be verified without exposing its specific content, providing a higher level of privacy protection and ensuring that sensitive spatial information is not obtained by unauthorized parties. This breaks down privacy barriers in data sharing, enabling secure cross-chain collaboration of spatial geographic data involving sensitive information while maintaining privacy. Combining local consistency verification, hash verification, and zero-knowledge verification under a hybrid consensus mechanism ensures the accuracy and tamper-proof nature of incremental data during cross-chain transmission, more reliably guaranteeing the data quality of each node in the distributed ledger network. The data ultimately stored on the target chain is consistent and reliable, providing a solid data foundation for decisions that rely on accurate geospatial data. Multi-level encryption mechanisms protect cross-chain transactions, and a hybrid consensus and arbitration process executed using smart contracts (or similar mechanisms) enhances the security of the entire synchronization process, reduces the security risks of data synchronization in distributed, heterogeneous network environments, and strengthens the confidence of all parties participating in collaboration. A dynamic permission model is adopted, flexibly adjusting and verifying data access and operation permissions according to actual conditions, better adapting to complex and ever-changing collaboration scenarios and role changes, improving the flexibility and efficiency of distributed ledger network management, and better supporting the data security control needs in dynamic collaboration environments. Through a dispute arbitration model and an execution strategy generation model, conflicts or disputes that may arise during the synchronization process can be handled automatically and intelligently, resolving disputes and executing results faster and more fairly, ensuring the smooth progress of the synchronization process, and reducing system stagnation or data inconsistency caused by disputes.

[0062] Other beneficial effects of the present invention will be further explained in the specific embodiments. Attached Figure Description

[0063] Figure 1 This is a flowchart of the spatial geographic data incremental update and synchronization method based on distributed ledger in this invention. Detailed Implementation

[0064] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0065] Example:

[0066] like Figure 1 As shown, this embodiment provides a method for incremental updating and synchronization of spatial geographic data based on a distributed ledger, including the following steps:

[0067] S1: In a distributed ledger network of spatial geographic data, incremental data from the data source chain is captured based on the geocoded index, and hashes and zero-knowledge proofs of the incremental data are generated, including the following steps:

[0068] S1-1: Collect some raw spatial geographic data, store the raw spatial geographic data in a distributed ledger network, and build a geocoded index for the raw spatial geographic data;

[0069] The formula is:

[0070]

[0071] In the formula, This is the original spatial geographic data; It is a collection of spatial geographic data; i For data indication quantity; For storage operation functions; It is a distributed ledger network;

[0072]

[0073] In the formula, The spatial characteristics, temporal version information, and ID information of the original spatial geographic data; Set functions for spatial feature extraction, time version information extraction, ID information extraction, and geocoding index; For geocoded indexes;

[0074] Spatial geographic data includes vector data, raster data (such as remote sensing imagery), 3D models, etc.

[0075] This embodiment applies to the joint operation of reservoirs. Five reservoirs (A, B, C, D, and E) are distributed in the same watershed, jointly serving the downstream flood control, water supply, and power generation needs. To achieve optimal operation, it is necessary to monitor the water level, storage capacity, inflow / outflow, surrounding rainfall, water quality, and related geospatial information (such as reservoir area, inundation line, and surrounding land use changes) of each reservoir in real time. These data are collected by different departments at different locations, and it is necessary to ensure the consistency, timeliness, and reliability of the data so that the dispatch center can make scientific decisions.

[0076] The data sources for each reservoir and its surrounding area include: automated water level / flow monitoring stations, meteorological radar / rain gauge stations, drone / satellite remote sensing image acquisition points, ground inspection personnel, and databases of various departments (such as land and resources, environmental protection). These data sources are connected to their respective data acquisition front-end machines through secure channels. These front-end machines, as nodes in the "data source chain," are responsible for the preliminary processing and uploading of raw spatial geographic data.

[0077] S1-2: Based on the geocoding index, monitor the data status of the original spatial geographic data in the data source chain. When several data statuses change, capture the incremental data of the original spatial geographic data.

[0078] The formula is:

[0079]

[0080] In the formula, Incremental data from the original spatial geographic data; Location function for data unit; Preset trigger conditions; j For data indication quantity;

[0081] Geocoding indexes are spatiotemporal index structures (such as R-trees, quadtrees, spatiotemporal cubes, etc.). This index not only records spatial location information but also timestamps or version information, which is used to quickly locate and compare the state of data at different points in time. By comparing spatial and temporal data through geocoding indexes, it is possible to accurately identify data units in the original spatial geographic data of the data source chain that have changed (such as modified geographic features, newly added image patches, updated attribute values, etc.). This is more accurate than simple timestamp comparison and can handle concurrent modifications and complex updates.

[0082] For each data unit with changing elements, extract its unique identifier (e.g., element ID), change type (add / delete / modify), spatial information before and after the change (e.g., coordinates, geometric shape representation), attribute information (e.g., name, type, status), version information (new and old version numbers), data type, and add necessary metadata, such as operation timestamps and operator identity, for subsequent verification and merging. Organize this information into a standardized "change set" data structure. For modification operations, complete or key information before and after the change should be included to facilitate verification and rollback. Transform the original change information of the data unit into structured and standardized incremental data for easy subsequent processing and verification.

[0083] Use the latest block hash, timestamp, or an incrementing version number of the dataset on the data source chain as the version identifier of the incremental data. At the same time, record the original data version on which the incremental data is based (e.g., the version of the data before the modification operation). This helps the target chain to perform version verification and conflict detection when the application is updated.

[0084] In this embodiment, an R-tree or quadtree is used to index spatial locations, with timestamps or version numbers appended. For the reservoir boundary vector data of reservoir A, the index records its polygon coordinates, the reservoir it belongs to (A), the version number (V1.0), and the timestamp (2025-07-14T00:00:00Z). For a remote sensing image covering the area of ​​reservoir B, the index records its boundaries, resolution, timestamp (capture time), and version number (V1.0). This index is not only stored on a single node, but is synchronized through a distributed ledger network to ensure that all participating nodes can query it quickly.

[0085] The system continuously monitors the data source chain (such as the monitoring station chain of Reservoir C). At 07:15, the water level sensor data of Reservoir C changed from 50.2 meters to 50.5 meters, exceeding the preset threshold. The data source chain node discovered the change in the status of the data point through the geocoding index (querying the water level monitoring point ID of Reservoir C and its time series). The system captures this incremental data: monitoring point ID, old value (50.2), new value (50.5), timestamp (2025-07-14T07:15:00Z), and data type (water level). At the same time, if the drone just transmits a new image of the area around Reservoir C, the system will also capture this newly added raster data block and its metadata.

[0086] S1-3: Perform local consistency verification on the incremental data. After the local consistency verification is successful, use a hash calculation function, such as a cryptographic hash function like SHA-256, to generate the hash of the incremental data.

[0087] The formula is:

[0088]

[0089] In the formula, Hash for incremental data; For hash calculation functions;

[0090] Local consistency verification includes checking incremental data for conflicts between new and existing features, validity of deletion operations, legality of modification operations, geometric validity (e.g., polygons do not self-intersect), attribute value range constraints (e.g., elevation values ​​are within a reasonable range), topological consistency (e.g., adjacent plot boundaries match), and data correlation (e.g., the association IDs of the attribute table and the feature table are consistent). This verification is based on the locally stored original data or its latest version.

[0091] In this embodiment, the captured incremental data is locally consistent, including checking whether the new value (50.5) is within the sensor's range and whether it is logically consistent with other related data in the same area (such as a decrease in outbound flow); checking whether the metadata (such as geographical location and shooting time) is valid and whether the image data is complete and undamaged;

[0092] S1-4: Using spatially constrained zero-knowledge proof algorithms (such as Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs), Zero-Knowledge Scalable Transparent Arguments of Knowledge (zk-STARKs), Short Proofs for Confidential Transactions and More (Bulletproofs), etc.), construct and generate zero-knowledge proofs for incremental data, and associate the zero-knowledge proofs and hashes with the corresponding incremental data;

[0093] The formula is:

[0094]

[0095] In the formula, Proofs of spatial constraints and attribute constraints for incremental data; Prove generating functions for spatial constraints and property constraints; These are spatial constraint rules and attribute constraint rules; For parameters of the zero-knowledge proof algorithm; Zero-knowledge proofs for incremental data; To prove the combinatorial function;

[0096] Spatial Constraint Proof (P1): Using a geometric algorithm (such as the Gilbert–Johnson–Keerthi (GJK) algorithm), we verify whether the coordinates (x, y) of the updated feature are within a preset update region (e.g., a polygon boundary), generating a zero-knowledge proof P1 that proves "the coordinates of the feature satisfy the condition that they are within region R. In this embodiment, it proves that the water level monitoring point is indeed located within the reservoir area of ​​Reservoir C" without revealing the specific coordinate values. This proof can limit the coordinate range error to less than 0.1 meters, ensuring spatial accuracy.

[0097] Attribute Constraint Proof (P2): If the update involves attribute changes, a zero-knowledge proof P2 is generated based on a predefined set of attribute rules to prove that "the updated attribute satisfies all rules. In this example, it proves that the water level of 50.5 meters is within the effective range of the sensor (e.g., 0-60 meters) and the change range (+0.3 meters) is within a reasonable physical range (e.g., the maximum possible rise in the past hour)" without revealing the specific attribute value. This proof supports multi-condition AND / OR logic combinations.

[0098] S2: Based on incremental data, corresponding hashes, and zero-knowledge proofs, a multi-level encryption mechanism is used to generate encrypted cross-chain transactions, and these encrypted cross-chain transactions are sent to several target chains of the distributed ledger network; the target chains include data chains of other reservoirs and the central scheduling and control chain.

[0099] The multi-level encryption mechanism has multiple encryption keys, including a root key set in the data source chain, an intermediate key set in the smart contract, and a leaf node key set in the target chain.

[0100] Based on incremental data, corresponding hashes, and zero-knowledge proofs, a multi-level encryption mechanism is used to generate encrypted cross-chain transactions, which are then sent to several target chains in the distributed ledger network. This process includes the following steps:

[0101] S2-1: Based on the business requirements or preset rules of incremental data, determine the identifiers of several target chains to which the update needs to be synchronized and the smart contract addresses to process this update, and extract the intermediate keys of the corresponding smart contracts.

[0102] The formula is:

[0103]

[0104] In the formula, This refers to the set of target chain identifiers and the corresponding set of smart contract addresses that need to be synchronized. For locating functions; For business needs and pre-defined rules;

[0105]

[0106] In the formula, This serves as the intermediate key for smart contracts; For smart contracts; k For smart contract indication quantities, corresponding to the first k Target chain; This is the intermediate key extraction function;

[0107] S2-2: Using the root key of the data source chain, encrypt the hash and zero-knowledge proof of the incremental data to obtain the encrypted digest, and then use the intermediate key to encrypt the incremental data to obtain the encrypted incremental data;

[0108] The formula is:

[0109]

[0110] In the formula, This is the encrypted digest; This is the encrypted incremental data; For encryption functions; This is the root key;

[0111] S2-3: Based on the target chain identifier, smart contract address, encrypted digest, and encrypted incremental data, construct the encrypted cross-chain transaction and send the encrypted cross-chain transaction to the cross-chain bridge of the distributed ledger network;

[0112] The formula is:

[0113]

[0114] In the formula, This is the encrypted digest; This is the encrypted incremental data; For encryption functions; This is the root key;

[0115]

[0116] In the formula, For encrypted cross-chain transactions; Identify the target chain; For random numbers and timestamps; Construct functions for cross-chain transactions;

[0117] A cross-chain bridge is a pre-defined cross-chain channel (e.g., based on an oracle, relay node, or a specific cross-chain protocol).

[0118] S2-4: On the cross-chain bridge, based on the root key, the encrypted digest of the encrypted cross-chain transaction is decrypted, and based on all the decrypted hashes obtained, an incremental data hash list is generated, and a corresponding synchronization request is generated.

[0119] The formula is:

[0120]

[0121] In the formula, This is the hash after decryption; This is the decryption function; A list of incremental data hashes; J The total number of data; For synchronization requests; Generate functions for synchronous requests;

[0122] S2-5: On the target chain, use smart contracts to listen to the cross-chain bridge. For example, the target chain can discover new incremental updates by subscribing to the block header or specific events of the source data chain on the cross-chain bridge, receive synchronization requests from the cross-chain bridge, and look up the list of incremental data hashes included in the synchronization request to locate the encrypted cross-chain transaction corresponding to the cross-chain bridge.

[0123] S2-6: Perform preliminary verification of encrypted cross-chain transactions. After the preliminary verification is passed, receive the encrypted cross-chain transactions in the cross-chain bridge of the target link.

[0124] The formula is:

[0125]

[0126] In the formula, These are preliminary verification results; This is for preliminary function verification;

[0127] S3: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on the encrypted cross-chain transactions using several target chains. After the zero-knowledge verification is passed, the incremental data is synchronized to all spatial geographic data of the target chains, including the following steps:

[0128] S3-1: Based on the hybrid consensus mechanism, perform zero-knowledge verification on encrypted cross-chain transactions on several target chains to obtain the zero-knowledge verification results, including the following steps:

[0129] S3-1-1: Based on the multi-level decryption mechanism, the root key of the data source chain is extracted using each target chain, and the encrypted digest in the encrypted cross-chain transaction is decrypted based on the root key to obtain the decrypted zero-knowledge proof.

[0130] The formula is:

[0131]

[0132] In the formula, This is a zero-knowledge proof after decryption;

[0133] S3-1-2: Configure a verification circuit on each target chain, and configure the space constraints and attribute constraints corresponding to the decrypted zero-knowledge proof for each verification circuit;

[0134] The formula is:

[0135]

[0136] In the formula, For the target chain Verification circuit; Configure functions for constraints;

[0137] S3-1-3: In each target chain, a verification circuit configured with spatial constraints is used to perform zero-knowledge verification on the decrypted zero-knowledge proof to obtain the zero-knowledge verification result;

[0138] The formula is:

[0139]

[0140] In the formula, The result is a zero-knowledge verification. For zero-knowledge verification functions;

[0141] Verification P1: Confirms that "the water level monitoring point is indeed located within the reservoir area of ​​Reservoir C" and that the error is within the allowable range, but does not obtain the specific coordinates;

[0142] Verification P2: Confirm that "the water level of 50.5 meters is within the effective range of the sensor (e.g., 0-60 meters), and the change range (+0.3 meters) is within a reasonable physical range (e.g., the maximum possible rise in the past hour)", but do not obtain specific attribute values;

[0143] S3-1-4: Based on the hybrid consensus mechanism, a hybrid consensus algorithm (e.g., a variant of Proof of Work (PoW) / Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT)) is used to perform hybrid consensus verification on the zero-knowledge verification results of all target chains. That is, after multiple verification nodes independently complete the zero-knowledge verification, they reach a consensus through the consensus protocol. If the hybrid consensus verification passes, the zero-knowledge verification result is output and the incremental data synchronization step is entered.

[0144] The formula is:

[0145]

[0146] In the formula, Verification results for hybrid consensus; Here, K represents the hybrid consensus verification function; K is the total number of target chains.

[0147] S3-1-5: If the hybrid consensus verification fails, the corresponding encrypted cross-chain transaction will be deleted on the target chain, an alarm signal will be returned to the data source chain, and the incremental data update and synchronization process will be terminated.

[0148] S3-2: If the zero-knowledge verification result is false, the zero-knowledge verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the data incremental update and synchronization process ends.

[0149] S3-3: If the zero-knowledge verification result is true, then the zero-knowledge verification passes, and a dynamic permission model is used to perform dynamic permission verification on each target chain to obtain the dynamic permission verification result.

[0150] The dynamic permission model includes the node role dimension, the decryption behavior dimension, and the illegal operation dimension;

[0151] The dynamic permission model is constructed based on the N-Generative Adversarial Network (GAN)-Attention-Multi-Layer Perceptron (MLP) algorithm, where N is the number of attention dimensions of the dynamic permission model. In this embodiment, N is 3.

[0152] Using a dynamic permission model, dynamic permission verification is performed on each target chain to obtain the dynamic permission verification result, including the following steps:

[0153] A-1: Extract the target chain reputation score and basic information of the decryption nodes in the target chain that receive encrypted cross-chain transactions;

[0154] A-2: Use N GANs of the dynamic permission model to extract node role features (such as administrator, ordinary user, auditor), decryption behavior features (such as decryption frequency, decryption data volume, decryption time) and illegal operation features (such as historical violation records, abnormal behavior patterns) from the basic information.

[0155] A-3: Based on the target chain reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics, perform dynamic permission verification to obtain the dynamic permission verification result;

[0156] In detail, based on dynamic attention weights, the Attention mechanism is used to weight and fuse the target chain reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics to obtain weighted fused features. Using MLP, dynamic permission verification is performed based on the weighted fused features to obtain dynamic permission verification results. The dynamic permission verification results include node permission type and predicted labels for pass or fail.

[0157] In this embodiment, based on the role characteristics of the requesting node (such as "manager"), its decryption behavior characteristics (first time decrypting this type of data), and its historical operation record characteristics (no illegal operation), it is determined that it has update permissions, and the dynamic permission verification is passed;

[0158] S3-4: If the dynamic permission verification fails, delete the corresponding encrypted cross-chain transaction on the target chain, return an alarm signal to the data source chain, and end the incremental data update and synchronization process.

[0159] S3-5: If the dynamic permission verification result is successful, the encrypted incremental data in the encrypted cross-chain transaction is decrypted on the target chain according to the intermediate key to obtain the decrypted incremental data, and then proceed to the next step.

[0160] The formula is:

[0161]

[0162] In the formula, This is the incremental data after decryption;

[0163] S3-6: Extract the leaf node key of each target chain, decrypt the first encrypted spatial geographic data stored in the target chain, and obtain the decrypted spatial geographic data.

[0164] The formula is:

[0165]

[0166] In the formula, For decrypted spatial geographic data; Leaf node key; This is the first encrypted spatial geographic data;

[0167] S3-7: Based on the decrypted incremental data, synchronize the decrypted spatial geographic data of each target chain to obtain synchronized spatial geographic data, including the following steps:

[0168] S3-7-1: Extract the local version of the decrypted spatial geographic data and the basis version of the decrypted incremental data for each target chain, and compare the local version and the basis version.

[0169] S3-7-2: If the comparison is consistent, then based on the decrypted incremental data, synchronize the decrypted spatial geographic data of each target chain to obtain synchronized spatial geographic data, and end the synchronization, then proceed to step S3-8; otherwise, proceed to the next step.

[0170] The formula is:

[0171]

[0172] In the formula, For synchronized spatial geographic data; This is a synchronization function;

[0173] The decrypted spatial geographic data of each target chain is synchronized. Based on the change type (add / delete / modify) of the decrypted incremental data, the decrypted spatial geographic database or status on the target chain is updated. For example, if it is a modification, the old value is replaced with the new value in the incremental data; if it is a deletion, the feature is marked or removed. The update operation itself is also recorded in the block of the target chain, forming a new version history.

[0174] S3-7-3: Based on the dispute resolution mechanism, conduct cross-chain arbitration on the decrypted incremental data and decrypted spatial geographic data, execute the obtained cross-chain arbitration result, and return the incremental data capture step, including the following steps:

[0175] S3-7-3-1: According to the dispute resolution mechanism, conflict resolution is performed on the decrypted incremental data and the decrypted spatial geographic data. If the conflict resolution is successful, return to the incremental data synchronization step; otherwise, proceed to the next step.

[0176] Conflict resolution methods include:

[0177] Rule-based automatic conflict resolution: Automatically resolves conflicts according to predefined rules, including: Based on timestamp / version number: retaining updates with higher (newer) version numbers; Based on business rules: for modifications to the same element, prioritize updates from specific sources (such as authoritative institutions), or decide based on update type (such as incident reports taking precedence over routine maintenance); Merging: if possible, attempt to merge conflicting updates (e.g., attribute modifications can be merged, but spatial location modifications are usually difficult to merge); Rejection: Reject applying the current increment and wait for the conflict to be resolved before retrying;

[0178] Verification resolution based on decrypted zero-knowledge proofs: If a conflict involves multiple valid decrypted zero-knowledge proofs, the content of the decrypted zero-knowledge proofs can be further analyzed (without revealing specific data) or on-chain consensus can be relied upon to determine which update should be retained;

[0179] Manual intervention: Complex or important conflicts can be marked for manual judgment and resolution by administrators; if information about increased rainfall in the area surrounding Reservoir C is also received from Reservoir B chain, and Chain A believes this may lead to a larger rise in water level, manual judgment may be needed on how to integrate the two pieces of information. Administrators can intervene through the management interface, view the proof and original data fragments, and decide whether to directly use the water level from Chain C or to make corrections based on the rainfall information; if Chain A and Chain B have different opinions on how to update the overall status of Reservoir C (water level + rainfall), and cannot resolve them quickly automatically or manually, a cross-chain arbitration process will be triggered.

[0180] S3-7-3-2: Extract disputed information from the decrypted incremental data and decrypted spatial geographic data, and send the disputed information to the dispute arbitration system of the distributed ledger network through a cross-chain mechanism;

[0181] The dispute information includes: data source chain identifier, target chain identifier, related element identifier, dispute type (verification failure / version conflict), decrypted incremental data, decrypted zero-knowledge proof, specific evidence of verification failure / conflict, current target chain status, source chain status (possibly referenced via block header hash), and dispute submission timestamp.

[0182] In this embodiment, there is a version difference (different timestamps) between the received increment (50.5 meters at 07:15) and the latest local data (50.2 meters at 07:00), which is marked as a potential conflict or an update is needed;

[0183] S3-7-3-3: Using the dispute arbitration model of the dispute arbitration system, extract the semantic features of the dispute information, perform cross-chain arbitration on the semantic features, and obtain the cross-chain arbitration result;

[0184] The dispute arbitration model is constructed based on the Long Short-Term Memory (LSTM) network algorithm;

[0185] The cross-chain arbitration ruling includes:

[0186] 1) Conflict type identification: Clearly identify the type of potential conflict (e.g., data overlay conflict, order conflict, authorization conflict, data integrity conflict, etc.), which helps to handle the issue more accurately later;

[0187] 2) Priority Determination: Urgency Determination: If the disputed information mentions words such as "urgent," "alarm," or "accident," the model may determine that the relevant update has a higher priority (echoing the previous "business logic" priority); Importance Determination: Analyze the text to determine which update is more important to the business process. For example, an update that affects core business logic may take precedence over a minor modification that does not affect core functionality; Reasonableness Determination: Based on the context, determine which update is more in line with the preset business rules or logic. For example, if an update describes an operation that clearly violates business rules, the model may determine that another update is more reasonable.

[0188] 3) Confidence score of the ruling: The model outputs a confidence score, which indicates its confidence in the outcome of the ruling. A ruling with low confidence may require higher-level confirmation or trigger a more complex arbitration process.

[0189] 4) Suggestions on liability attribution: Although the ruling itself may not directly punish, the model may analyze which node or chain's behavior is more likely to cause conflict, providing a basis for subsequent punishment mechanisms (such as deducting collateral);

[0190] In this embodiment, the cross-chain arbitration ruling is "the measured water level value provided by Chain C is adopted, and the impact of rainfall will be assessed in the next comprehensive evaluation," etc.

[0191] S3-7-3-4: Based on the cross-chain arbitration ruling, use the dispute arbitration system's execution strategy generation model to generate the corresponding cross-chain arbitration execution strategy;

[0192] The execution strategy generation model is built on the Hierarchical Multi-Agent Reinforcement Learning (HMARL) algorithm. The model includes a macro-policy generation layer and an operational policy generation layer. The macro-policy generation layer is equipped with a macro-agent built on the Deep Q-Network (DQN) algorithm, which can learn from the high-dimensional state space and determine which macro-action direction should be taken under the current global state (based on the arbitration result and system environment). The operational policy generation layer includes several operational agents built on the Proximal Policy Optimization (PPO) algorithm, which are responsible for refining the macro-policy into specific, executable operational instructions. Each operational agent corresponds to a participant (such as the data source chain, target chain, multiple arbitration nodes, and multiple execution nodes). The operational agent represents an entity, and several operational agents need to work together to execute the ruling.

[0193] Based on the cross-chain arbitration ruling, the corresponding cross-chain arbitration enforcement strategy is generated using the dispute arbitration system's enforcement strategy generation model, including the following steps:

[0194] S3-7-3-4-1: The macro-level strategy generation layer generates the cross-chain arbitration ruling and input execution strategy, and collects the global state of the current cross-chain environment, including: the chains involved and their current state (such as block height and network latency); the status of the participants (such as whether nodes are online and resource availability); the specific content of the arbitration ruling (such as which transaction needs to be rolled back and which update needs to be applied); and the progress of the current execution task.

[0195] S3-7-3-4-2: Encode the cross-chain arbitration result and the global state of the current cross-chain environment into a high-dimensional vector, which serves as the input to the macroscopic intelligent agent;

[0196] S3-7-3-4-3: Based on the Q-value function learned by the macro-agent, evaluate the expected rewards of all possible macro-actions (e.g., "execute rollback", "apply high-priority update", "request more evidence", "coordinate multi-party operations") in the current state.

[0197] S3-7-3-4-4: The macroscopic agent selects the macroscopic action with the highest Q value, which represents a high-level direction of execution. For example, it may decide to "execute the update of the measured water level provided by the C-chain".

[0198] S3-7-3-4-5: The macro strategy generation layer broadcasts the selected macro strategy (such as "execute the update of the measured water level value provided by the C chain") to all relevant operational agents in the operation strategy generation layer. Each operational agent initializes its operational decision state based on the received macro strategy and its own local state (such as local data and cached information).

[0199] Local state includes: the current data of the local chain; the communication state with other participants (such as the source chain and other target chains); and the specific tasks that the macro strategy requires it to perform.

[0200] S3-7-3-4-6: Each operational agent selects an action from the space of possible operational actions based on its learned policy (based on the context of the macro-policy); for example:

[0201] The action space of the source chain smart agent includes: data reading: reading specific transaction data (such as the original data of the water level update request); signature verification: verifying the data signature or the authorization certificate of the source chain; status query: querying the data status on the current chain (such as the current water level value); error reporting: reporting an error if the data is invalid or the signature is incorrect; staking operation (if a penalty is required): calling the staking contract to deduct the staking amount of the violating party;

[0202] The action space of the target chain smart agent includes: transaction preparation: constructing a smart contract to call a transaction (such as updating water level data); broadcasting a transaction: broadcasting the transaction to the target chain network; log triggering: calling the audit contract to record operation logs (such as updating source and time); state rollback: if execution fails, triggering a rollback transaction to restore the original data; resource request: requesting channel resources or computing resources for the cross-chain bridge;

[0203] The action space of the arbitration node agent includes: legality verification: checking whether the operation complies with cross-chain rules (such as data range, permissions); log recording: recording the arbitration ruling and execution details to the on-chain log; dynamic parameter adjustment: updating the reputation score or verification difficulty of the violating party; appeal handling: suspending the penalty within the time window and waiting for the participating party to appeal; anomaly reporting: if there is a conflict in the arbitration logic, reporting to the macro-level agent;

[0204] The action space of the execution node smart agent includes: data writing: executing smart contract updates (such as setting the water level to 50.5 meters); result reporting: returning the execution status (success / failure) and error information; partial rollback: if the write operation fails, undoing the executed operation; resource release: releasing cross-chain channels or computing resources; monitoring indicator reporting: submitting data such as execution time and resource consumption.

[0205] S3-7-3-4-7: Integrate the actions selected by the operational agent to obtain the cross-chain arbitration execution strategy;

[0206] Cross-chain arbitration enforcement strategies include:

[0207] 1) Specific operation instructions: On the target chain: clearly specify which function of which smart contract to call to apply or undo the update, specify the data field to be modified and the new value, and trigger specific audit or logging functions; On the data source chain (if penalties are required): call the staking contract to deduct the staking amount of a specific participant and update the participant's credit score or status; Cross-chain communication instructions: notify the relevant chain or node of the details of the execution strategy through cross-chain bridges or other communication mechanisms;

[0208] 2) Execution order and timing: Define the execution order of operations, which operations need to be completed first, and which can be performed in parallel; set time windows or delays, for example, giving participants an opportunity to appeal or make corrections before the penalty is executed;

[0209] 3) Resource allocation: Specify which node(s) / smart contracts(s) will perform the specific operations and allocate the necessary computing resources;

[0210] 4) Exception handling and rollback plan: Define the measures to be taken if errors are encountered during execution (such as transaction failure or node crash), and provide a mechanism for partial or complete rollback to the state before the ruling;

[0211] 5) Dynamically adjust parameters:

[0212] Includes dynamically adjusted parameters, such as automatically increasing the verification difficulty of subsequent updates for nodes that frequently violate the rules, and imposing dynamic penalties on chains / nodes that frequently send invalid updates or fail to verify, such as lowering their reputation score, increasing the verification difficulty or delay of their subsequent updates, or the required collateral ratio.

[0213] 5) Monitoring and reporting requirements: Specify key metrics that need to be monitored (such as execution time, resource consumption, and participant response), and require the generation of detailed execution logs and reports for auditing and further learning;

[0214] In this embodiment, the cross-chain arbitration execution strategy is as follows: "The A chain node updates the water level of Reservoir C to 50.5 meters according to the arbitration result (or automatic resolution result), and records the update source (C Reservoir chain), time, and arbitration / resolution basis, etc."

[0215] S3-7-3-5: Execute the corresponding cross-chain arbitration execution strategy on the distributed ledger network and return to the spatial geographic data synchronization step S3-7-2;

[0216] This update operation itself will also be recorded on the target chain's distributed ledger;

[0217] S3-8: Encrypt the synchronized spatial geographic data according to the leaf node key, and store the resulting second encrypted spatial geographic data into the target chain;

[0218] Spatial geographic data is synchronously recorded on the distributed ledger of the target chain;

[0219] The formula is:

[0220]

[0221] In the formula, This is the second encrypted spatial geographic data.

[0222] This invention provides a method for incremental updating and synchronization of spatial geographic data based on a distributed ledger. By accurately capturing and synchronizing only incremental data, it significantly reduces the amount of data that needs to be transmitted and processed, greatly shortening synchronization time and reducing network bandwidth consumption and computing resource overhead. It is particularly suitable for spatial geographic data, which is typically large in volume, enabling faster and more economical updates and sharing of spatial geographic data. Utilizing zero-knowledge proof technology, the validity and integrity of incremental data can be verified without exposing its specific content, providing a higher level of privacy protection and ensuring that sensitive spatial information is not obtained by unauthorized parties. This breaks down privacy barriers in data sharing, enabling secure cross-chain collaboration of spatial geographic data involving sensitive information while maintaining privacy. Combining local consistency verification, hash verification, and zero-knowledge verification under a hybrid consensus mechanism ensures the accuracy and tamper-proof nature of incremental data during cross-chain transmission, more reliably guaranteeing the data quality of each node in the distributed ledger network. The data ultimately stored on the target chain is consistent and reliable, providing a solid data foundation for decisions that rely on accurate geospatial data. Multi-level encryption mechanisms protect cross-chain transactions, and a hybrid consensus and arbitration process executed using smart contracts (or similar mechanisms) enhances the security of the entire synchronization process, reduces the security risks of data synchronization in distributed, heterogeneous network environments, and strengthens the confidence of all parties participating in collaboration. A dynamic permission model is adopted, flexibly adjusting and verifying data access and operation permissions according to actual conditions, better adapting to complex and ever-changing collaboration scenarios and role changes, improving the flexibility and efficiency of distributed ledger network management, and better supporting the data security control needs in dynamic collaboration environments. Through a dispute arbitration model and an execution strategy generation model, conflicts or disputes that may arise during the synchronization process can be handled automatically and intelligently, resolving disputes and executing results faster and more fairly, ensuring the smooth progress of the synchronization process, and reducing system stagnation or data inconsistency caused by disputes.

[0223] This invention is not limited to the optional embodiments described above, and anyone can derive other various forms of products based on the inspiration of this invention. The specific embodiments described above should not be construed as limiting the scope of protection of this invention; the scope of protection of this invention should be determined by the claims, and the specification can be used to interpret the claims.

Claims

1. A method for incremental updating and synchronization of spatial geographic data based on distributed ledger, characterized in that: Includes the following steps: In a distributed ledger network of spatial geographic data, incremental data from the data source chain is captured based on the geocoded index, and hashes and zero-knowledge proofs of the incremental data are generated. Based on incremental data, corresponding hashes, and zero-knowledge proofs, a multi-level encryption mechanism is used to generate encrypted cross-chain transactions, which are then sent to several target chains of the distributed ledger network. The multi-level encryption mechanism is configured with multiple encryption keys, including a root key set in the data source chain, an intermediate key set in the smart contract, and a leaf node key set in the target chain. Includes the following steps: Based on the business requirements or preset rules for incremental data, identify the target chain identifiers to which the update needs to be synchronized and the smart contract addresses to process this update, and extract the intermediate keys of the corresponding smart contracts. Using the root key of the data source chain, the hash and zero-knowledge proof of the incremental data are encrypted to obtain the encrypted digest. Then, the intermediate key is used to encrypt the incremental data to obtain the encrypted incremental data. Based on the target chain identifier, smart contract address, encrypted digest, and encrypted incremental data, construct an encrypted cross-chain transaction and send the encrypted cross-chain transaction to the cross-chain bridge of the distributed ledger network; In the cross-chain bridge, the encrypted digest of the encrypted cross-chain transaction is decrypted based on the root key, and an incremental data hash list is generated based on all the decrypted hashes obtained, and a corresponding synchronization request is generated. On the target chain, a smart contract is used to listen to the cross-chain bridge, receive the synchronization request from the cross-chain bridge, and look up the list of incremental data hashes included in the synchronization request to locate the encrypted cross-chain transaction corresponding to the cross-chain bridge. The encrypted cross-chain transactions are initially verified. Once the initial verification is passed, the encrypted cross-chain transactions are received in the cross-chain bridge on the target link. Based on the hybrid consensus mechanism, several target chains are used to perform zero-knowledge verification on encrypted cross-chain transactions. After the zero-knowledge verification is passed, the incremental data is synchronized to all spatial geographic data of the target chains.

2. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 1, characterized in that: In a distributed ledger network of spatial geographic data, incremental data from the data source chain is captured based on a geocoded index, and hashes and zero-knowledge proofs of the incremental data are generated, including the following steps: Collect a number of raw spatial geographic data, store the raw spatial geographic data in a distributed ledger network, and build a geocoded index for the raw spatial geographic data; Based on the geocoding index, monitor the data status of the original spatial geographic data in the data source chain. When several data statuses change, capture the incremental data of the original spatial geographic data. Perform local consistency verification on the incremental data. After the local consistency verification passes, use a hash calculation function to generate the hash of the incremental data. Using a space-constrained zero-knowledge proof algorithm, we construct and generate zero-knowledge proofs for incremental data, and associate the zero-knowledge proofs and hashes with the corresponding incremental data.

3. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 2, characterized in that: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on encrypted cross-chain transactions using several target chains. After the zero-knowledge verification is passed, the incremental data is synchronized to all spatial geographic data of the target chains, including the following steps: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on encrypted cross-chain transactions on several target chains to obtain zero-knowledge verification results; If the zero-knowledge verification result is false, the zero-knowledge verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the data incremental update and synchronization process ends. If the zero-knowledge verification result is true, then the zero-knowledge verification passes, and a dynamic permission model is used to perform dynamic permission verification on each target chain to obtain the dynamic permission verification result. If the dynamic permission verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the incremental data update and synchronization process is terminated. If the dynamic permission verification result is successful, the encrypted incremental data in the encrypted cross-chain transaction is decrypted on the target chain according to the intermediate key to obtain the decrypted incremental data, and then proceed to the next step. Extract the leaf node key of each target chain, decrypt the first encrypted spatial geographic data stored in the target chain, and obtain the decrypted spatial geographic data. Based on the decrypted incremental data, the decrypted spatial geographic data of each target chain is synchronized to obtain synchronized spatial geographic data. Based on the leaf node key, the synchronized spatial geographic data is encrypted, and the resulting second encrypted spatial geographic data is stored in the target chain.

4. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 3, characterized in that: The dynamic permission model includes node role dimension, decryption behavior dimension, and illegal operation dimension; Using a dynamic permission model, dynamic permission verification is performed on each target chain to obtain the dynamic permission verification result, including the following steps: Extract the target chain reputation score and basic information of the decryption nodes in the target chain that receive encrypted cross-chain transactions; Using a dynamic permission model, node role characteristics, decryption behavior characteristics, and illegal operation characteristics are extracted from basic information; Dynamic permission verification is performed based on the target chain reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics to obtain dynamic permission verification results.

5. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 4, characterized in that: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on encrypted cross-chain transactions on several target chains to obtain the zero-knowledge verification results, including the following steps: Based on the multi-level decryption mechanism, the root key of the data source chain is extracted using each target chain, and the encrypted digest in the encrypted cross-chain transaction is decrypted based on the root key to obtain the decrypted zero-knowledge proof. Configure a verification circuit on each target chain, and configure the space constraints and property constraints corresponding to the decrypted zero-knowledge proof for each verification circuit. In each target chain, a verification circuit configured with spatial constraints is used to perform zero-knowledge verification on the decrypted zero-knowledge proof to obtain the zero-knowledge verification result. According to the hybrid consensus mechanism, the zero-knowledge verification results of all target chains are verified by hybrid consensus. If the hybrid consensus verification passes, the zero-knowledge verification result is output and the incremental data synchronization step is initiated. If the hybrid consensus verification fails, the corresponding encrypted cross-chain transaction is deleted on the target chain, an alarm signal is returned to the data source chain, and the incremental data update and synchronization process is terminated.

6. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 5, characterized in that: Based on the decrypted incremental data, the decrypted spatial geographic data of each target chain is synchronized to obtain synchronized spatial geographic data, including the following steps: Extract the local version of the decrypted spatial geographic data and the basis version of the decrypted incremental data for each target chain, and compare the local version and the basis version. If the comparison matches, the decrypted spatial geographic data of each target chain is synchronized based on the decrypted incremental data to obtain synchronized spatial geographic data, and the synchronization ends; otherwise, proceed to the next step. According to the dispute resolution mechanism, cross-chain arbitration is conducted on the decrypted incremental data and decrypted spatial geographic data, the cross-chain arbitration result is executed, and the incremental data capture step is returned.

7. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 6, characterized in that: According to the dispute resolution mechanism, cross-chain arbitration is conducted on the decrypted incremental data and decrypted spatial geographic data. The obtained cross-chain arbitration result is executed, and the incremental data capture step is returned, including the following steps: According to the dispute resolution mechanism, the conflict between the decrypted incremental data and the decrypted spatial geographic data is resolved. If the conflict is resolved successfully, the process returns to the incremental data synchronization step; otherwise, it proceeds to the next step. Extract disputed information from the decrypted incremental data and decrypted spatial geographic data, and send the disputed information to the dispute arbitration system of the distributed ledger network through a cross-chain mechanism; Using the dispute arbitration model of the dispute arbitration system, cross-chain arbitration decisions are made on dispute information to obtain cross-chain arbitration results; Based on the cross-chain arbitration ruling, the corresponding cross-chain arbitration execution strategy is generated using the dispute arbitration system's execution strategy generation model. The corresponding cross-chain arbitration execution strategy is executed on the distributed ledger network, and the spatial geographic data synchronization steps are returned.

8. The method for incremental updating and synchronization of spatial geographic data based on distributed ledger as described in claim 7, characterized in that: The dynamic permission model is constructed based on the N-GAN-Attention-MLP algorithm, the dispute arbitration model is constructed based on the LSTM algorithm, and the execution strategy generation model is constructed based on the HMARL algorithm.

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