Space geographic data increment updating and synchronizing method based on distributed account book

Abstract

The invention belongs to the technical field of data management, and discloses a spatial geographic data increment updating and synchronizing method based on a distributed account book. The method comprises the following steps: capturing incremental data from a data source chain according to a geocoding index in a distributed account book network of spatial geographic data, and generating hash and zero-knowledge proof of the incremental data; according to the incremental data, the corresponding hash and the zero-knowledge proof, generating an encrypted cross-chain transaction by using a multi-level encryption mechanism, and sending the encrypted cross-chain transaction to a plurality of target chains; and according to a hybrid consensus mechanism, performing zero-knowledge verification on the encrypted cross-chain transaction by using a plurality of target chains, and after the zero-knowledge verification is passed, synchronizing incremental data to all spatial geographic data of the target chains. According to the invention, the problems of low data synchronization efficiency, high total transmission cost, insufficient data privacy protection, risk in sharing, lack of a conflict resolution mechanism, insufficient cross-chain communication security and rigid authority management in the prior art are solved.

Description

Technical Field

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

[0002] The rapid development of applications such as smart cities, digital twins, and environmental monitoring has placed higher demands on the real-time, accuracy, and sharing of spatial geographic data (such as vector maps, remote sensing imagery, and 3D models). Traditional spatial data management methods, such as centralized databases, suffer from single points of failure, susceptibility to data tampering, and difficulty in achieving secure cross-departmental and cross-regional sharing.

[0003] Distributed ledger technologies (such as blockchain) offer new approaches to managing spatial and geographic data due to their decentralized, tamper-proof, and traceable nature. However, applying distributed ledgers to spatial and geographic data, especially in scenarios involving multi-chain collaboration, still faces numerous challenges: Defects of existing technology: 1) Inefficient data synchronization and high full-data transfer costs: Traditional centralized databases or simple distributed systems often require transferring large amounts of duplicate or unchanged data during data synchronization. For spatial and geographic data, which is often large in size, full-data synchronization not only consumes a large amount of network bandwidth but also slows down the synchronization process, affecting real-time performance.

[0004] 2) Insufficient data privacy protection and sharing risks: When sharing spatial geographic data across chains, directly transmitting raw data can lead to the leakage of sensitive information (such as precise location and specific area details), violating privacy protection regulations or commercial confidentiality requirements. Existing technologies lack sophisticated privacy protection methods.

[0005] 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 arbitration mechanisms, or rely on centralized arbitration, which is inefficient and may introduce single points of failure.

[0006] 4) Inadequate cross-chain communication security and vulnerability to attacks: Some existing cross-chain solutions may directly transmit plaintext data or have insufficient encryption strength, making them susceptible to eavesdropping or tampering during transmission. There is also a lack of effective verification of the integrity and source reliability of cross-chain transactions.

[0007] 5) Rigid permission management 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 collaboration scenarios (such as temporarily adding new participants or adjusting data access levels). Summary of the Invention

[0008] In order to solve the problems of low data synchronization efficiency, high full transmission cost, insufficient data privacy protection, sharing risks, lack of conflict resolution mechanism, insufficient cross-chain communication security and rigid authority management in the existing technology, the purpose of the present invention is to provide a spatial geographic data incremental update and synchronization method based on distributed ledger.

[0009] The technical solution adopted in the present invention is: A method for incremental updating and synchronization of spatial geographic data based on a distributed ledger, comprising the following steps: 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; Based on the incremental data, the corresponding hash, and the zero-knowledge proof, 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. According to 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 will be synchronized to all spatial geographic data of the target chain.

[0010] 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 a hash and zero-knowledge proof of the incremental data are generated, including the following steps: Collect some original spatial geographic data, store the original spatial geographic data in a distributed ledger network, and construct a geocoding index for the original spatial geographic data; According to the geocoding index, the data status of the original spatial geographic data of the data source chain is monitored, and several data states are transformed to capture the incremental data of the original spatial geographic data; Perform local consistency verification on the incremental data. Once the local consistency verification passes, use the hash calculation function to generate the hash of the incremental data. Use the space-constrained zero-knowledge proof algorithm to construct and generate zero-knowledge proof of incremental data, and associate the zero-knowledge proof and hash with the corresponding incremental data.

[0011] Furthermore, the multi-level encryption mechanism is provided with a multi-level encryption key, which includes 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.

[0012] Furthermore, based on the incremental data, the corresponding hash, and the zero-knowledge proof, a multi-level encryption mechanism is used to generate an encrypted cross-chain transaction, and the encrypted cross-chain transaction is sent to several target chains of the distributed ledger network, including the following steps: Based on the business needs or preset rules of incremental data, determine the target chain identifiers to which the update needs to be synchronized and the smart contract address that processes the update, and extract the corresponding smart contract intermediate key; Use the root key of the data source chain to encrypt the hash and zero-knowledge proof of the incremental data to obtain the encrypted digest, and use the intermediate key to encrypt the incremental data to obtain the encrypted incremental data; Construct an encrypted cross-chain transaction based on the target chain identifier, smart contract address, encrypted summary, and encrypted incremental data, and send the encrypted cross-chain transaction to the cross-chain bridge of the distributed ledger network; On the cross-chain bridge, the encrypted digest in 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 monitor the cross-chain bridge, receive the synchronization request from the cross-chain bridge, and search the incremental data hash list included in the synchronization request to locate the encrypted cross-chain transaction corresponding to the cross-chain bridge; Perform preliminary verification on the encrypted cross-chain transaction. After the preliminary verification passes, the encrypted cross-chain transaction in the cross-chain bridge is received on the target link.

[0013] Furthermore, according to the hybrid consensus mechanism, several target chains are used to perform zero-knowledge verification on the encrypted cross-chain transactions. After the zero-knowledge verification passes, the incremental data is synchronized to all spatial geographic data of the target chain, including the following steps: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on the 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, the zero-knowledge verification is passed, and the 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 result 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 data incremental update and synchronization process will be terminated; If the dynamic permission verification result passes, the encrypted incremental data in the encrypted cross-chain transaction is decrypted on the target chain based on the intermediate key to obtain the decrypted incremental data and proceed to the next step; Extracting the leaf node key of each target chain, decrypting the first encrypted spatial geographic data stored in the target chain, and obtaining the decrypted spatial geographic data; Based on the decrypted incremental data, synchronize the decrypted spatial geographic data of each target chain to obtain synchronized spatial geographic data; The synchronized spatial geographic data is encrypted according to the leaf node key, and the obtained second encrypted spatial geographic data is stored in the target chain.

[0014] Furthermore, the dynamic permission model includes the node role dimension, the decryption behavior dimension, and the illegal operation dimension; Using the 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’s reputation score and the basic information of the decryption node that receives the encrypted cross-chain transaction in the target chain; Use a dynamic permission model to extract node role characteristics, decryption behavior characteristics, and illegal operation characteristics from basic information; According to the target chain's reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics, dynamic permission verification is performed to obtain dynamic permission verification results.

[0015] Furthermore, according to the hybrid consensus mechanism, the encrypted cross-chain transaction is subjected to zero-knowledge verification on several target chains to obtain the zero-knowledge verification results, including the following steps: According to the multi-level decryption mechanism, each target chain is used to extract the root key of the data source 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 attribute constraints corresponding to the zero-knowledge proof after decryption for each verification circuit; In each target chain, a verification circuit configured with space constraints is used to perform zero-knowledge verification on the decrypted zero-knowledge proof to obtain a zero-knowledge verification result; According to the hybrid consensus mechanism, the zero-knowledge verification results of all target chains are verified through hybrid consensus. If the hybrid consensus verification passes, the zero-knowledge verification results are output and the incremental data synchronization step is entered; 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 data incremental update and synchronization process will end.

[0016] Furthermore, according to 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 reference version of the decrypted incremental data of each target chain, and compare the local version with the reference version; If the comparison is consistent, the decrypted spatial geographic data of each target chain is synchronized according to the decrypted incremental data to obtain the synchronized spatial geographic data and end the synchronization. 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 obtained cross-chain arbitration result is executed, and the incremental data capture step is returned.

[0017] Furthermore, according to the dispute resolution mechanism, cross-chain arbitration is conducted on the decrypted incremental data and the decrypted spatial geographic data, the 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 decrypted incremental data and decrypted spatial geographic data are conflict resolved. If the conflict resolution is successful, the process returns to the incremental data synchronization step; otherwise, it proceeds to the next step. Extract dispute information about decrypted incremental data and decrypted spatial geographic data, and send the dispute information to the dispute arbitration system of the distributed ledger network through a cross-chain mechanism; Use the dispute arbitration model of the dispute arbitration system to conduct cross-chain arbitration on the dispute information and obtain the cross-chain arbitration result; Based on the cross-chain arbitration award results, the dispute arbitration system’s execution strategy generation model is used to generate the corresponding cross-chain arbitration execution strategy; Execute the corresponding cross-chain arbitration execution strategy in the distributed ledger network and return to the spatial geographic data synchronization step.

[0018] 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.

[0019] The beneficial effects of the present invention are: The present invention provides a spatial geographic data incremental update and synchronization method based on a distributed ledger. By accurately capturing and synchronizing only incremental data, the method greatly reduces the amount of data that needs to be transmitted and processed, can significantly shorten the synchronization time, reduce network bandwidth consumption and computing resource overhead, and is particularly suitable for spatial geographic data, which is usually a large data type, and realizes faster and more economical spatial geographic data update and sharing. By 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, ensuring that sensitive spatial information is not obtained by unauthorized parties, breaking the privacy barrier in data sharing, and enabling spatial geographic data involving sensitive information to be cross-chain securely collaborated under the premise of maintaining privacy. Combined with local consistency verification, hash verification and zero-knowledge verification under a hybrid consensus mechanism, the accuracy and non-tampering of incremental data during cross-chain transmission are ensured, and the nodes in the distributed ledger network are more reliably guaranteed. The data ultimately stored on the (target chain) is consistent and trustworthy, providing a solid data foundation for decision-making that relies on accurate spatial geographic data. A multi-level encryption mechanism is used to 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 a distributed, heterogeneous network environment, and strengthens the confidence of all parties involved in collaboration. A dynamic permission model is adopted to flexibly adjust and verify data access and operation permissions based on actual conditions, making it more adaptable to complex and changing collaboration scenarios and role changes, improving the flexibility and efficiency of distributed ledger network management, and better supporting data security management needs in a dynamic collaborative environment. Through a dispute arbitration model and an execution strategy generation model, conflicts or disputes that may arise during the synchronization process can be automatically and intelligently handled, and disputes can be adjudicated and executed more quickly and fairly, ensuring the smooth progress of the synchronization process and reducing system stagnation or data inconsistencies caused by disputes.

[0020] Other beneficial effects of the present invention will be further described in the specific embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flowchart of the method for incremental updating and synchronization of spatial geographic data based on a distributed ledger in the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further explained below with reference to the accompanying drawings and specific embodiments.

[0023] Example: 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: S1: In the distributed ledger network of spatial geographic data, incremental data from the data source chain is captured based on the geocoding index, and a hash and zero-knowledge proof of the incremental data are generated, including the following steps: S1-1: Collect some original spatial geographic data, store the original spatial geographic data in the distributed ledger network, and build a geocoding index for the original spatial geographic data; The formula is:

[0024] Where, It is the original spatial geographic data; It is a collection of spatial geographic data; i is the data indicator; It is a storage operation function; For distributed ledger networks;

[0025] Where, The spatial characteristics, time version information, and ID information of the original spatial geographic data; Set functions for spatial feature extraction function, time version information extraction function, ID information extraction function, and geocoding index; For geocoding index; Spatial geographic data includes vector data, raster data (such as remote sensing images), three-dimensional models, etc. This embodiment applies to joint reservoir operation. Five reservoirs (A, B, C, D, and E) are located in the same river basin and jointly serve downstream flood control, water supply, and power generation needs. To achieve optimal operation, it is necessary to have real-time information on each reservoir's 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). This data is collected by different departments at different locations, and it is necessary to ensure its consistency, timeliness, and reliability so that the operation center can make informed decisions. The data sources for each reservoir and its surrounding areas include automated water level / flow monitoring stations, weather radar / rain gauges, drone / satellite remote sensing imagery collection points, ground inspection personnel, and databases from various departments (such as land and environmental protection). These data sources are connected to their respective data collection front-end machines through secure channels. These front-end machines serve as nodes in the "data source chain" and are responsible for the initial processing and uploading of raw spatial geographic data. S1-2: Based on the geocoding index, the data status of the original spatial geographic data in the data source chain is monitored. When several data states are transformed, the incremental data of the original spatial geographic data is captured. The formula is:

[0026] Where, It is the incremental data of the original spatial geographic data; Locate functions for data units; It is the preset trigger condition; j is the data indicator; The geocoding index is a spatiotemporal index structure (such as R-tree, quadtree, spatiotemporal cube, etc.). This index not only records spatial location information, but also records timestamps or version information, which is used to quickly locate and compare the status of data at different time points. Through the geocoding index, spatial and temporal comparisons are performed to accurately identify the data units with changes in the original spatial geographic data of the data source chain (such as modified geographic features, newly added image blocks, updated attribute values, etc.). This is more accurate than simple timestamp comparisons and can handle concurrent modifications and complex updates. For each changed element of a data unit, extract its unique identifier (such as element ID), change type (addition / deletion / modification), spatial information before and after the change (such as coordinates, geometric shape representation), attribute information (such as name, type, status, etc.), version information (new and old version numbers), data type, and additional necessary metadata such as operation timestamp 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 must be included to facilitate verification and rollback. Convert the original change information of the data unit change into structured, standardized incremental data to facilitate subsequent processing and verification; Use the latest block hash, timestamp, or an incrementing version number of the data set 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 (for example, the version of the data before the modification operation). This helps the target chain perform version verification and conflict detection when applying updates; In this embodiment, an R-tree or quadtree is used to index spatial locations and append a timestamp or version number. For reservoir A's reservoir boundary vector data, the index records its polygon coordinates, the reservoir to which it belongs (A), the version number (V1.0), and the timestamp (2025-07-14T00:00:00Z). For a remote sensing image covering reservoir B, the index records its bounding box, resolution, timestamp (shooting time), and version number (V1.0). This index is not only stored on a single node but is synchronized across the distributed ledger network to ensure that all participating nodes can quickly query it. Continuously monitor the data source chain (e.g., the monitoring station chain for Reservoir C). At 07:15, the water level sensor data for Reservoir C changes from 50.2 meters to 50.5 meters, exceeding the preset threshold. The data source chain node detects the change in the data point's status through a geocoding index (querying the water level monitoring point ID and its time series for Reservoir C). The system captures this incremental data: the monitoring point ID, old value (50.2), new value (50.5), timestamp (2025-07-14T07:15:00Z), and data type (water level). Simultaneously, if the drone has just transmitted a new image of the area surrounding Reservoir C, the system also captures this newly added raster data block and its metadata. S1-3: Perform local consistency verification on the incremental data. After the local consistency verification passes, use a hash calculation function, such as a cryptographic hash function such as SHA-256, to generate a hash of the incremental data. The formula is:

[0027] Where, is the hash of the incremental data; is the hash calculation function; Local consistency verification includes checking incremental data to see whether newly added features conflict with existing features, whether deletion operations are valid, whether modification operations are legal, geometric validity (e.g., polygons do not intersect with each other), attribute value domain constraints (e.g., elevation values ​​are within a reasonable range), topological consistency (e.g., matching adjacent land parcel boundaries), and data association (e.g., consistency in the association IDs between the attribute table and the feature table). This verification is based on the locally stored original data or its latest version. In this embodiment, local consistency verification is performed on the captured incremental data, including checking whether the new value (50.5) is within the sensor range and whether it is logically consistent with other relevant data in the same area (such as the reduction in outbound traffic); checking whether the metadata (such as geographic location and shooting time) is valid, and whether the image data is intact; S1-4: Use space-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), and Short Proofs for Confidential Transactions and More (Bulletproofs)) to construct and generate zero-knowledge proofs for incremental data, and associate the zero-knowledge proofs and hashes with the corresponding incremental data. The formula is:

[0028] Where, Proof of space constraints and attribute constraints for incremental data; Generate functions for space constraint proofs and property constraint proofs; These are spatial constraint rules and attribute constraint rules; is the zero-knowledge proof algorithm parameter; Zero-knowledge proof for incremental data; To prove the combinatorial function; Spatial Constraint Proof (P1): Use a geometric algorithm (such as the Gilbert–Johnson–Keerthi (GJK) algorithm) to verify whether the coordinates (x, y) of the updated feature are within a preset update region (for example, a polygonal boundary). Generate a zero-knowledge proof P1 that proves that the coordinates of the feature are within region R. In this example, it proves that the water level monitoring point is indeed located within the reservoir area of ​​​​C. Without revealing the specific coordinate values, this proof can limit the coordinate range error to less than 0.1 meters, ensuring spatial accuracy. 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, proving that "the updated attributes satisfy all rules. In this example, it proves that the water level value of 50.5 meters is within the effective range of the sensor (e.g., 0-60 meters) and the change (+0.3 meters) is within a reasonable physical range (e.g., the maximum possible increase in the past hour)." The proof does not reveal the specific attribute values. This proof supports multi-condition AND / OR logical combinations. S2: Based on the incremental data, the corresponding hash, and the zero-knowledge proof, 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. The target chains include the data chains of other reservoirs and the central dispatch control chain. The multi-level encryption mechanism is equipped with multiple levels of encryption keys, which include the root key set in the data source chain, the intermediate key set in the smart contract, and the leaf node key set in the target chain; Based on the incremental data, the corresponding hash, and the zero-knowledge proof, a multi-level encryption mechanism is used to generate an encrypted cross-chain transaction, which is then sent to several target chains in the distributed ledger network, including the following steps: S2-1: Based on the business needs or preset rules of the incremental data, determine the target chain identifiers to which the update needs to be synchronized and the smart contract address that processes the update, and extract the corresponding smart contract intermediate key; The formula is:

[0029] Where, The target chain identifier set and the corresponding smart contract address set that need to be synchronized; is the positioning function; For business needs and preset rules;

[0030] Where, It is the intermediate key of the smart contract; For smart contracts; k is the smart contract indicator, corresponding to k target chain; It is the intermediate key extraction function; S2-2: Use the root key of the data source chain to encrypt the hash and zero-knowledge proof of the incremental data to obtain the encrypted digest, and use the intermediate key to encrypt the incremental data to obtain the encrypted incremental data; The formula is:

[0031] Where, is the encrypted summary; It is the incremental data after encryption; is the encryption function; is the root key; S2-3: Construct an encrypted cross-chain transaction based on the target chain identifier, smart contract address, encrypted summary, and encrypted incremental data, and send the encrypted cross-chain transaction to the cross-chain bridge of the distributed ledger network; The formula is:

[0032] Where, is the encrypted summary; It is the incremental data after encryption; is the encryption function; is the root key;

[0033] Where, For encrypted cross-chain transactions; Identifies the target chain. is a random number and a timestamp; Build functions for cross-chain transactions; A cross-chain bridge is a pre-defined cross-chain channel (e.g., based on an oracle, relay node, or specific cross-chain protocol); S2-4: On the cross-chain bridge, the encrypted digest in 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; The formula is:

[0034] Where, is the decrypted hash; is the decryption function; It is the incremental data hash list; J is the total number of data; For synchronous requests; Generate functions for synchronous requests; S2-5: On the target chain, a smart contract is used to monitor the cross-chain bridge. For example, the target chain can discover new incremental updates by subscribing to block headers or specific events on the source data chain on the cross-chain bridge. The target chain then receives synchronization requests from the cross-chain bridge and searches for the incremental data hash list included in the synchronization request to locate the encrypted cross-chain transaction corresponding to the cross-chain bridge. S2-6: Perform preliminary verification on the encrypted cross-chain transaction. After passing the preliminary verification, the encrypted cross-chain transaction in the cross-chain bridge is received on the target link; The formula is:

[0035] Where, To initially verify the results; It is a preliminary verification function; S3: Based on the hybrid consensus mechanism, several target chains are used to perform zero-knowledge verification on the encrypted cross-chain transactions. After the zero-knowledge verification passes, the incremental data is synchronized to all spatial geographic data of the target chain, including the following steps: S3-1: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on the encrypted cross-chain transaction on several target chains to obtain the zero-knowledge verification result, including the following steps: S3-1-1: Based on the multi-level decryption mechanism, use each target chain to extract the root key of the data source chain, and use the root key to decrypt the encrypted digest in the encrypted cross-chain transaction to obtain the decrypted zero-knowledge proof; The formula is:

[0036] Where, It is the zero-knowledge proof after decryption; S3-1-2: Configure a verification circuit on each target chain, and configure the space constraints and attribute constraints corresponding to the zero-knowledge proof after decryption for each verification circuit; The formula is:

[0037] Where, Target chain Verification circuit; Configure functions for constraints; S3-1-3: On each target chain, use a verification circuit configured with a spatial constraint to perform zero-knowledge verification on the decrypted zero-knowledge proof and obtain a zero-knowledge verification result; The formula is:

[0038] Where, It is the result of zero-knowledge verification; is the zero-knowledge verification function; Verification P1: Confirm that "the water level monitoring point is indeed located within the reservoir area of ​​C" and the error is within the allowable range, but do not obtain the specific coordinates; Verification P2: Confirms that "the water level value of 50.5 meters is within the effective range of the sensor (e.g., 0-60 meters), and the change (+0.3 meters) is within a reasonable physical range (e.g., the maximum possible increase in the past hour)", but does not obtain specific attribute values; S3-1-4: Based on the hybrid consensus mechanism, a hybrid consensus algorithm (for example, a variant combining Proof of Work (PoW) / Proof of Stake (PoS) with 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 zero-knowledge verification, they reach consensus through a consensus protocol. If the hybrid consensus verification passes, the zero-knowledge verification result is output and the incremental data synchronization step is entered; The formula is:

[0039] Where, Verify the results for hybrid consensus; is the hybrid consensus verification function; K is the total number of target chains; S3-1-5: 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 data incremental update and synchronization process ends; 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; S3-3: If the zero-knowledge verification result is true, the zero-knowledge verification passes, and the dynamic permission model is used to perform dynamic permission verification on each target chain to obtain the dynamic permission verification result; The dynamic permission model includes the node role dimension, the decryption behavior dimension, and the illegal operation dimension; The dynamic permission model is built 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. Using the dynamic permission model, dynamic permission verification is performed on each target chain to obtain the dynamic permission verification result, including the following steps: A-1: Extract the target chain's reputation score and the basic information of the decryption node that receives the encrypted cross-chain transaction in the target chain; A-2: Using N GANs with a dynamic permission model, we extract node role characteristics (e.g., administrator, ordinary user, auditor), decryption behavior characteristics (e.g., decryption frequency, decrypted data volume, decryption time), and illegal operation characteristics (e.g., historical violation records, abnormal behavior patterns) from basic information. A-3: Perform dynamic permission verification based on the target chain's reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics to obtain dynamic permission verification results; Specifically, based on dynamic attention weights, the Attention mechanism is used to perform weighted fusion of the target chain reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics to obtain weighted fusion features. MLP is then used to perform dynamic permission verification based on the weighted fusion features to obtain dynamic permission verification results. The dynamic permission verification results include the node permission type and the predicted label of pass or fail. In this embodiment, based on the role characteristics of the requesting node (such as "administrator"), its decryption behavior characteristics (first time decrypting this type of data), and its historical operation record characteristics (no illegal operations), it is determined that it has update permission and the dynamic permission verification passes; S3-4: If the dynamic permission verification result 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; S3-5: If the dynamic permission verification result passes, the encrypted incremental data in the encrypted cross-chain transaction is decrypted on the target chain based on the intermediate key to obtain the decrypted incremental data and proceed to the next step; The formula is:

[0040] Where, It is the incremental data after decryption; 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 decrypted spatial geographic data; The formula is:

[0041] Where, It is the decrypted spatial geographic data; is the leaf node key; The first encrypted spatial geographic data; S3-7: Synchronize the decrypted spatial geographic data of each target chain based on the decrypted incremental data to obtain synchronized spatial geographic data, including the following steps: S3-7-1: Extract the local version of the decrypted spatial geographic data and the reference version of the decrypted incremental data of each target chain, and compare the local version with the reference version; S3-7-2: If the comparison is consistent, synchronize the decrypted spatial geographic data of each target chain based on the decrypted incremental data to obtain the synchronized spatial geographic data, and end the synchronization and proceed to step S3-8. Otherwise, proceed to the next step; The formula is:

[0042] Where, For synchronized spatial geographic data; is a synchronous function; Synchronize the decrypted spatial geographic data of each target chain and update the decrypted spatial geographic database or status on the target chain based on the change type (addition / deletion / modification) of the decrypted incremental data. For example, if it is a modification, the old value is replaced by the new value in the incremental data; if it is a deletion, the feature is marked or removed. The update operation itself will also be recorded in the block of the target chain to form a new version history. S3-7-3: 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, including the following steps: S3-7-3-1: Based on the dispute resolution mechanism, resolve conflicts between 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. Conflict resolution methods include: Rule-based automatic resolution: automatically resolves conflicts based on predefined rules, including: timestamp / version number-based: retain updates with higher (newer) version numbers; business rule-based: for modifications to the same feature, give priority to updates from specific sources (such as authoritative agencies), or decide based on the type of update (such as accident reports taking precedence over regular maintenance); merge: try to merge conflicting updates if possible (for example, attribute modifications can be merged, but spatial location modifications are usually difficult to merge); reject: refuse to apply the current increment and try again after the conflict is resolved; Verification resolution based on post-decryption zero-knowledge proofs: If the conflict involves multiple valid post-decryption zero-knowledge proofs, we can further analyze the content of the post-decryption zero-knowledge proofs (without leaking specific data) or rely on on-chain consensus to decide which update should be retained; Manual intervention: Complex or important conflicts can be flagged for manual judgment and resolution by administrators. For example, if Chain B also receives information about increased rainfall in the area surrounding Reservoir C, and Chain A believes this may lead to a greater water level increase, manual judgment may be needed on how to integrate the two pieces of information. The administrator intervenes through the management interface, reviews the proof and raw data fragments, and decides whether to directly adopt the water level from Chain C or make corrections based on rainfall information. If Chain A and Chain B disagree on how to update the overall status of Reservoir C (water level + rainfall), and a quick resolution cannot be achieved automatically or manually, a cross-chain arbitration process is triggered. S3-7-3-2: Extract dispute information about decrypted incremental data and decrypted spatial geographic data, and send the dispute information to the dispute arbitration system of the distributed ledger network through a cross-chain mechanism; 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 by block header hash), and dispute submission timestamp; In this example, 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), marking this as a potential conflict or requiring an update; S3-7-3-3: Use the dispute arbitration model of the dispute arbitration system to extract the semantic features of the dispute information, conduct cross-chain arbitration on the semantic features, and obtain the cross-chain arbitration result; The dispute arbitration model is built based on the Long Short-Term Memory (LSTM) algorithm; Cross-chain arbitration awards include: 1) Conflict type identification: Clearly indicate the type of potential conflict the dispute belongs to (e.g., data overwrite conflict, sequence conflict, authorization conflict, data integrity conflict, etc.), which facilitates more precise subsequent processing; 2) Priority determination: Urgency determination: If the dispute information mentions words such as "emergency," "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 correction that does not affect core functionality; Reasonableness determination: Based on the context, determine which update is more consistent with the preset business rules or logic. For example, if one update describes an operation that clearly violates business rules, the model may determine that another update is more reasonable; 3) Decision Confidence / Confidence Score: The model outputs a confidence score, indicating its level of certainty about the decision outcome. Decisions with low confidence may require a higher level of confirmation or trigger a more complex arbitration process. 4) Responsibility attribution suggestions: 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); In this embodiment, the cross-chain arbitration decision is "use the measured water level value provided by Chain C, and leave the impact of rainfall for the next comprehensive assessment"; S3-7-3-4: Based on the cross-chain arbitration award, use the dispute arbitration system’s execution strategy generation model to generate the corresponding cross-chain arbitration execution strategy; The execution strategy generation model is built based on the Hierarchical Multi-Agent Reinforcement Learning (HMARL) algorithm and consists of a macro-strategy generation layer and an operational strategy generation layer. The macro-strategy generation layer is equipped with a macro-agent built based on the Deep Q-Network (DQN) algorithm. It 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 results and system environment). The operational strategy generation layer includes several operational agents built based on the Proximal Policy Optimization (PPO) algorithm. They are responsible for refining the macro-strategy into specific, executable operational instructions. Each operational agent corresponds to a participant (such as a data source chain, a target chain, multiple arbitration nodes, and multiple execution nodes). The operational agent represents an entity, and multiple operational agents are required to work together to execute the arbitration. Based on the cross-chain arbitration award, the dispute arbitration system’s execution strategy generation model is used to generate the corresponding cross-chain arbitration execution strategy, including the following steps: S3-7-3-4-1: The cross-chain arbitration decision is input into the macro-strategy generation layer of the execution strategy generation model, and the global state of the current cross-chain environment is collected, including: the chains involved and their current states (such as block height and network latency); the states of the participating parties (such as whether the nodes are online and resource availability); the specific content of the arbitration decision (such as which transactions need to be rolled back and which updates need to be applied); and the progress of the current execution task; S3-7-3-4-2: Encode the cross-chain arbitration decision and the global state of the current cross-chain environment into a high-dimensional vector as input to the macro agent; S3-7-3-4-3: Evaluate the expected rewards of all possible macro actions (e.g., “perform rollback,” “apply high-priority updates,” “request more evidence,” “coordinate multi-party actions”) in the current state, based on the Q-value function learned by the macro agent; S3-7-3-4-4: The macro agent selects the macro action with the highest Q value, which represents a high-level execution direction. For example, it may decide to "update the measured water level value provided by the C chain"; S3-7-3-4-5: The macro-strategy generation layer broadcasts the selected macro-strategy (e.g., "update the measured water level value provided by chain C") to all relevant operational agents in the operational strategy generation layer. Each operational agent initializes its operational-level decision-making state based on the received macro-strategy and its own local state (e.g., local data, cached information). The local state includes: the current data of the local chain; the communication status with other participants (such as the source chain, other target chains); and the specific tasks required by the macro strategy. S3-7-3-4-6: Each agent selects an action from the space of possible actions based on its learned strategy (based on the context of the macro strategy); for example: The action space of the data source chain 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 exception if the data is invalid or the signature is incorrect; mortgage operation (if punishment is required): calling the mortgage contract to deduct the mortgage of the violator; The action space of the target chain agent includes: transaction preparation: constructing a smart contract to call a transaction (such as updating water level data); broadcast transaction: broadcasting the transaction to the target chain network; log triggering: calling the audit contract to record the operation log (such as the update source and time); state rollback: if the execution fails, triggering the rollback transaction to restore the original data; resource request: applying for channel resources or computing resources of the cross-chain bridge; The action space of the arbitration node agent includes: legitimacy verification: checking whether the operation complies with cross-chain rules (such as data scope and permissions); logging: recording arbitration decisions and execution details in the on-chain log; dynamic parameter adjustment: updating the reputation score or verification difficulty of the violating party; appeal processing: suspending penalties within the time window and waiting for the participant to appeal; exception reporting: reporting any arbitration logic conflicts to the macro agent; The action space of the execution node 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 fails, undo the executed operation; resource release: releasing cross-chain channels or computing resources; monitoring indicator reporting: submitting data such as execution time and resource consumption; S3-7-3-4-7: Integrate the actions selected by the operating agents to obtain the cross-chain arbitration execution strategy; Cross-chain arbitration execution strategies include: 1) Specific operational instructions: On the target chain: specify which smart contract function to call to apply or undo the update, specify the data fields to be modified and the new values, and trigger specific audit or logging functions; On the data source chain (if penalties are required): call the mortgage contract, deduct the mortgage of a specific participant, and update the participant's reputation score or status; Cross-chain communication instructions: notify the relevant chains or nodes of the details of the execution policy through a cross-chain bridge or other communication mechanism; 2) Execution order and timing: Define the order in which operations are executed, which operations need to be completed first, and which can be completed in parallel; set time windows or delays, for example, to give participants an opportunity to appeal or make corrections before penalties are imposed; 3) Resource allocation: specify which node(s) / smart contracts will execute specific operations and allocate necessary computing resources; 4) Exception handling and rollback plan: Defines the actions to be taken if errors are encountered during execution (e.g., transaction failure, node downtime), and provides a mechanism for partial or complete rollback to the pre-rule state; 5) Dynamically adjust parameters: Includes dynamically adjusted parameters, for example, automatically increasing the verification difficulty of subsequent updates for nodes that frequently violate the rules; imposing dynamic penalties on chains / nodes that frequently send invalid updates or verification failures, such as reducing their reputation scores, increasing the verification difficulty or delay of their subsequent updates, and the required collateral ratio; 5) Monitoring and reporting requirements: Specify key metrics to be monitored (e.g., execution time, resource consumption, and participant responses), and require the generation of detailed execution logs and reports for auditing and further learning. In this embodiment, the cross-chain arbitration execution strategy is: "Based on the arbitration result (or automatic resolution result), the A-chain node updates the water level of Reservoir C to 50.5 meters and records the update source (Reservoir C chain), time, and arbitration / resolution basis, etc." 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; This update operation itself will also be recorded on the distributed ledger of the target chain; S3-8: Encrypt the synchronized spatial geographic data according to the leaf node key, and store the obtained second encrypted spatial geographic data into the target chain; Spatial geographic data is synchronously recorded on the distributed ledger of the target chain; The formula is:

[0043] Where, The second encrypted spatial geographic data.

[0044] The present invention provides a spatial geographic data incremental update and synchronization method based on a distributed ledger. By accurately capturing and synchronizing only incremental data, the method greatly reduces the amount of data that needs to be transmitted and processed, can significantly shorten the synchronization time, reduce network bandwidth consumption and computing resource overhead, and is particularly suitable for spatial geographic data, which is usually a large data type, and realizes faster and more economical spatial geographic data update and sharing. By 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, ensuring that sensitive spatial information is not obtained by unauthorized parties, breaking the privacy barrier in data sharing, and enabling spatial geographic data involving sensitive information to be cross-chain securely collaborated under the premise of maintaining privacy. Combined with local consistency verification, hash verification and zero-knowledge verification under a hybrid consensus mechanism, the accuracy and non-tampering of incremental data during cross-chain transmission are ensured, and the nodes in the distributed ledger network are more reliably guaranteed. The data ultimately stored on the (target chain) is consistent and trustworthy, providing a solid data foundation for decision-making that relies on accurate spatial geographic data. A multi-level encryption mechanism is used to 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 a distributed, heterogeneous network environment, and strengthens the confidence of all parties involved in collaboration. A dynamic permission model is adopted to flexibly adjust and verify data access and operation permissions based on actual conditions, making it more adaptable to complex and changing collaboration scenarios and role changes, improving the flexibility and efficiency of distributed ledger network management, and better supporting data security management needs in a dynamic collaborative environment. Through a dispute arbitration model and an execution strategy generation model, conflicts or disputes that may arise during the synchronization process can be automatically and intelligently handled, and disputes can be adjudicated and executed more quickly and fairly, ensuring the smooth progress of the synchronization process and reducing system stagnation or data inconsistencies caused by disputes.

[0045] The present invention is not limited to the above optional embodiments. Anyone can derive various other forms of products based on the teachings of the present invention. The above specific embodiments should not be construed as limiting the scope of protection of the present invention. The scope of protection of the present invention shall be based on the scope defined in the claims, and the description can be used to interpret the claims.

Claims

1. A method for incremental updating and synchronization of spatial geographic data based on a distributed ledger, characterized by: The steps include: 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; Based on the incremental data, the corresponding hash, and the zero-knowledge proof, 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. According to 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 will be synchronized to all spatial geographic data of the target chain.

2. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 1, characterized in that: In a distributed ledger network for 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. This involves the following steps: Collect some original spatial geographic data, store the original spatial geographic data in a distributed ledger network, and construct a geocoding index for the original spatial geographic data; According to the geocoding index, the data status of the original spatial geographic data of the data source chain is monitored, and several data states are transformed to capture the incremental data of the original spatial geographic data; Perform local consistency verification on the incremental data. Once the local consistency verification passes, use the hash calculation function to generate the hash of the incremental data. Use the space-constrained zero-knowledge proof algorithm to construct and generate zero-knowledge proof of incremental data, and associate the zero-knowledge proof and hash with the corresponding incremental data.

3. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 2, characterized in that: The multi-level encryption mechanism is provided with a multi-level encryption key, and the multi-level encryption key includes 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.

4. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 3, characterized in that: Based on the incremental data, the corresponding hash, and the zero-knowledge proof, a multi-level encryption mechanism is used to generate an encrypted cross-chain transaction, which is then sent to several target chains in the distributed ledger network, including the following steps: Based on the business needs or preset rules of incremental data, determine the target chain identifiers to which the update needs to be synchronized and the smart contract address that processes the update, and extract the corresponding smart contract intermediate key; Use the root key of the data source chain to encrypt the hash and zero-knowledge proof of the incremental data to obtain the encrypted digest, and use the intermediate key to encrypt the incremental data to obtain the encrypted incremental data; Construct an encrypted cross-chain transaction based on the target chain identifier, smart contract address, encrypted summary, and encrypted incremental data, and send the encrypted cross-chain transaction to the cross-chain bridge of the distributed ledger network; On the cross-chain bridge, the encrypted digest in 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 monitor the cross-chain bridge, receive the synchronization request from the cross-chain bridge, and search the incremental data hash list included in the synchronization request to locate the encrypted cross-chain transaction corresponding to the cross-chain bridge; Perform preliminary verification on the encrypted cross-chain transaction. After the preliminary verification passes, the encrypted cross-chain transaction in the cross-chain bridge is received on the target link.

5. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 4, characterized in that: According to the hybrid consensus mechanism, several target chains are used to perform zero-knowledge verification on encrypted cross-chain transactions. After the zero-knowledge verification passes, the incremental data is synchronized to all spatial geographic data of the target chain, including the following steps: Based on the hybrid consensus mechanism, zero-knowledge verification is performed on the 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, the zero-knowledge verification is passed, and the 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 result 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 data incremental update and synchronization process will be terminated; If the dynamic permission verification result passes, the encrypted incremental data in the encrypted cross-chain transaction is decrypted on the target chain based on the intermediate key to obtain the decrypted incremental data and proceed to the next step; Extracting the leaf node key of each target chain, decrypting the first encrypted spatial geographic data stored in the target chain, and obtaining the decrypted spatial geographic data; Based on the decrypted incremental data, synchronize the decrypted spatial geographic data of each target chain to obtain synchronized spatial geographic data; The synchronized spatial geographic data is encrypted according to the leaf node key, and the obtained second encrypted spatial geographic data is stored in the target chain.

6. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 5, characterized in that: The dynamic permission model includes the node role dimension, the decryption behavior dimension, and the illegal operation dimension; Using the 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’s reputation score and the basic information of the decryption node that receives the encrypted cross-chain transaction in the target chain; Use a dynamic permission model to extract node role characteristics, decryption behavior characteristics, and illegal operation characteristics from basic information; According to the target chain's reputation score, node role characteristics, decryption behavior characteristics, and illegal operation characteristics, dynamic permission verification is performed to obtain dynamic permission verification results.

7. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 6, characterized in that: 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: According to the multi-level decryption mechanism, each target chain is used to extract the root key of the data source 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 attribute constraints corresponding to the zero-knowledge proof after decryption for each verification circuit; In each target chain, a verification circuit configured with space constraints is used to perform zero-knowledge verification on the decrypted zero-knowledge proof to obtain a zero-knowledge verification result; According to the hybrid consensus mechanism, the zero-knowledge verification results of all target chains are verified through hybrid consensus. If the hybrid consensus verification passes, the zero-knowledge verification results are output and the incremental data synchronization step is entered; 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 data incremental update and synchronization process will end.

8. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 7, 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 reference version of the decrypted incremental data of each target chain, and compare the local version with the reference version; If the comparison is consistent, the decrypted spatial geographic data of each target chain is synchronized according to the decrypted incremental data to obtain the synchronized spatial geographic data and end the synchronization. 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 obtained cross-chain arbitration result is executed, and the incremental data capture step is returned.

9. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 8, 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 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 decrypted incremental data and decrypted spatial geographic data are conflict resolved. If the conflict resolution is successful, the process returns to the incremental data synchronization step; otherwise, it proceeds to the next step. Extract dispute information about decrypted incremental data and decrypted spatial geographic data, and send the dispute information to the dispute arbitration system of the distributed ledger network through a cross-chain mechanism; Use the dispute arbitration model of the dispute arbitration system to conduct cross-chain arbitration on the dispute information and obtain the cross-chain arbitration result; Based on the cross-chain arbitration award results, the dispute arbitration system’s execution strategy generation model is used to generate the corresponding cross-chain arbitration execution strategy; Execute the corresponding cross-chain arbitration execution strategy in the distributed ledger network and return to the spatial geographic data synchronization step.

10. The method for incremental updating and synchronization of spatial geographic data based on a distributed ledger according to claim 9, 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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