Block chain finance RWA asset intelligent scheduling platform combined with trusted data

Abstract

The invention relates to the technical field of asset management, in particular to a block chain finance RWA asset intelligent scheduling platform combined with credible data, and the platform comprises a right confirmation time comparison module, a credit rating judgment module, a scheduling path compression module, a node time locking module and a link synchronization checking module. According to the method, the states of the assets in different life cycle stages can be accurately determined by comparing the original right confirmation time of the assets with the remaining timestamps, the real consistency of the asset information is ensured, the circulation risk caused by inconsistent time records is avoided, and by identifying the active states of the assets in the ownership period and the transfer period, the loss of the assets is reduced. According to the method, assets with stable credit states can be efficiently screened out, potential credit risks are avoided, the efficiency and safety of asset scheduling are improved, synchronous check of node time ensures that operation time of each path node is consistent with account book transaction time, potential path sequence errors are reduced, and the efficiency of asset scheduling is improved. And the verifiability, the safety and the efficiency in the asset management process are effectively improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of asset management, in particular to a blockchain financial RWA asset intelligent scheduling platform combined with trusted data. BACKGROUND

[0002] The technical field of asset management involves the whole process management of digital modeling, registration, right confirmation, circulation, valuation, scheduling and risk control of various financial assets. The core matters include real and reliable acquisition of asset data, compliant and controllable execution of asset transactions, whole-process tracking and auditing of asset status, and safety mechanism design of asset circulation. The technical field mainly covers asset digitization, distributed ledger management, financial contract rule modeling, trusted data interaction, identity authentication and authorization management, and cross-institutional collaboration mechanism. With the development of blockchain and data trusted computing, the asset management technology presents the technical trends of decentralization, verifiability and auditability. Among them, traditional RWA asset management refers to the process of right confirmation, registration, circulation, scheduling and valuation of real-world assets such as real estate debt instruments in the financial market. It mainly relies on centralized financial institutions to complete the authenticity judgment and circulation control of assets through internal asset evaluation systems, asset liability management systems and regular auditing mechanisms. The process uses asset information registration based on self-built databases of institutions, relies on bank-embedded risk control models for credit judgment, and uses offline credit audit material cross-validation for asset evaluation to complete the risk screening and value assessment before asset scheduling. The scheduling operation relies on static asset pool structure and fixed periodic scheduling strategy for asset matching and distribution.

[0003] The existing technology relies on centralized financial institutions for asset evaluation and risk control in the asset management process, which ensures the authenticity and circulation control of some assets, but the effectiveness of asset right confirmation and status tracking is weak, especially when facing multiple time sources or heterogeneous information, which easily causes time sequence confusion, leading to increased asset circulation risk. The traditional method relies on static asset pool and periodic scheduling strategy, which cannot flexibly respond to dynamic changes in actual operation, easily causing untimely or mismatched asset scheduling, affecting circulation efficiency. The credit judgment in the existing technology mostly relies on offline credit audit material cross-validation, which has poor real-time and accuracy of information, leading to lag in credit risk assessment of some assets, affecting asset scheduling and circulation efficiency. SUMMARY

[0004] To address the weaknesses in the effectiveness of existing technologies for asset ownership confirmation and status tracking, particularly when faced with multiple time sources or heterogeneous information, which can easily lead to temporal sequence disorder and increased asset circulation risk, traditional methods rely on static asset pools and periodic scheduling strategies. These methods cannot flexibly respond to dynamic changes in actual operations, easily resulting in untimely or mismatched asset scheduling, impacting circulation efficiency. Existing technologies largely rely on cross-verification of offline credit review materials, resulting in poor real-time information accuracy and delayed credit risk assessment of certain assets, further affecting asset scheduling and circulation efficiency. This invention provides a blockchain-based financial RWA asset intelligent scheduling platform that integrates trusted data. The technical solution is as follows: On the one hand, it provides a blockchain-based financial RWA asset intelligent scheduling platform that combines trusted data. This platform includes: The confirmation time comparison module obtains the original confirmation timestamp of RWA assets and the timestamp in the real estate registration platform, calls the asset change time marker in the financial credit review record, divides the asset status into three stages: confirmation period, holding period and transfer period, and generates a list of schedulable asset ownership consistency identifiers. The credit rating determination module uses the list of consistent ownership identifiers of the schedulable assets to extract the holding record time period and circulation record, identify the time coverage of static and transfer states, construct a state active classification structure, and generate a set of assets with stable credit status during the scheduling period. The scheduling path compression module uses the set of assets with stable credit status during the scheduling period to extract the total number of jump nodes of the scheduling path, identify path items whose total number of jump nodes is greater than a limited threshold, perform scheduling priority sorting processing, and generate a priority scheduling list of jump paths. The node time locking module extracts the local time value of the path node and the task plan time value based on the jump path priority scheduling list, determines the degree of overlap of time intervals, performs node locking action and records the path position, and generates a scheduling time synchronization node cluster list.

[0005] As a further embodiment of the present invention, the list of schedulable assets with consistent ownership includes asset identifier, asset status, timestamp sequence, time pairing group, and heterogeneous group; the set of assets with stable credit status includes asset identifier, holding record time period, circulation record, static state time, transfer state time, active percentage, and credit status; the priority scheduling list of jump paths includes scheduling path, total number of jump nodes, path item, path set, and priority ranking path; and the scheduling time synchronization node cluster list includes path node, task plan time value, time interval overlap, path node group, node locking action, and path location.

[0006] As a further aspect of the present invention, the rights confirmation time comparison module includes: The timestamp acquisition submodule obtains the original confirmation timestamp of RWA assets and the timestamp in the real estate registration platform. For asset change records, it obtains asset status information by calling the asset change time mark in the financial credit review record. The asset status is divided into three stages: confirmation period, holding period and transfer period. The timestamp sequence of each stage is extracted to obtain the stage timestamp sequence. The time sequence determination submodule determines whether the order of each set of timestamps is consistent based on the time pairing set, analyzes whether there are multiple time source pairing groups in the same stage, compares the order of timestamps, determines whether there is a contradiction, and obtains the time sequence consistency determination result. The heterogeneous group marking submodule uses the time sequence consistency determination result to mark the paired groups with time sequence deviations, forming heterogeneous groups, and summarizes the corresponding asset identifiers to generate a list of schedulable asset ownership consistency identifiers.

[0007] As a further aspect of the present invention, the credit rating determination module includes: The asset record extraction submodule extracts the holding period and circulation record of the assets based on the list of consistent ownership identifiers of the schedulable assets, extracts the required time period information, identifies the time coverage of the static and transferred states of the assets, performs state activity classification, and obtains asset activity distribution data. The credit status judgment submodule filters asset identifiers with an active percentage greater than a preset condition based on the asset activity distribution data, performs credit status judgment operations, and generates a set of assets with stable credit status during the scheduling period.

[0008] As a further aspect of the present invention, the scheduling path compression module includes: The jump node identification submodule extracts the total number of jump nodes in the scheduling path based on the set of assets with stable credit status during the scheduling period, counts the jump nodes for the scheduling path, and filters the number of jump nodes according to a set limit threshold to identify path items with a total number of jump nodes greater than the threshold, thus obtaining a set of jump node paths. The path filtering and reconstruction submodule, based on the set of jump node paths, deletes path items that do not meet the conditions, and obtains the reconstructed set of scheduling paths by analyzing and reorganizing the jump nodes of the remaining paths. The path priority sorting submodule uses the reconstructed set of scheduling paths to sort the paths by scheduling priority, and sorts the path items according to the scheduling priority to generate a priority scheduling list of jump paths.

[0009] As a further aspect of the present invention, the process of extracting the total number of jump nodes is as follows: based on the set of assets with stable credit status during the scheduling period, the number of jump nodes in each scheduling path is calculated according to a predetermined rule; when the number of jump nodes exceeds a set threshold, path filtering and analysis are performed to identify path items with a total number of jump nodes greater than the threshold, thereby obtaining a set of jump node paths. The defined threshold is a dynamic threshold set based on the statistical analysis results of scheduling data. By performing frequency analysis and deviation calculation on path data, the range of reliable jump nodes is determined.

[0010] As a further aspect of the present invention, the node time locking module includes: The time value extraction submodule extracts the local time value of the path node and the corresponding task plan time value based on the jump path priority scheduling list to obtain the path node time value pair; The time overlap determination submodule compares the degree of overlap between the local time and the task plan time based on the time value pairs of the path nodes, and filters the path node groups with an overlap time ratio exceeding a set threshold to obtain the path node groups with an overlap time ratio. The node locking and recording submodule performs node locking operations based on the path node group with overlapping time proportions, records the path position of the locked node, and generates a list of scheduling time synchronization node clusters.

[0011] As a further aspect of the present invention, the comparison of the overlap between local time and task schedule time intervals is as follows: based on the path node time value pairs, the overlap ratio between the local time and task schedule time intervals of the path nodes is calculated; when the overlap ratio exceeds a predetermined threshold, path node groups that meet the conditions are selected to obtain path node groups with overlapping time ratios. The set threshold is a dynamic threshold set based on the statistical analysis results of the scheduling situation. The range of overlapping time proportions is determined by analyzing the time overlap in the data.

[0012] As a further aspect of the present invention, the platform also includes a link synchronization verification module: The link synchronization verification module obtains the node ledger operation time and path ledger transaction time through the scheduling time synchronization node cluster list, constructs a path node time comparison structure, checks whether the time record is consistent with the path order, screens reverse-order path nodes, performs path availability screening operation, and generates an executable scheduling path list. The list of executable scheduling paths includes path nodes, ledger operation time, path ledger transaction time, time comparison structure, reverse path nodes, and path availability screening.

[0013] As a further aspect of the present invention, the link synchronization verification module includes: The time comparison structure construction submodule extracts the node ledger operation time and path ledger transaction time of the path nodes based on the scheduling time synchronization node cluster list, constructs the path node time comparison structure, and obtains the path node time comparison table. The time consistency detection submodule, based on the path node time lookup table, detects whether the time records of the path nodes are consistent with the order of the path, screens out the reverse-order path nodes, and obtains a set of reverse-order path nodes. The path availability screening submodule performs a path availability screening operation based on the set of reverse path nodes, removes unavailable path nodes, and generates a list of executable scheduling paths.

[0014] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By comparing the original asset ownership confirmation time with the remaining timestamp, the status of assets at different stages of their lifecycle can be accurately determined, ensuring the authenticity and consistency of asset information and avoiding circulation risks caused by inconsistent time records. By identifying the active status of assets during the holding and transfer periods, assets with stable credit status can be efficiently screened, providing a safer basis for subsequent asset scheduling, avoiding potential credit risks, optimizing scheduling paths, reducing unnecessary path jumps by prioritizing jump paths, improving the efficiency and security of asset scheduling, and ensuring that the operation time of each path node is consistent with the ledger transaction time by synchronizing node time, reducing potential path sequence errors and improving the feasibility of scheduling path execution. Through accurate asset status determination and path scheduling optimization, the verifiability, security, and efficiency of asset management are effectively improved. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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 schematic diagram of the blockchain-based financial RWA asset intelligent scheduling platform that combines trusted data, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the platform framework of the present invention; Figure 3 This is a flowchart of the rights confirmation time comparison module in this invention; Figure 4 This is a flowchart of the credit rating determination module in this invention; Figure 5 This is a flowchart of the scheduling path compression module in this invention; Figure 6 This is a flowchart of the node time locking module in this invention; Figure 7 This is a flowchart of the link synchronization verification module in this invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a blockchain-based financial RWA asset intelligent scheduling platform that combines trusted data, such as... Figures 1-2 The diagram shown illustrates a blockchain-based financial RWA asset intelligent scheduling platform that integrates trusted data. This platform includes: The confirmation time comparison module obtains the original confirmation timestamp of RWA assets and the timestamp in the real estate registration platform, calls the asset change time mark in the financial credit review record, divides the asset status into three stages: confirmation period, holding period and transfer period, extracts the timestamp sequence by stage, establishes a time pairing set, identifies pairing groups with multiple time sources in the same stage, judges whether the time order in the pairing group is consistent, marks the pairing group with contradictory time order as heterogeneous group, summarizes the asset identifiers corresponding to the heterogeneous group, and generates a list of schedulable asset ownership consistency identifiers; The credit rating determination module uses a list of schedulable assets with consistent ownership, extracts holding record time periods and circulation records, identifies the time coverage of static and transferred states, constructs a status activity classification structure, extracts asset identifiers with an active ratio greater than preset conditions, performs credit status determination operations, and generates a set of assets with stable credit status during the scheduling period. The scheduling path compression module utilizes the set of assets with stable credit status during the scheduling period to extract the total number of jump nodes in the scheduling path, identifies path items whose total number of jump nodes exceeds a certain threshold, deletes path items that do not meet the conditions, reconstructs the path set, performs scheduling priority sorting, and generates a priority scheduling list for jump paths. The node time locking module extracts the local time value of the path node and the task plan time value based on the low jump path priority scheduling list, determines the degree of overlap of time intervals, filters the path node group with the proportion of overlapping time, performs node locking action and records the path position, and generates a scheduling time synchronization node cluster list. The link synchronization verification module obtains the node ledger operation time and path ledger transaction time by scheduling the time synchronization node cluster list, constructs a path node time comparison structure, checks whether the time record is consistent with the path order, screens reverse-order path nodes, performs path availability screening operation, and generates an executable scheduling path list. The list of schedulable assets with consistent ownership includes asset identifier, asset status, timestamp sequence, time pairing group, and heterogeneous group. The set of assets with stable credit status includes asset identifier, holding record period, circulation record, static state time, transfer state time, active percentage, and credit status. The priority scheduling list for jump paths includes scheduling paths, total number of jump nodes, path items, path sets, and priority ranking paths. The list of scheduling time synchronization node clusters includes path nodes, task plan time values, time interval overlap, path node groups, node locking actions, and path locations. The list of executable scheduling paths includes path nodes, ledger operation time, path ledger transaction time, time comparison structure, reverse-order path nodes, and path availability screening.

[0023] Specifically, such as Figure 2 , 3 As shown, the time comparison module for confirming property rights includes: The timestamp acquisition submodule obtains the original confirmation timestamp of RWA assets and the timestamp in the real estate registration platform. For asset change records, it obtains asset status information by calling the asset change time mark in the financial credit review record. The asset status is divided into three stages: confirmation period, holding period and transfer period. The timestamp sequence of each stage is extracted to obtain the stage timestamp sequence. To accurately classify the different stages of an asset, the original asset registration time stamp and the timestamp in the real estate registration platform are checked for consistency or discrepancies. The timestamps are obtained by accessing asset change time stamps in financial credit review records. These records contain asset change records, including initial registration, holding period, and subsequent transfers. Each change record corresponds to a timestamp. By comparing the original registration time stamp with the timestamp in the real estate registration platform, three distinct time periods can be identified for each asset: the registration period, the holding period, and the transfer period. The registration period refers to the time when the asset is first registered; the holding period refers to the time during which the asset is held; and the transfer period refers to the time during which the asset is transferred from one holder to another. The timestamps of each stage are extracted and arranged in chronological order to form a stage timestamp sequence. Taking a real estate transaction as an example, the original confirmation timestamp is set as "2022-01-01", the timestamp in the real estate registration platform is "2022-02-01", and the asset change timestamp in the financial credit review record is "2023-06-01". Then the real estate asset goes through the confirmation period (January 1, 2022 to February 1, 2022), the holding period (February 1, 2022 to June 1, 2023), and the transfer period (after June 1, 2023). The system can automatically identify the order of each timestamp, accurately distinguish the duration of each stage, construct the time dimension framework of the asset, and obtain the stage timestamp sequence.

[0024] The time sequence determination submodule determines whether the order of each set of timestamps is consistent based on the time pairing set, analyzes whether there are multiple time source pairings in the same stage, compares the order of timestamps, determines whether there are any contradictions, and obtains the time sequence consistency determination result. The system assesses time-source pairings within the same timeframe, verifying the consistency of timestamp sequences. By comparing timestamp order, the system detects anomalies from different time sources and performs in-depth analysis to determine the relative order of timestamps. If multiple timestamp pairings occur within the same timeframe, the system compares the order of each timestamp to check for conflicts. During the holding period, two timestamps from different financial credit records, representing "2022-04-01" and "2022-06-01" respectively, are used. The system compares the relative order of these two timestamps to determine if they conform to the logical order of the holding period. Each time the time-series analysis is performed, the system makes a judgment based on the timestamp order. If a deviation or conflict in timestamp order is found, the system marks the pairing as inconsistent, resulting in a time-series consistency determination.

[0025] The heterogeneous group marking submodule uses the time sequence consistency determination result to mark the paired groups with time sequence deviations, forming heterogeneous groups, and summarizes the corresponding asset identifiers to generate a list of schedulable asset ownership consistency identifiers. The system automatically marks timestamp pairs with time discrepancies into heterogeneous groups. The purpose of marking heterogeneous groups is to focus on pairs with time discrepancies to aid subsequent analysis. Each heterogeneous group corresponds to an asset identifier. The system will aggregate asset identifiers with time discrepancies and set them in the asset identifiers of a real estate transaction. When the system detects inconsistent timestamp sequences, it marks them as heterogeneous groups and generates an identifier list for the real estate transaction. This allows for the verification and confirmation of property ownership, ensuring that the asset's time sequence is consistent with the relevant records. When generating the asset ownership consistency identifier list, the system will automatically filter out records that match the time sequence based on the marked heterogeneous groups, generating a schedulable asset ownership consistency identifier list.

[0026] Specifically, such as Figure 2 , 4 As shown, the credit rating determination module includes: The asset record extraction submodule extracts the holding period and circulation records of assets based on the list of consistent ownership identifiers of schedulable assets, extracts the required time period information, identifies the time coverage of the static and transferred states of assets, performs state activity classification, and obtains asset activity distribution data. The system extracts the holding and circulation records for each asset. Combining this time period information, the system analyzes time coverage based on the asset's static and transfer status. A static state refers to a period during which the asset remains untouched or untransferred, indicating full ownership. A transfer state refers to the period during which ownership has been transferred or traded. The system identifies the asset's status within each time period, calculates the ratio of static to transfer time, and generates an active category for the asset status. In practice, the system obtains the holding period (set from January 1, 2022 to June 1, 2022) and circulation records (set from June 2, 2022 to September 1, 2022) for each asset. The system extracts time period information from the records and analyzes whether the asset was in a transfer state during this period. If an asset underwent multiple transfers between June 1, 2022 and September 1, 2022, its activity level is high. If the asset remained stationary during this period, its activity level is low. The system will classify each asset as "active" or "inactive" based on the time coverage of stationary and transfer states within the time period. For example, if an asset remained stationary between January 1, 2022 and June 1, 2022, and then underwent two transfers between June 2, 2022 and September 1, 2022, then the asset's stationary coverage during the holding period is 50%, and the active time of the transfer state is 50%, thus obtaining asset activity distribution data.

[0027] The credit status assessment submodule filters asset identifiers with an active percentage greater than a preset condition based on asset activity distribution data, performs credit status assessment operations, and generates a set of assets with stable credit status during the scheduling period. Assets with an activity percentage exceeding a preset threshold are selected for credit status assessment. This threshold can be set at 80%, meaning an asset is considered "highly active" when its activity time exceeds 80%. Assets meeting this condition enter the credit status assessment process. The system analyzes the asset's behavior during the scheduling period and assesses credit stability based on activity distribution data. This assessment considers asset holdings, circulation frequency, and the presence of frequent ownership transfers to evaluate the credit risk of active assets. For example, if a property has been transferred four times between different owners within the past six months with a relatively even distribution of transfer times, this asset has high credit risk. The system will then generate a set of stable assets within the scheduling period based on this information and by screening and assessing active assets.

[0028] Specifically, such as Figure 2 , 5 As shown, the scheduling path compression module includes: The jump node identification submodule extracts the total number of jump nodes in the scheduling path based on the set of assets with stable credit status during the scheduling period, counts the jump nodes for the scheduling path, and filters the number of jump nodes according to the set limit threshold. It identifies path items with a total number of jump nodes greater than the threshold and obtains the jump node path set. The scheduling path represents the transfer process of assets in different states. Jump nodes refer to the key nodes in the path where the asset's state changes. During the extraction process, the system identifies the jump nodes of each asset during the scheduling period by tracking records such as asset ownership changes and transaction history. For a real estate transaction, jump nodes include ownership transfer records from a buyer to a seller. The system will count the total number of ownership change nodes in each transaction. The system will count the number of jump nodes in the scheduling path. By setting a limited threshold, the system will filter out path items with more than the threshold. If the threshold is set to 3 and the number of jump nodes in a certain path is 4, the path will be retained in the jump node path set. The system will filter out path items with a total number of jump nodes greater than the threshold, indicating that the path is a relatively complex path involving multiple asset ownership transfers, reflecting high transaction activity. The system can identify paths with many jump nodes and frequent state changes during the scheduling period, providing data support for subsequent path filtering and reconstruction, and obtaining the jump node path set.

[0029] The path filtering and reconstruction submodule is based on the set of jump node paths. It deletes path items that do not meet the conditions, and obtains the reconstructed set of scheduling paths by analyzing and reorganizing the jump nodes of the remaining paths. Paths that do not meet the criteria are deleted. The filtering criteria can include path stability, the complexity of jump nodes, and the presence of too many unnecessary duplicate jumps. If jump nodes in a path are repeated or their states change too frequently, the system will consider the path unsuitable as a scheduling path and delete it. The system will then analyze the jump nodes of the remaining paths, checking the role of each node in each path and its impact on asset scheduling, and optimizing the path structure. During the analysis, the system will find unnecessary jump nodes in the path. If a path has multiple irrelevant intermediate nodes, the system will reorganize the nodes, remove redundant jumps, and reconstruct the path into a simpler and more efficient form. If nodes A, B, and C in a path have a high degree of temporal overlap, the system will merge the nodes into a new node to reduce the path complexity. Based on the analysis and reorganization, the system obtains the reconstructed set of scheduling paths.

[0030] The path priority sorting submodule uses the reconstructed set of scheduling paths to sort the paths by scheduling priority, and sorts the path items according to the scheduling priority to generate a priority scheduling list of jump paths. Each path is prioritized for scheduling. The prioritization can be based on factors such as the number of jump nodes, path stability, and historical performance. If a path involves multiple jump nodes and the nodes change frequently, it will be rated as a high-risk path and therefore have a low priority. Conversely, if a path has fewer jump nodes and has performed stably in past scheduling processes, it will be rated as a high-priority path. The system assigns a priority to each path based on these factors and sorts the path items according to priority to ensure that low-risk and stable paths are processed first. In a scheduling process, if two paths have 4 and 2 jump nodes respectively, and the nodes of the first path are more complex, the first path will be scheduled later, while the second path will be scheduled first due to its simplicity and low risk. A priority scheduling list of jump paths is generated.

[0031] Specifically, such as Figure 2 , 6 As shown, the node time locking module includes: The time value extraction submodule extracts the local time value of the path node and the corresponding task plan time value based on the jump path priority scheduling list, and obtains the path node time value pair; The system extracts the local time value and the corresponding task plan time value of each path node. Path nodes represent the specific time of each key event or asset transfer during asset scheduling. The local time value of each node refers to the actual time when the event occurred, such as the time of ownership transfer for a transaction. The task plan time value refers to the preset execution time in the plan, which is set in advance by the scheduling system or specified by the user. For example, if a path node represents the transfer of an asset from one owner to another, and the local time of the node is "2022-08-15-10:00:00", the task plan time is "2022-08-15-09:00:00". The system extracts the time value of each node for each path. By extracting the local time value and the task plan time value pair, the system can compare the actual execution status of each path node with the planned time, providing a data basis for subsequent time overlap judgment and obtaining the path node time value pair.

[0032] The time overlap judgment submodule compares the degree of overlap between local time and task schedule time based on the time value pairs of path nodes, and filters the path node groups with the overlap time ratio exceeding a set threshold to obtain the path node groups with the overlap time ratio. The system compares the overlap between local time and scheduled task time. The criterion for time overlap is whether there is an overlapping interval between the local time and the scheduled task time; that is, whether these two time periods intersect within a certain period. The system calculates the overlap interval between local time and scheduled task time and filters path node groups whose overlap exceeds a set threshold. For example, if the local time of a path node is "2022-08-15-10:00:00" and the scheduled task time is "2022-08-15-09:00:00 to 2022-08-15-1", then the system will determine if the overlap exceeds the threshold. If the time is 1:00:00, then the overlap between the node and the task's planned time is "2022-08-15-10:00:00 to 2022-08-15-11:00:00", with an overlap rate of 100%. The system compares the time overlap rate of the path nodes with a preset threshold. If the set overlap rate threshold is 70%, then the path node group with an overlap rate greater than 70% will be filtered out. The node group represents the path nodes with a high degree of overlap between the actual time and the planned time, which need to be paid special attention to to ensure the smooth execution of the scheduling plan, thus obtaining the path node group with the overlap time rate.

[0033] The node locking and recording submodule performs node locking operations based on the path node group with overlapping time ratio, records the path position of the locked node, and generates a list of scheduling time synchronization node clusters. The node locking operation is performed to ensure that nodes with a high overlap in time during scheduling will not conflict or cause errors. If the local time of a path node overlaps significantly with the scheduled time of the task, the system will lock the node to ensure that the scheduling is carried out according to the plan and to avoid time conflicts or scheduling errors. The location of the locked node refers to the specific location of the path node in the scheduling list or its sequence number in the path. The system will record the path location of the node so that it can be specially scheduled in subsequent operations and generate a list of time synchronization node clusters.

[0034] Specifically, such as Figure 2 , 7 As shown, the link synchronization verification module includes: The time mapping structure construction submodule extracts the node ledger operation time and path ledger transaction time of path nodes based on the scheduling time synchronization node cluster list, constructs the path node time mapping structure, and obtains the path node time mapping table. The system extracts the node ledger operation time and path ledger transaction time of path nodes. The node ledger operation time refers to the actual time record when the asset status changes or ownership is transferred during asset scheduling, while the path ledger transaction time refers to the planned transaction time related to the node. Through time information, the system constructs a path node time comparison structure, that is, matching and comparing the ledger operation time of each path node with the corresponding transaction time. By constructing the comparison structure, the system can intuitively show how the time record of the path node is related to the time nodes in the scheduling path, helping to determine whether there are time deviations or sequence problems. For example, if the ledger operation time of a certain path node is set to "2022-08-10-14:00:00" and the path ledger transaction time is set to "2022-08-10-13:00:00", these two time values ​​are mapped together and compared with the time values ​​of the path nodes to obtain a path node time comparison table.

[0035] The time consistency detection submodule uses a path node time lookup table to check whether the time records of path nodes are consistent with the order of the path, screens out reverse-order path nodes, and obtains a set of reverse-order path nodes. The system checks whether the time records of path nodes are consistent with the path order. The path order refers to the sequence in which path nodes should occur during asset scheduling to ensure a smooth transition of assets from one node to the next. The system checks the time records of path nodes to determine whether the time conforms to the preset order rules. If the time of a node is later than the time of the node that should precede it, the system will determine that it is in reverse order, meaning there is a problem with the node's time record. By analyzing the path node time comparison table, the system will screen out path nodes with inconsistent times and reverse order. For example, in a certain path, if the time of node A is "2022-08-10-14:00:00" and the time of node B is "2022-08-10-13:00:00", the system will detect that node B is in reverse order and mark the path node as a reverse order node, thus obtaining a set of reverse order path nodes.

[0036] The path availability screening submodule performs path availability screening operations based on the reverse path node set, removes unavailable path nodes, and generates a list of executable scheduling paths. Perform a path availability screening operation. The purpose of path availability screening is to determine which paths are still valid and which paths need to be removed when reverse path nodes exist. For paths with a significant impact from reverse path nodes, the system will mark them as unavailable paths and remove the path nodes to ensure the smooth progress of the scheduling process. Suppose there is a reverse path node in a path with a time of "2022-08-10-13:00:00". If the time of the path node that should have occurred after "2022-08-10-14:00:00" is earlier, the system will consider the path to be unavailable due to the reverse path node. By performing the screening operation, the system will remove the unavailable path nodes and generate a list of executable scheduling paths.

[0037] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A blockchain-based financial RWA asset intelligent scheduling platform that integrates trusted data, characterized in that: The platform includes: The confirmation time comparison module obtains the original confirmation timestamp of RWA assets and the timestamp in the real estate registration platform, calls the asset change time marker in the financial credit review record, divides the asset status into three stages: confirmation period, holding period and transfer period, and generates a list of schedulable asset ownership consistency identifiers. The credit rating determination module uses the list of consistent ownership identifiers of the schedulable assets to extract the holding record time period and circulation record, identify the time coverage of static and transfer states, construct a state active classification structure, and generate a set of assets with stable credit status during the scheduling period. The scheduling path compression module uses the set of assets with stable credit status during the scheduling period to extract the total number of jump nodes of the scheduling path, identify path items whose total number of jump nodes is greater than a limited threshold, perform scheduling priority sorting processing, and generate a priority scheduling list of jump paths. The node time locking module extracts the local time value of the path node and the task plan time value based on the jump path priority scheduling list, determines the degree of overlap of time intervals, performs node locking action and records the path position, and generates a scheduling time synchronization node cluster list.

2. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data as described in claim 1, characterized in that: The list of schedulable assets with consistent ownership includes asset identifier, asset status, timestamp sequence, time pairing group, and heterogeneous group. The set of assets with stable credit status includes asset identifier, holding record time period, circulation record, static state time, transfer state time, active percentage, and credit status. The priority scheduling list of jump paths includes scheduling path, total number of jump nodes, path item, path set, and priority ranking path. The scheduling time synchronization node cluster list includes path node, task plan time value, time interval overlap, path node group, node locking action, and path location.

3. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data as described in claim 1, characterized in that: The time comparison module for confirming ownership includes: The timestamp acquisition submodule obtains the original confirmation timestamp of RWA assets and the timestamp in the real estate registration platform. For asset change records, it obtains asset status information by calling the asset change time mark in the financial credit review record. The asset status is divided into three stages: confirmation period, holding period and transfer period. The timestamp sequence of each stage is extracted to obtain the stage timestamp sequence. The time sequence determination submodule determines whether the order of each set of timestamps is consistent based on the time pairing set, analyzes whether there are multiple time source pairing groups in the same stage, compares the order of timestamps, determines whether there is a contradiction, and obtains the time sequence consistency determination result. The heterogeneous group marking submodule uses the time sequence consistency determination result to mark the paired groups with time sequence deviations, forming heterogeneous groups, and summarizes the corresponding asset identifiers to generate a list of schedulable asset ownership consistency identifiers.

4. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data as described in claim 3, characterized in that: The credit rating determination module includes: The asset record extraction submodule extracts the holding period and circulation record of the assets based on the list of consistent ownership identifiers of the schedulable assets, extracts the required time period information, identifies the time coverage of the static and transferred states of the assets, performs state activity classification, and obtains asset activity distribution data. The credit status judgment submodule filters asset identifiers with an active percentage greater than a preset condition based on the asset activity distribution data, performs credit status judgment operations, and generates a set of assets with stable credit status during the scheduling period.

5. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data according to claim 4, characterized in that: The scheduling path compression module includes: The jump node identification submodule extracts the total number of jump nodes in the scheduling path based on the set of assets with stable credit status during the scheduling period, counts the jump nodes for the scheduling path, and filters the number of jump nodes according to a set limit threshold to identify path items with a total number of jump nodes greater than the threshold, thus obtaining a set of jump node paths. The path filtering and reconstruction submodule, based on the set of jump node paths, deletes path items that do not meet the conditions, and obtains the reconstructed set of scheduling paths by analyzing and reorganizing the jump nodes of the remaining paths. The path priority sorting submodule uses the reconstructed set of scheduling paths to sort the paths by scheduling priority, and sorts the path items according to the scheduling priority to generate a priority scheduling list of jump paths.

6. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data according to claim 5, characterized in that: The process of extracting the total number of jump nodes is as follows: based on the set of assets with stable credit status during the scheduling period, the number of jump nodes in each scheduling path is calculated according to a predetermined rule; when the number of jump nodes exceeds a set threshold, path filtering and analysis are performed to identify path items with a total number of jump nodes greater than the threshold, thereby obtaining a set of jump node paths. The defined threshold is a dynamic threshold set based on the statistical analysis results of scheduling data. By performing frequency analysis and deviation calculation on path data, the range of reliable jump nodes is determined.

7. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data as described in claim 5, characterized in that: The node time locking module includes: The time value extraction submodule extracts the local time value of the path node and the corresponding task plan time value based on the jump path priority scheduling list to obtain the path node time value pair; The time overlap determination submodule compares the degree of overlap between the local time and the task plan time based on the time value pairs of the path nodes, and filters the path node groups with an overlap time ratio exceeding a set threshold to obtain the path node groups with an overlap time ratio. The node locking and recording submodule performs node locking operations based on the path node group with overlapping time proportions, records the path position of the locked node, and generates a list of scheduling time synchronization node clusters.

8. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data according to claim 7, characterized in that: The degree of overlap between the local time and the task plan time is calculated by: based on the path node time value pairs, calculating the time interval overlap ratio between the local time and the task plan time of the path node. When the overlap ratio exceeds a predetermined threshold, the path node groups that meet the conditions are filtered to obtain the path node groups with overlapping time ratios. The set threshold is a dynamic threshold set based on the statistical analysis results of the scheduling situation. The range of overlapping time proportions is determined by analyzing the time overlap in the data.

9. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data as described in claim 1, characterized in that: The platform also includes a link synchronization verification module: The link synchronization verification module obtains the node ledger operation time and path ledger transaction time through the scheduling time synchronization node cluster list, constructs a path node time comparison structure, checks whether the time record is consistent with the path order, screens reverse-order path nodes, performs path availability screening operation, and generates an executable scheduling path list. The list of executable scheduling paths includes path nodes, ledger operation time, path ledger transaction time, time comparison structure, reverse path nodes, and path availability screening.

10. The blockchain-based financial RWA asset intelligent scheduling platform combining trusted data according to claim 9, characterized in that: The link synchronization verification module includes: The time comparison structure construction submodule extracts the node ledger operation time and path ledger transaction time of the path nodes based on the scheduling time synchronization node cluster list, constructs the path node time comparison structure, and obtains the path node time comparison table. The time consistency detection submodule, based on the path node time lookup table, detects whether the time records of the path nodes are consistent with the order of the path, screens out the reverse-order path nodes, and obtains a set of reverse-order path nodes. The path availability screening submodule performs a path availability screening operation based on the set of reverse path nodes, removes unavailable path nodes, and generates a list of executable scheduling paths.

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