An asset management platform and method based on digital twin technology

Abstract

The embodiment of the present application relates to the technical field of asset management, and discloses an asset management method based on digital twin technology, comprising: automatically generating a plurality of hierarchical spatial blocks based on multi-source geospatial data; constructing a block relationship graph for each second-level dynamic sub-block; based on a benchmark address library associated with the second-level dynamic sub-block, performing entity analysis and semantic fusion on multi-source heterogeneous business data flowing into the dynamic sub-block; dynamically calculating the governance heat value of each second-level dynamic sub-block; in response to the output of the block health degree evaluation model of a specific dynamic sub-block being lower than a threshold value, performing root cause analysis based on the block relationship graph, and generating a collaborative disposal strategy. The embodiment of the present application breaks the technical defects in traditional asset management, such as static solidification of spatial division, and inability to adapt in real time with changes in asset state / environment, so that the spatial block can accurately map the actual distribution and dynamic change of assets in the physical world.

Description

Technical Field

[0001] This invention relates to the field of asset management technology, and specifically to an asset management platform and method based on digital twin technology. Background Technology

[0002] Currently, urban assets are expanding rapidly in scale and becoming increasingly complex, encompassing various types such as municipal infrastructure, public buildings, transportation facilities, energy pipelines, and environmental monitoring equipment. Traditional asset management methods, relying mainly on manual inspections, paper-based ledgers, and decentralized information systems, are no longer sufficient to meet the demands of modern, refined, and intelligent urban governance. Therefore, designing a solution that facilitates urban asset management has become a pressing technical problem for those skilled in the art. Summary of the Invention

[0003] To address the aforementioned shortcomings, this invention discloses an asset management method based on digital twin technology, which enables efficient management of data assets in smart cities.

[0004] The first aspect of this invention discloses an asset management platform based on digital twin technology, comprising: The intelligent sensing and access layer is configured to access the real-time status data of physical assets through various IoT protocols and sensing devices, and to assign an automatically identifiable physical identity code to each physical asset. The digital twin and data management layer, connected to the intelligent sensing and access layer, includes: An asset twin construction engine is configured to create and dynamically update digital twins for physical assets based on multi-source data. The digital twins are bound to geometric models, business attributes, and real-time data streams. The asset blockchain notarization service is configured to generate a decentralized digital identity for each of the digital twins on the management consortium blockchain and to perform on-chain trusted notarization of key events throughout the asset's lifecycle. The asset intelligence engine layer, connected to the digital twin and data management layer, includes: An asset valuation model is configured to predict asset status based on multi-source data from the digital twin; The asset association engine is configured to automatically associate an asset with a spatial block unit in the digital space base when the asset status is abnormal, and generate an urban governance event work order. The platform service layer is configured to encapsulate query, control, analysis, and on-chain verification services for digital twins of assets, and provides services through APIs; The application interaction layer is configured to provide an application interface for a panoramic asset cockpit and mobile inspection.

[0005] As an optional implementation, in a first aspect of the present invention, the asset association engine includes: Based on the block data model, establish the ownership relationship between the digital twin of the asset and its corresponding second-level dynamic sub-block; When the asset health and value dynamic assessment model outputs an abnormality, or when an asset chain state change event is detected, the block unit to which the asset belongs is automatically located. Based on pre-configured rules, a structured event work order containing asset information, location information, and disposal suggestions is generated and pushed to the responsible department or personnel associated with the block unit; The application interaction layer also provides a panoramic cockpit for assets through full vector multi-threaded mapping. The application interaction layer is used to: respond to user operations, dynamically retrieve, read and render asset digital twins, block unit boundaries and related vector data from the spatial database in real time to realize real-time visualization of asset status, spatial distribution and related information, without the need to pre-generate static tiles; And / or, the underlying data architecture of the digital space base includes: A data lake is configured to store mirror copies of the original data according to the data source directory. It supports the inclusion of relational databases, offline spatial files, and structured files into the lake and records the data lineage. The data warehouse, connected to the data lake, is configured to perform quality checks, cleaning, transformation, and modeling on the data entering the lake according to predefined data standards, in order to form data assets that can be used for analysis. Raw sensor data from the Internet of Things is accessed through the data lake and can be processed in real time through a stream computing pipeline or after being governed by the data warehouse, and used to drive the updating of the digital twin and intelligent asset evaluation.

[0006] A second aspect of this invention discloses an asset management method based on digital twin technology, comprising: On the digital space base, multiple levels of spatial blocks are automatically generated based on multi-source geospatial data. The spatial blocks include a first-level standard block and a second-level dynamic sub-block. The second-level dynamic sub-block dynamically adjusts the boundary of the first-level standard block based on real-time acquired sensing data. A block relationship graph is constructed for each second-level dynamic sub-block, and the block relationship graph is used to encode the spatial or functional association between the corresponding dynamic sub-block and other dynamic sub-blocks; Based on the reference address library associated with the second-level dynamic sub-block, entity parsing and semantic fusion are performed on the multi-source heterogeneous business data flowing into the dynamic sub-block to form intra-block fused data set with a unified identifier; Based on the preset block heat assessment model, the governance heat value of each second-level dynamic sub-block is dynamically calculated, and the priority of data calculation and updating is assigned to each dynamic sub-block based on the governance heat value; If the output of the block health assessment model for a specific dynamic sub-block is lower than a threshold, root cause analysis is performed based on the block relationship graph, and a collaborative treatment strategy is generated.

[0007] As an optional implementation, in a second aspect of the present invention, the automatic generation of multiple levels of spatial blocks based on multi-source geospatial data includes: Based on the preset urban governance grid division rules, an initial spatial unit is generated as the first-level standard block; Acquire real-time spatial contour data from drones, high-precision maps, or image recognition devices; Based on the real-time spatial contour data and preset event density or business rules, at least one first-level standard block is divided into multiple second-level dynamic sub-blocks, and their vector boundaries are automatically corrected.

[0008] As an optional implementation, in a second aspect of the present invention, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: Configure different business concept ontology libraries for different types of dynamic sub-blocks; When multiple business data entries describing the same geographic entity that have semantic conflicts are received, the business concept ontology library corresponding to the dynamic sub-block type and the conflict resolution rules of the corresponding sub-block are invoked to generate the merged data record. Alternatively, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: Receive entity information containing non-standard address descriptions; The place name and address matching engine is invoked to match the non-standard address description with a high-precision reference address database and convert it into standard coordinates or standard address codes. Based on the converted standard coordinates or address codes, entities are associated with corresponding second-level dynamic sub-blocks, buildings, or specific spatial units; When address matching cannot uniquely determine the spatial unit to which an entity belongs, or when different data sources conflict in their spatial attribution of the same entity, a preset association rule is used for decision-making; the association rule includes proximity association, association based on business jurisdiction weight, or association based on data source priority.

[0009] As an optional implementation, in a second aspect of the present invention, the allocation of data calculation and update priorities for each dynamic sub-block based on the governance heat value includes: The data processing task covering the entire city will be broken down into subtasks, with each second-level dynamic sub-block as an independent computing unit. The subtasks are placed into a dynamic priority queue, where the subtasks corresponding to dynamic sub-blocks with high governance heat values ​​are allocated higher computing resource priority and shorter execution cycles. The computation results of each dynamic sub-task are cached independently, and when upper-level aggregation data is needed, the cached results are directly aggregated.

[0010] As an optional implementation, in a second aspect of the present invention, the root cause analysis based on the block relation map includes: The target sub-blocks are identified as those whose output of the block health assessment model is below a threshold. Traverse the block relationship graph to identify one or more associated sub-blocks that are strongly associated with the target sub-block; Analyze the real-time status indicators of the associated sub-blocks to determine whether the deterioration of the health of the target sub-block is due to the influence of the associated sub-blocks; The output of the block health assessment model responding to a specific dynamic sub-block is below a threshold, including: Monitor state change events issued by the smart contract account corresponding to the digital twin of the physical asset; When a state change event indicating a deterioration in health status is captured, the block health of the second-level dynamic sub-block to which it belongs is determined to be below a threshold.

[0011] As an optional implementation, in a second aspect of the present invention, after automatically generating multiple levels of spatial blocks based on multi-source geospatial data, the method further includes: Associate one or more physical assets with the second-level dynamic sub-block; Create a corresponding digital twin for each of the physical assets; A decentralized digital identity is generated on the management consortium blockchain for each digital twin as its immutable unique identifier, wherein the initial attribute information of the digital twin is associated with the unique identifier; The generation of decentralized digital identities on the management consortium blockchain includes: On a management consortium blockchain based on a set management mechanism, a corresponding smart contract account is deployed for the digital twin; wherein, the address of the smart contract account serves as the decentralized digital identity, and the smart contract encodes business logic that defines the rules for the transfer of ownership or change of status of the physical assets; The process of creating a corresponding digital twin for each of the physical assets includes: A globally unique block identification code is assigned to the physical asset using a unified coding method. The unified coding method includes generating a composite code containing a grid location code and a multi-level category code as the basis for the unique identifier. The grid location code represents the precise location of the second-level dynamic sub-block or the physical asset in the spatial grid, and the multi-level category code represents its asset type, ownership department, or functional classification. When the boundary of the second-level dynamic sub-block is dynamically refined or the physical asset is split / merged, a new unique identifier is generated by adjusting the precision of the grid location code or the granularity of the multi-level category code, while maintaining the traceability relationship with the original identifier. The initial file information of the physical asset is accurately linked to its second-level dynamic sub-block and its corresponding three-dimensional spatial location by address matching and spatial association using the entity fusion method. The binding relationship between the physical asset, digital twin, its corresponding second-level dynamic sub-block, and its unique identifier on the management consortium blockchain is established based on the block identification code.

[0012] As an optional implementation, in a second aspect of the present invention, the blockchain network is a management consortium blockchain, and its management nodes include: Multiple council nodes operated by a predetermined governing body, the council nodes being configured to manage the initial parameters of the blockchain network; Multiple witness nodes are elected by multiple asset stakeholders through voting, and these witness nodes are configured to take turns generating blocks; Multiple vote-holding nodes, which obtain voting rights for the witness nodes through data rights; wherein, the data consensus mechanism requires that a block is confirmed as the final block after it is generated and verified and signed by more than a set proportion of witness nodes.

[0013] As an optional implementation, in a second aspect of the present invention, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: A semantic conflict resolution engine is used to process heterogeneous data from different business systems that involve the same physical asset. The engine performs the following operations: Based on pre-built cross-departmental business ontology mapping relationships, business concepts describing the same real-world entity in different data sources are identified; wherein, the cross-departmental business ontology mapping relationships include automatically extracting departmental-level business concepts and their attributes from business specifications and data dictionaries of different departments; based on the semantic similarity of concept names, shared instance data or association relationships, equivalence, parent-child or related relationships between business concepts of different departments are automatically or semi-automatically discovered, and mapping relationships are established. The application configures a conflict resolution rule chain for specific business scenarios to automatically process identified semantic conflict data. The rule chain includes deduplication rules, merging rules, or data priority rules. Based on the evaluation feedback of the conflict resolution results, the parameters of the conflict resolution rule chain are automatically adjusted; The method further includes: Identify the source data regions and computational paths associated with core metrics that are frequently accessed or of high interest in the cockpit, and mark them as hot data paths; Monitor the update status of the source data on which the hot data path depends; In response to the detection of an update to the source data, only the partial computational subgraph related to the hot data path is triggered and executed to update the corresponding cockpit visualization data incrementally, rather than recalculating the entire data base.

[0014] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: The asset management method based on digital twin technology in this embodiment of the invention uses standard blocks to build the basic spatial framework for asset management. At the same time, it dynamically adjusts the boundaries of dynamic sub-blocks based on real-time sensing data. This breaks through the technical defects of static and fixed spatial division in traditional asset management, which cannot adapt to changes in asset status / environment in real time. It enables spatial blocks to accurately map the actual distribution and dynamic changes of assets in the physical world, effectively solving problems such as management blind spots and asset positioning deviations caused by the mismatch between static spatial division and dynamic asset status. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the asset management method based on digital twin technology disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram of the block update process disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the address matching process disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of the task decomposition process disclosed in an embodiment of the present invention; Figure 5 This is a schematic diagram of the root cause analysis process disclosed in an embodiment of the present invention; Figure 6This is a schematic diagram of the data association structure disclosed in an embodiment of the present invention; Figure 7 This is a schematic diagram showing the data architecture scenario disclosed in an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that the terms first, second, third, fourth, etc., in the specification and claims of this invention are used to distinguish different objects, not to describe a specific order. The terms used in the embodiments of this invention include and have, and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products, or devices.

[0019] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating an asset management method based on digital twin technology disclosed in an embodiment of the present invention. The execution entity of the method described in this embodiment is an execution entity composed of software and / or hardware. This execution entity can receive relevant information via wired and / or wireless means and can send certain instructions. It may also have certain processing and storage functions. This execution entity can control multiple devices, such as remote physical servers or cloud servers and related software, or local hosts or servers and related software that perform related operations on devices located in a certain location. In some scenarios, it can also control multiple storage devices, which may be placed in the same location as the devices or in different locations. Figures 1 to 7 As shown, this asset management method based on digital twin technology includes the following steps: S101: On the digital space base, multiple levels of spatial blocks are automatically generated based on multi-source geospatial data. The spatial blocks include a first-level standard block and a second-level dynamic sub-block. The second-level dynamic sub-block dynamically adjusts the boundary of the first-level standard block based on real-time acquired sensing data. S102: Construct a block relationship graph for each second-level dynamic sub-block, the block relationship graph being used to encode the spatial or functional association between the corresponding dynamic sub-block and other dynamic sub-blocks; S103: Based on the reference address library associated with the second-level dynamic sub-block, perform entity parsing and semantic fusion on the multi-source heterogeneous business data flowing into the dynamic sub-block to form intra-block fused data set in a unified identifier; S104: Based on the preset block heat assessment model, dynamically calculate the governance heat value of each second-level dynamic sub-block, and assign a priority for data calculation and updating to each dynamic sub-block based on the governance heat value; S105: In response to the output of the block health assessment model for a specific dynamic sub-block being lower than a threshold, root cause analysis is performed based on the block relationship graph, and a collaborative treatment strategy is generated.

[0020] Traditional methods use fixed administrative or geographical grids, which cannot reflect dynamic changes in assets, events, and people flow. The method in this invention achieves flexible scaling of management units through a first-level standard block and a second-level dynamic sub-block. It can dynamically generate sub-blocks based on real-time perceived data, allowing management attention to automatically focus on areas that truly require attention.

[0021] In practice, by associating with a baseline address database, entity parsing and semantic fusion are performed on various types of business data flowing into different dynamic sub-blocks, assigning them unified identifiers. This breaks down the traditional data silos categorized by department or system, forming context-rich intra-block fused data centered on spatial units, laying the foundation for panoramic analysis.

[0022] The block heat assessment model in this invention calculates a dynamic governance priority for each dynamic sub-block. This allows computing resources, storage resources, and processing bandwidth to be preferentially allocated to the most needed, most active, or highest-risk areas, achieving global efficiency optimization with limited resources. When the block health assessment model issues an early warning, traditional investigation methods are time-consuming and labor-intensive. The method in this invention utilizes a block relationship graph to automatically analyze the status of problematic sub-blocks and other spatially or functionally related sub-blocks (such as upstream energy blocks, downstream transportation blocks, and parallel similar facility blocks), quickly identifying whether the problem is localized or propagated, thereby generating collaborative disposal strategies involving multiple departments and assets.

[0023] This invention achieves intelligent clustering and perception of managed objects through dynamic spatial units, understands complex internal relationships through relationship graphs, releases the intrinsic value of multi-source data through semantic fusion, and ultimately drives proactive and optimized management decisions through heat and health models. It transforms a descriptive digital mirror into an intelligent management hub with perception, analysis, decision-making, and optimization capabilities.

[0024] More preferably, such as Figure 2As shown, the automatic generation of multiple levels of spatial blocks based on multi-source geospatial data includes: S1011: Based on the preset urban governance grid division rules, generate the initial spatial unit as the first-level standard block; S1012: Acquire real-time spatial contour data from drones, high-precision maps, or image recognition devices; S1013: Based on the real-time spatial contour data and preset event density or business rules, at least one first-level standard block is divided into multiple second-level dynamic sub-blocks, and their vector boundaries are automatically corrected.

[0025] The solution in this invention uses pre-defined urban governance grid division rules (such as existing administrative divisions, community grids, and responsibility areas) as the basis for the first-level standard blocks, enabling the newly built digital twin system to seamlessly integrate with the existing management system. Managers can work within a familiar standard block framework, reducing resistance to system deployment and learning costs, and ensuring the continuity of governance work.

[0026] Specifically, the standard blocks provide a stable and unified spatial reference benchmark and clear attribution of responsibilities. Based on this, dynamic sub-blocks are generated through real-time data, ensuring framework stability and timely data updates. This avoids management chaos caused by frequent boundary changes while accurately responding to rapidly changing realities. By comprehensively utilizing multi-source data from drones, high-precision maps, and image recognition equipment, it is possible to comprehensively and accurately acquire real-time spatial morphological changes of the target area from multiple dimensions, including aerial, ground, static, and dynamic perspectives.

[0027] In this embodiment of the invention, the generation of dynamic sub-blocks no longer relies on a fixed map, but is driven by real-time spatial contour data and event density or business rules. Event density-driven, for example, during large-scale events, when the pedestrian density in a certain area within a standard block consistently exceeds a threshold, the system can automatically divide that area into independent high-density sub-blocks, triggering higher-level monitoring and traffic control plans. Business rule-driven, for example, based on major construction project plans, the system automatically divides the construction impact area from the standard block into temporary construction management sub-blocks and associates them with specific safety and environmental monitoring rules. This generation method ensures that the boundaries of management units remain synchronized with changes in the actual physical world and governance needs, achieving on-demand block division.

[0028] More preferably, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: Configure different business concept ontology libraries for different types of dynamic sub-blocks; When multiple business data entries describing the same geographic entity that have semantic conflicts are received, the business concept ontology library corresponding to the dynamic sub-block type and the conflict resolution rules of the corresponding sub-block are invoked to generate the merged data record. Or, such as Figure 3 As shown, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: S1031: Receive entity information containing non-standard address descriptions; S1032: Call the place name address matching engine to match the non-standard address description with a high-precision reference address library and convert it into standard coordinates or standard address codes; S1033: Based on the converted standard coordinates or address codes, associate entities with corresponding second-level dynamic sub-blocks, buildings, or specific spatial units; S1034: When address matching cannot uniquely determine the spatial unit to which an entity belongs, or when different data sources conflict in their spatial attribution of the same entity, a preset association rule is used for decision-making; the association rule includes proximity association, association based on business jurisdiction weight, or association based on data source priority.

[0029] This invention provides different business concept ontology libraries for different types of dynamic sub-blocks (such as commercial area sub-blocks, industrial facility sub-blocks, and transportation hub sub-blocks), which is equivalent to equipping different business scenarios with dedicated semantic dictionaries and relationship graphs. For example, in the commercial area sub-block, passenger flow may be associated with consumer behavior analysis; in the transportation hub sub-block, passenger flow is associated with capacity scheduling and safety warnings.

[0030] When data from different systems (such as security, property management, and consumer systems) contains semantic conflicts in describing the same entity (e.g., a person) (e.g., different identity identifiers or behavioral category definitions), the system can map the multi-source descriptions to a unified semantic framework that conforms to the current governance scenario, based on the business ontology library of the current sub-block, generating an unambiguous data record rich in business context. This solves the core problem of cross-departmental data being unable to be directly integrated due to inconsistent terminology and standards.

[0031] In this embodiment of the invention, by calling a place name and address matching engine and a high-precision reference address library, various non-standard address descriptions (such as "downstairs at XX company" or "small square on the east side of the main building") are standardized and mapped. This ensures that regardless of the data source, as long as it contains spatial information, it can be accurately associated with a specific second-level dynamic sub-block, or even a specific building or room. This is a fundamental and crucial step in realizing panoramic data analysis indexed by space.

[0032] In this embodiment of the invention, conflict resolution rules matching the sub-block type are invoked. For example, for device status, it can be regularly agreed that the highest priority sensor data or the latest maintenance report shall prevail. When the address matching result is ambiguous or the location of different data sources is inconsistent, preset association rules (proximity association, business weight association, data source priority association) are used for automated decision-making.

[0033] This avoids the lag and inefficiency of relying on manual intervention for data reconciliation. Through a pre-set rule engine, data cleaning, conflict resolution, and fusion are completed in real time and automatically as data flows in, ensuring the consistency and high quality of the data used for subsequent analysis and decision-making. This is a necessary condition for achieving automated and intelligent operation of the system. Through the above steps, the data flowing into each dynamic sub-block is no longer a raw, chaotic flow of information, but rather contains precise data correspondences.

[0034] Specifically, the method further includes: Build and maintain a multi-source heterogeneous reference address library, which integrates address and coordinate information from multiple data sources with different authority levels; A dynamic confidence weight is associated with each address entry in the baseline address database, and the dynamic confidence weight is dynamically calculated based on the historical matching accuracy and data freshness of the corresponding data source; When receiving input containing non-standard address text, multimodal matching is performed. The multimodal matching includes: performing natural language processing on the non-standard address text to extract spatial relationship descriptions; matching the non-standard address text with the baseline address database, wherein the matching process simultaneously considers text similarity, the degree of conformity between the extracted spatial relationship descriptions and the spatial context of the baseline address, and the location trajectory information accompanying the input; determining the final geographic coordinates based on the matching results and the dynamic confidence weights; and when multiple matching coordinates of the same address are detected to have deviations exceeding a threshold, a crowdsourced correction process is initiated to optimize the baseline address database and the matching model.

[0035] The method further includes: When the system detects that the dispersion of multiple reported coordinates for the same semantic address exceeds a preset threshold, it automatically marks it as a suspected deviation address. The suspected deviation address and related context information are pushed to the authenticated terminal user for location confirmation; Based on the received confirmation feedback, the coordinates and confidence weights of the corresponding addresses in the multi-source reference address database are updated, and the updated data is used to perform online learning and optimization of the multimodal matching algorithm. This method achieves continuous data optimization and updating.

[0036] More preferably, such as Figure 4As shown, the prioritization of data calculation and updating for each dynamic sub-block based on the governance heat value includes: S1041: The data processing task covering the entire city is decomposed into subtasks, with each second-level dynamic sub-block as an independent computing unit; S1042: Place the subtask into a dynamic priority queue, wherein the subtask corresponding to the dynamic sub-block with a high governance heat value is assigned a higher computing resource priority and a shorter execution cycle; S1043: Independently cache the computation results of each dynamic sub-block sub-task, and directly aggregate the cached results when upper-level aggregation data is needed.

[0037] Traditional city-wide data processing (such as indicator calculation and model inference) typically employs timed batch polling or fixed-period updates, resulting in high response latency and an inability to prioritize the most urgent areas. The solution in this invention decomposes the city-wide task into independent sub-tasks based on dynamic sub-blocks, and dynamically prioritizes these sub-tasks according to real-time calculated governance heat values.

[0038] This ensures that computing resources and cycles are timely and prioritized for the hottest and most critical areas. Data in high-intensity sub-blocks can be processed and updated at near real-time or higher frequencies, while computation frequency can be appropriately reduced in low-intensity areas. This results in immediate response to critical areas as a whole, greatly improving the timeliness of management response.

[0039] Performing uniform and periodic calculations across the entire city would result in a large amount of redundant calculations on low-activity, unchanging areas, leading to a waste of resources. The solution in this invention dynamically adjusts the execution cycle of subtasks (shorter cycles for high-activity areas and longer cycles for low-activity areas), enabling on-demand allocation of computing resources. This frees up valuable computing power from idle areas and concentrates it on processing truly valuable and dynamic hotspots.

[0040] By breaking down city-wide tasks into independent subtasks and placing them into a dynamic priority queue, the computational tasks of each sub-block are independent of each other. The failure or delay of one sub-block will not directly affect other sub-blocks, improving the system's robustness. The system can dynamically expand or shrink computing nodes horizontally to handle tasks in the queue based on the overall load and heat distribution, perfectly adapting to tidal computing demands. This design enables the system to economically and efficiently handle large-scale urban computing scenarios, avoiding the single-point bottlenecks and resource rigidity problems that can occur with centralized processing.

[0041] In practical implementation, independently caching the calculation results of each dynamic sub-block is a key design feature. This means that when it is necessary to generate an aggregated view of the entire city or a certain area (such as the city's safety index or the total energy consumption of a certain administrative district), the system does not need to re-trigger the original calculations of all the underlying sub-blocks, but directly aggregates the latest cached results of each sub-block.

[0042] This greatly improves the speed of upper-level macro-level queries and report generation, achieving a seamless connection from micro-level real-time calculations to macro-level instant insights; and saves a lot of repetitive aggregation computing resources, enabling the system to continuously invest more computing power in high-priority real-time subtask processing.

[0043] More preferably, such as Figure 5 As shown, the root cause analysis based on the block relationship graph includes: S1051: Determine the dynamic sub-blocks whose output of the block health assessment model is below the threshold as target sub-blocks; S1052: Traverse the block relationship graph to identify one or more associated sub-blocks that are strongly associated with the target sub-block; S1053: Analyze the real-time status indicators of the associated sub-blocks to determine whether the deterioration of the health of the target sub-block is due to the influence of the associated sub-blocks; The output of the block health assessment model responding to a specific dynamic sub-block is below a threshold, including: Monitor state change events issued by the smart contract account corresponding to the digital twin of the physical asset; When a state change event indicating a deterioration in health status is captured, the block health of the second-level dynamic sub-block to which it belongs is determined to be below a threshold.

[0044] In traditional asset management, when a problem occurs in a certain area (sub-block) (such as deterioration of environmental indicators), the investigation is often limited to the area itself. The solution of this invention can automatically identify related sub-blocks that are strongly associated with the target sub-block in terms of space or function by traversing the block relationship graph (such as upstream energy supply blocks, downstream drainage blocks, adjacent transportation hub blocks, and similar equipment cluster blocks).

[0045] This allows the system to transcend physical or administrative boundaries and analyze problems from a broader systemic perspective. For example, if the energy consumption of a building sub-block spikes abnormally, root cause analysis might reveal that it is caused by a surge in computing load in its associated data center sub-block.

[0046] After identifying related sub-blocks, the solution does not perform static logical reasoning, but analyzes the status indicators of these related sub-blocks in real time. By comparing time series, analyzing the correlation between indicators, or invoking pre-defined causal models, it determines whether the problem in the target sub-block is caused by changes in the status of related sub-blocks (such as failure, overload, or shutdown). This upgrades root cause analysis from probabilistic speculation to evidence chain analysis based on real-time data, significantly improving the accuracy and reliability of diagnosis and providing a solid data foundation for generating effective collaborative response strategies.

[0047] In practical implementation, this invention introduces a state monitoring and reliable early warning mechanism by monitoring state change events issued by the smart contract accounts corresponding to the digital twins of physical assets. The system does not need to periodically poll the state of all assets; instead, the digital twins of the assets themselves proactively issue signals when critical state changes occur. State changes in smart contracts are transparent, traceable, and tamper-proof on the blockchain or distributed ledger. This ensures high reliability of alert information and avoids false alarms caused by data tampering or transmission errors, making it particularly suitable for scenarios with extremely high security and auditing requirements. Linking health threshold determination to smart contract events makes the triggering of the entire early warning mechanism extremely efficient, automated, and reliable.

[0048] More preferably, after automatically generating multiple levels of spatial blocks based on multi-source geospatial data, the method further includes: Associate one or more physical assets with the second-level dynamic sub-block; Create a corresponding digital twin for each of the physical assets; A decentralized digital identity is generated on the management consortium blockchain for each digital twin as its immutable unique identifier, wherein the initial attribute information of the digital twin is associated with the unique identifier; The generation of decentralized digital identities on the management consortium blockchain includes: On a management consortium blockchain based on a set management mechanism, a corresponding smart contract account is deployed for the digital twin; wherein, the address of the smart contract account serves as the decentralized digital identity, and the smart contract encodes business logic that defines the rules for the transfer of ownership or change of status of the physical assets; The process of creating a corresponding digital twin for each of the physical assets includes: A globally unique block identification code is assigned to the physical asset using a unified coding method. The unified coding method includes generating a composite code containing a grid location code and a multi-level category code as the basis for the unique identifier. The grid location code represents the precise location of the second-level dynamic sub-block or the physical asset in the spatial grid, and the multi-level category code represents its asset type, ownership department, or functional classification. When the boundary of the second-level dynamic sub-block is dynamically refined or the physical asset is split / merged, a new unique identifier is generated by adjusting the precision of the grid location code or the granularity of the multi-level category code, while maintaining the traceability relationship with the original identifier. The initial file information of the physical asset is accurately linked to its second-level dynamic sub-block and its corresponding three-dimensional spatial location by address matching and spatial association using the entity fusion method. The binding relationship between the physical asset, digital twin, its corresponding second-level dynamic sub-block, and its unique identifier on the management consortium blockchain is established based on the block identification code.

[0049] This invention achieves precise spatial and business-level location of physical assets by generating a composite code that includes a grid location code (precise spatial coordinates) and a multi-level category code (asset attributes, ownership, and function) as a block identification code. This surpasses traditional serial numbers or simple classification codes, allowing the asset's identity to inherently carry its spatial attribution and business semantics. This encoding method provides a unified, unambiguous, and machine-readable identity index for large and complex asset networks at the city or park level, serving as the cornerstone for all subsequent data association, analysis, and decision-making.

[0050] When dynamic sub-block boundaries are refined or the asset itself is split / merged, the scheme generates new identifiers by adjusting the precision of the grid location code or the granularity of the category code, while maintaining the traceability relationship with the original identifier.

[0051] In practice, address matching and spatial association are used to accurately link the initial asset profile information to its corresponding second-level dynamic sub-block and three-dimensional spatial location. This ensures that the digital twin is not only an abstract model of the asset, but also a precise, complete, and interactive mirror image of it in virtual space. When the boundaries of the dynamic sub-block are adjusted, the identity and spatial affiliation of the assets within it can be automatically and accurately updated accordingly, achieving synchronous evolution and deep binding between digital and physical spaces.

[0052] Specifically, a strong binding was established between these four key entities using block identifiers as the link. This enables: The location and status (sub-blocks, twins) of an asset can be checked.

[0053] All assets (sub-blocks, asset list) within the location can be viewed.

[0054] On-chain identity can verify asset ownership and key history (identity, smart contract records).

[0055] This forms a highly cohesive asset management object model, providing unprecedented convenience and reliability for complex queries, statistical analysis, and collaborative operations across levels and departments.

[0056] Based on consortium blockchains and standardized digital identities, different management departments, operating companies, and service providers can develop and integrate their respective applications on a shared foundation of rules (smart contracts) and trust (on-chain identities), enabling them to perform authorized operations and contribute data to assets. This solves the problems of closed systems and poor interoperability in traditional centralized systems, allowing digital twin asset management platforms to evolve into open and scalable ecosystems that attract diverse stakeholders to participate and jointly enhance the value and efficiency of asset management.

[0057] More preferably, the blockchain network is a managed consortium blockchain, whose management nodes include: Multiple council nodes operated by a predetermined governing body, the council nodes being configured to manage the initial parameters of the blockchain network; Multiple witness nodes are elected by multiple asset stakeholders through voting, and these witness nodes are configured to take turns generating blocks; Multiple vote-holding nodes, which obtain voting rights for the witness nodes through data rights; wherein, the data consensus mechanism requires that a block is confirmed as the final block after it is generated and verified and signed by more than a set proportion of witness nodes.

[0058] This invention sets up a council node operated by a predetermined management agency (such as a government authority, park management committee, or asset owner) and grants it the power to manage the initial parameters of the blockchain network. This solves the regulatory problems that may arise from the complete decentralization of public blockchains, allowing the consortium blockchain to be embedded in the existing administrative or asset management framework from the very beginning, meeting the requirements of being regulatory and auditable, and clearing institutional obstacles for its application in critical infrastructure and public asset fields.

[0059] By electing witness nodes through a vote by asset stakeholders (such as users, operating companies, and service providers) and granting these nodes the right to produce blocks, a distributed distribution of governance power is achieved. This avoids excessive concentration of power in a single management body, creating a system of checks and balances where the council sets the direction and witnesses ensure implementation. It grants substantial governance participation rights to key participants within the ecosystem, enhancing the system's fairness and broad acceptance, making it a true private blockchain of a management alliance rather than a single entity.

[0060] A fixed number of elected witness nodes take turns producing blocks, which greatly reduces the number of nodes participating in consensus. This enables the consortium blockchain to achieve high transaction throughput and short block times, fully adapting to the needs of digital twin asset management, which may generate a large number of state updates, event records, and ownership change transactions, ensuring the system's usability and responsiveness.

[0061] The consensus mechanism requires that after a block is generated, it must be verified and signed by more than a set proportion (e.g., 2 / 3) of the witness nodes to be confirmed as the final block. Once a block is confirmed by a majority of witnesses, the transaction is considered irreversibly completed. This provides timely and deterministic guarantees for the state changes of the upper-layer asset digital twin and the execution results of smart contracts, enabling business logic based on on-chain state (such as asset transfer completion and alarm triggering) to be immediately and securely adopted by subsequent applications, perfectly matching the real-time requirements of management operations.

[0062] Witnesses are elected and take turns producing blocks, introducing a competition and rotation mechanism. This incentivizes witness nodes to work honestly and efficiently, prevents power from becoming entrenched, and enhances the system's security resilience and active decentralization. Final data confirmation depends not only on the producer but also on verification and signing by more than a predetermined proportion of witness nodes. This means the legitimacy of a block is endorsed by a majority of elected witnesses. Under a well-designed election and reputation mechanism, this is extremely costly, effectively mitigating security threats such as double-spending attacks.

[0063] Voting nodes gain voting rights over witness nodes through data rights, which can be understood as a measure of contribution to the data in the digital twin system (such as providing high-quality asset data, timely maintenance of information, and participation in numerous verification tasks). This creates a positive feedback loop in the incentive ecosystem: participants contribute high-quality data, gain more data rights (voting rights), have a greater influence on witness elections, and thus influence the governance direction of the consortium blockchain, making it more favorable to honest contributors. This fundamentally incentivizes all parties within the ecosystem to actively and honestly maintain and contribute asset data, solving the problem of insufficient node participation often faced by consortium blockchains.

[0064] More preferably, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: A semantic conflict resolution engine is used to process heterogeneous data from different business systems that involve the same physical asset. The engine performs the following operations: Based on pre-built cross-departmental business ontology mapping relationships, business concepts describing the same real-world entity in different data sources are identified; wherein, the cross-departmental business ontology mapping relationships include automatically extracting departmental-level business concepts and their attributes from business specifications and data dictionaries of different departments; based on the semantic similarity of concept names, shared instance data or association relationships, equivalence, parent-child or related relationships between business concepts of different departments are automatically or semi-automatically discovered, and mapping relationships are established. The application configures a conflict resolution rule chain for specific business scenarios to automatically process identified semantic conflict data. The rule chain includes deduplication rules, merging rules, or data priority rules. Based on the evaluation feedback of the conflict resolution results, the parameters of the conflict resolution rule chain are automatically adjusted; Specifically, the method in this embodiment of the invention further includes: Identify the source data regions and computational paths associated with core metrics that are frequently accessed or of high interest in the cockpit, and mark them as hot data paths; Monitor the update status of the source data on which the hot data path depends; In response to the detection of an update to the source data, only the partial computational subgraph related to the hot data path is triggered and executed to update the corresponding cockpit visualization data incrementally, rather than recalculating the entire data base.

[0065] In this invention, the construction of cross-departmental business ontology mapping relationships no longer relies on the arduous task of manual, line-by-line comparison. Instead, it utilizes automated or semi-automated technologies (based on semantic similarity, shared instances, and associations) to discover complex relationships (equivalence, parent-child relationships, and correlations) between concepts from different departments. This is equivalent to establishing an automatic translation and association rule base for the various departmental systems (such as urban management, transportation, and safety supervision) that operate independently in urban governance. It significantly reduces the human and time costs of building and maintaining a unified data model and can be dynamically updated as the business concepts of each department evolve, providing a high-quality semantic dictionary for subsequent accurate entity identification and conflict resolution.

[0066] The introduction of conflict resolution rule chains (deduplication, merging, and priority rules) transforms the fusion process from a simple data overlay or splicing to an intelligent adjudication based on the logic of specific business scenarios. For example, in security scenarios, real-time alarm data from sensors has higher priority than daily inspection records.

[0067] More importantly, the system can automatically adjust the parameters of the rule chain based on the evaluation feedback of the conflict resolution results. This means that the fusion engine has the ability to continuously learn and optimize. If a certain rule frequently causes errors in subsequent analysis, the system can automatically reduce its weight or trigger manual review, thereby continuously improving the accuracy and reliability of data fusion and achieving the effect of becoming smarter with use.

[0068] From automated ontology mapping to scenario-based rule resolution, and then to feedback-based rule optimization, a complete semantic governance closed loop is formed. This transforms data fusion from a static, pre-configured ETL process into a dynamic, adaptive, and continuously evolving intelligent service, effectively addressing the challenges brought by changes in business rules and the integration of new data sources.

[0069] In practical implementation, by identifying frequently accessed or highly focused core metrics in the cockpit and marking their hot data paths, the system can accurately grasp the manager's attention focus. This achieves a shift in computing resources from uniform dispersion to focused allocation. When the source data supporting these core metrics is updated, the system no longer initiates a time-consuming full data recalculation; instead, it triggers and executes only the sub-graphs related to the hot data paths, updating the visualized data incrementally. The dual-disc cockpit's dashboard can react quickly to key changes, ensuring that managers always see the latest critical situation, greatly improving the timeliness of decision-making and user experience.

[0070] In ultra-large-scale digital twin scenarios, recompiling all data is extremely costly and time-consuming. Incremental update mechanisms, through precise targeting and tailoring of computational tasks, avoid a large amount of unnecessary repetitive computation. This significantly reduces the computational load, enabling the system to support more complex models and more frequent data updates with limited computing resources. Simultaneously, it shortens the end-to-end latency from data changes to visual presentation, giving the large-screen dashboard true real-time capabilities, enabling it to navigate rapidly changing urban operating scenarios.

[0071] Specifically, the aggregation of multi-source heterogeneous business data on the digital space platform is achieved through ETL spatiotemporal aggregation and exchange technology, which includes: Performing cross-network heterogeneous spatial data synchronization at the database layer, the synchronization process includes: Serialize the spatial data layers of the source system; The serialized data is transmitted through a data push or extraction mechanism; The received data is standardized, cleaned, and subjected to coordinate and projection transformations, and dirty data is processed. The processed data is deserialized and reconstructed into a spatiotemporal data layer and attribute table compatible with the target digital space base.

[0072] The asset management method based on digital twin technology in this embodiment of the invention uses standard blocks to build the basic spatial framework for asset management. At the same time, it dynamically adjusts the boundaries of dynamic sub-blocks based on real-time sensing data. This breaks through the technical defects of static and fixed spatial division in traditional asset management, which cannot adapt to changes in asset status / environment in real time. It enables spatial blocks to accurately map the actual distribution and dynamic changes of assets in the physical world, effectively solving problems such as management blind spots and asset positioning deviations caused by the mismatch between static spatial division and dynamic asset status.

[0073] Example 2 The asset management platform based on digital twin technology disclosed in this invention includes: an intelligent sensing and access layer, configured to access real-time status data of physical assets through various Internet of Things protocols and sensing devices, and to assign an automatically identifiable physical identity code to each physical asset; The digital twin and data management layer, connected to the intelligent sensing and access layer, includes: An asset twin construction engine is configured to create and dynamically update digital twins for physical assets based on multi-source data. The digital twins are bound to geometric models, business attributes, and real-time data streams. The asset blockchain notarization service is configured to generate a decentralized digital identity for each of the digital twins on the management consortium blockchain and to perform on-chain trusted notarization of key events throughout the asset's lifecycle. The asset intelligence engine layer, connected to the digital twin and data management layer, includes: An asset valuation model is configured to predict asset status based on multi-source data from the digital twin; The asset association engine is configured to automatically associate an asset with a spatial block unit in the digital space base when the asset status is abnormal, and generate an urban governance event work order. The platform service layer is configured to encapsulate query, control, analysis, and on-chain verification services for digital twins of assets, and provides services through APIs; The application interaction layer is configured to provide an application interface for a panoramic asset cockpit and mobile inspection.

[0074] More preferably, the asset association engine includes: Based on the block data model, establish the ownership relationship between the digital twin of the asset and its corresponding second-level dynamic sub-block; When the asset health and value dynamic assessment model outputs an abnormality, or when an asset chain state change event is detected, the block unit to which the asset belongs is automatically located. Based on pre-configured rules, a structured event work order containing asset information, location information, and disposal suggestions is generated and pushed to the responsible department or personnel associated with the block unit; The application interaction layer also provides a panoramic cockpit for assets through full vector multi-threaded mapping. The application interaction layer is used to: respond to user operations, dynamically retrieve, read and render asset digital twins, block unit boundaries and related vector data from the spatial database in real time to realize real-time visualization of asset status, spatial distribution and related information, without the need to pre-generate static tiles; And / or, the underlying data architecture of the digital space base includes: A data lake is configured to store mirror copies of the original data according to the data source directory. It supports the inclusion of relational databases, offline spatial files, and structured files into the lake and records the data lineage. The data warehouse, connected to the data lake, is configured to perform quality checks, cleaning, transformation, and modeling on the data entering the lake according to predefined data standards, in order to form data assets that can be used for analysis. Raw sensor data from the Internet of Things is accessed through the data lake and can be processed in real time through a stream computing pipeline or after being governed by the data warehouse, and used to drive the updating of the digital twin and intelligent asset evaluation.

[0075] The asset management method based on digital twin technology in this embodiment of the invention uses standard blocks to build the basic spatial framework for asset management. At the same time, it dynamically adjusts the boundaries of dynamic sub-blocks based on real-time sensing data. This breaks through the technical defects of static and fixed spatial division in traditional asset management, which cannot adapt to changes in asset status / environment in real time. It enables spatial blocks to accurately map the actual distribution and dynamic changes of assets in the physical world, effectively solving problems such as management blind spots and asset positioning deviations caused by the mismatch between static spatial division and dynamic asset status.

[0076] The asset management method and platform based on digital twin technology disclosed in the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. An asset management platform based on digital twin technology, characterized in that, include: The intelligent sensing and access layer is configured to access the real-time status data of physical assets through various IoT protocols and sensing devices, and to assign an automatically identifiable physical identity code to each physical asset. The digital twin and data management layer, connected to the intelligent sensing and access layer, includes: An asset twin construction engine is configured to create and dynamically update digital twins for physical assets based on multi-source data. The digital twins are bound to geometric models, business attributes, and real-time data streams. The asset blockchain notarization service is configured to generate a decentralized digital identity for each of the digital twins on the management consortium blockchain and to perform on-chain trusted notarization of key events throughout the asset's lifecycle. The asset intelligence engine layer, connected to the digital twin and data management layer, includes: An asset valuation model is configured to predict asset status based on multi-source data from the digital twin; The asset association engine is configured to automatically associate an asset with a spatial block unit in the digital space base when the asset status is abnormal, and generate an urban governance event work order. The platform service layer is configured to encapsulate query, control, analysis, and on-chain verification services for digital twins of assets, and provides services through APIs; The application interaction layer is configured to provide an application interface for a panoramic asset cockpit and mobile inspection.

2. The asset management platform based on digital twin technology as described in claim 1, characterized in that, The asset association engine includes: Based on the block data model, establish the ownership relationship between the digital twin of the asset and its corresponding second-level dynamic sub-block; When the asset health and value dynamic assessment model outputs an abnormality, or when an asset chain state change event is detected, the block unit to which the asset belongs is automatically located. Based on pre-configured rules, a structured event work order containing asset information, location information, and disposal suggestions is generated and pushed to the responsible department or personnel associated with the block unit; The application interaction layer also provides a panoramic cockpit for assets through full vector multi-threaded mapping. The application interaction layer is used to: respond to user operations, dynamically retrieve, read and render asset digital twins, block unit boundaries and related vector data from the spatial database in real time to realize real-time visualization of asset status, spatial distribution and related information, without the need to pre-generate static tiles; And / or, the underlying data architecture of the digital space base includes: A data lake is configured to store mirror copies of the original data according to the data source directory. It supports the inclusion of relational databases, offline spatial files, and structured files into the lake and records the data lineage. The data warehouse, connected to the data lake, is configured to perform quality checks, cleaning, transformation, and modeling on the data entering the lake according to predefined data standards, in order to form data assets that can be used for analysis. Raw sensor data from the Internet of Things is accessed through the data lake and can be processed in real time through a stream computing pipeline or after being governed by the data warehouse, and used to drive the updating of the digital twin and intelligent asset evaluation.

3. An asset management method based on digital twin technology, characterized in that, include: On the digital space base, multiple levels of spatial blocks are automatically generated based on multi-source geospatial data. The spatial blocks include a first-level standard block and a second-level dynamic sub-block. The second-level dynamic sub-block dynamically adjusts the boundary of the first-level standard block based on real-time acquired sensing data. A block relationship graph is constructed for each second-level dynamic sub-block, and the block relationship graph is used to encode the spatial or functional association between the corresponding dynamic sub-block and other dynamic sub-blocks; Based on the reference address library associated with the second-level dynamic sub-block, entity parsing and semantic fusion are performed on the multi-source heterogeneous business data flowing into the dynamic sub-block to form intra-block fused data set with a unified identifier; Based on the preset block heat assessment model, the governance heat value of each second-level dynamic sub-block is dynamically calculated, and the priority of data calculation and updating is assigned to each dynamic sub-block based on the governance heat value; If the output of the block health assessment model for a specific dynamic sub-block is lower than a threshold, root cause analysis is performed based on the block relationship graph, and a collaborative treatment strategy is generated.

4. The asset management method based on digital twin technology as described in claim 3, characterized in that, The automatic generation of multiple-level spatial blocks based on multi-source geospatial data includes: Based on the preset urban governance grid division rules, an initial spatial unit is generated as the first-level standard block; Acquire real-time spatial contour data from drones, high-precision maps, or image recognition devices; Based on the real-time spatial contour data and preset event density or business rules, at least one first-level standard block is divided into multiple second-level dynamic sub-blocks, and their vector boundaries are automatically corrected.

5. The asset management method based on digital twin technology as described in claim 3, characterized in that, The entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: Configure different business concept ontology libraries for different types of dynamic sub-blocks; When multiple business data entries describing the same geographic entity that have semantic conflicts are received, the business concept ontology library corresponding to the dynamic sub-block type and the conflict resolution rules of the corresponding sub-block are invoked to generate the merged data record. Alternatively, the entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: Receive entity information containing non-standard address descriptions; The place name and address matching engine is invoked to match the non-standard address description with a high-precision reference address database and convert it into standard coordinates or standard address codes. Based on the converted standard coordinates or address codes, entities are associated with corresponding second-level dynamic sub-blocks, buildings, or specific spatial units; When address matching cannot uniquely determine the spatial unit to which an entity belongs, or when different data sources conflict in their spatial attribution of the same entity, a preset association rule is used for decision-making; the association rule includes proximity association, association based on business jurisdiction weight, or association based on data source priority.

6. The asset management method based on digital twin technology as described in claim 3, characterized in that, The prioritization of data calculation and update for each dynamic sub-block based on the governance heat value includes: The data processing task covering the entire city will be broken down into subtasks, with each second-level dynamic sub-block as an independent computing unit. The subtasks are placed into a dynamic priority queue, where the subtasks corresponding to dynamic sub-blocks with high governance heat values ​​are allocated higher computing resource priority and shorter execution cycles. The computation results of each dynamic sub-task are cached independently, and when upper-level aggregation data is needed, the cached results are directly aggregated.

7. The asset management method based on digital twin technology as described in claim 3, characterized in that, The root cause analysis based on the block relationship graph includes: The target sub-blocks are identified as those whose output of the block health assessment model is below a threshold. Traverse the block relationship graph to identify one or more associated sub-blocks that are strongly associated with the target sub-block; Analyze the real-time status indicators of the associated sub-blocks to determine whether the deterioration of the health of the target sub-block is due to the influence of the associated sub-blocks; The output of the block health assessment model responding to a specific dynamic sub-block is below a threshold, including: Monitor state change events issued by the smart contract account corresponding to the digital twin of the physical asset; When a state change event indicating a deterioration in health status is captured, the block health of the second-level dynamic sub-block to which it belongs is determined to be below a threshold.

8. The asset management method based on digital twin technology as described in claim 3, characterized in that, After automatically generating multiple levels of spatial blocks based on multi-source geospatial data, the method further includes: Associate one or more physical assets with the second-level dynamic sub-block; Create a corresponding digital twin for each of the physical assets; A decentralized digital identity is generated on the management consortium blockchain for each digital twin as its immutable unique identifier, wherein the initial attribute information of the digital twin is associated with the unique identifier; The generation of decentralized digital identities on the management consortium blockchain includes: On a management consortium blockchain based on a set management mechanism, a corresponding smart contract account is deployed for the digital twin; wherein, the address of the smart contract account serves as the decentralized digital identity, and the smart contract encodes business logic that defines the rules for the transfer of ownership or change of status of the physical assets; The process of creating a corresponding digital twin for each of the physical assets includes: A globally unique block identification code is assigned to the physical asset using a unified coding method. The unified coding method includes generating a composite code containing a grid location code and a multi-level category code as the basis for the unique identifier. The grid location code represents the precise location of the second-level dynamic sub-block or the physical asset in the spatial grid, and the multi-level category code represents its asset type, ownership department, or functional classification. When the boundary of the second-level dynamic sub-block is dynamically refined or the physical asset is split / merged, a new unique identifier is generated by adjusting the precision of the grid location code or the granularity of the multi-level category code, while maintaining the traceability relationship with the original identifier. The initial file information of the physical asset is accurately linked to its second-level dynamic sub-block and its corresponding three-dimensional spatial location by address matching and spatial association using the entity fusion method. The binding relationship between the physical asset, digital twin, its corresponding second-level dynamic sub-block, and its unique identifier on the management consortium blockchain is established based on the block identification code.

9. The asset management method based on digital twin technology as described in claim 3, characterized in that, The blockchain network is a managed consortium blockchain, and its management nodes include: Multiple council nodes operated by a predetermined governing body, the council nodes being configured to manage the initial parameters of the blockchain network; Multiple witness nodes are elected by multiple asset stakeholders through voting, and these witness nodes are configured to take turns generating blocks; Multiple vote-holding nodes, which obtain voting rights for the witness nodes through data rights; wherein, the data consensus mechanism requires that a block is confirmed as the final block after it is generated and verified and signed by more than a set proportion of witness nodes.

10. The asset management method based on digital twin technology as described in claim 3, characterized in that, The entity parsing and semantic fusion of the multi-source heterogeneous business data flowing into the dynamic sub-block includes: A semantic conflict resolution engine is used to process heterogeneous data from different business systems that involve the same physical asset. The engine performs the following operations: Based on pre-built cross-departmental business ontology mapping relationships, business concepts describing the same real-world entity in different data sources are identified; wherein, the cross-departmental business ontology mapping relationships include automatically extracting departmental-level business concepts and their attributes from business specifications and data dictionaries of different departments; based on the semantic similarity of concept names, shared instance data or association relationships, equivalence, parent-child or related relationships between business concepts of different departments are automatically or semi-automatically discovered, and mapping relationships are established. The application configures a conflict resolution rule chain for specific business scenarios to automatically process identified semantic conflict data. The rule chain includes deduplication rules, merging rules, or data priority rules. Based on the evaluation feedback of the conflict resolution results, the parameters of the conflict resolution rule chain are automatically adjusted; The method further includes: Identify the source data regions and computational paths associated with core metrics that are frequently accessed or of high interest in the cockpit, and mark them as hot data paths; Monitor the update status of the source data on which the hot data path depends; In response to the detection of an update to the source data, only the partial computational subgraph related to the hot data path is triggered and executed to update the corresponding cockpit visualization data incrementally, rather than recalculating the entire data base.

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