A digital asset multi-service collaborative processing system and method

Abstract

The application relates to the field of digital assets, and discloses a digital asset multi-service cooperative processing system and method, which comprises the following steps: uniformly analyzing a service request message, field associating subject identification, asset occupation identification and transaction confirmation identification obtained through analysis, calling data associated with a digital asset identification after a digital asset event unit set is written into a platform log record structure, performing consistency comparison on state nodes related to the same subject identification, generating corresponding cooperative abnormal nodes through preset state conflict identification rules when it is detected that a transaction confirmation state has been established but a resource occupation state is not synchronously updated, calling user interaction records and operation track data after the cooperative abnormal nodes are identified, performing semantic matching on interaction text records, and rearranging a processing trigger sequence of a transaction processing unit, a resource scheduling unit and a benefit processing unit. The application has the advantage of improving service cooperative efficiency.

Description

Technical Field

[0001] This invention relates to the field of digital assets, specifically to a multi-business collaborative processing system and method for digital assets. Background Technology

[0002] With the development of digital business models, more and more enterprises are using platform-based approaches to manage and operate various digital assets in a unified manner. In existing technologies, to meet diverse business needs, platforms typically deploy transaction systems, resource sharing modules, agency sales modules, procurement management modules, and financial and legal service interfaces independently. These modules interact with each other through interface calls. In actual operation, when a user completes a purchase of a digital product or service on the platform, multiple related business processes are often triggered simultaneously, such as generating transaction records, updating asset usage status in the resource sharing center, recording agency sales revenue distribution information, synchronously generating centralized procurement order data, and submitting corresponding financial records. Users may also use AI-powered intelligent customer service for business inquiries or order confirmations, and the customer service system will generate corresponding interaction records and participate in the business processes.

[0003] However, in specific application scenarios, such as when users simultaneously complete digital asset purchases, resource allocation requests, and agent sales registrations within a short period, the different data processing mechanisms and update sequences of each business module can lead to some business records being written or updated at different times. This can result in synchronization delays between transaction records, resource usage information, and agent sales status. When dealing with centralized procurement or subsequent financial accounting, this can cause inconsistencies in the digital asset status information read by the platform at a particular moment, thus affecting the platform's efficiency in uniformly processing related business processes.

[0004] Therefore, it is essential to design a digital asset multi-business collaborative processing system and method to improve business collaboration efficiency. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a digital asset multi-service collaborative processing system and method, which has the advantage of improving business collaboration efficiency and solves the problems mentioned in the background technology.

[0006] To achieve the aforementioned goal of improving business collaboration efficiency, this invention provides the following technical solution: a method for multi-business collaborative processing of digital assets, comprising the following steps:

[0007] The business request messages are uniformly parsed, and the obtained subject identifier, asset occupancy identifier and transaction confirmation identifier are associated to construct a set of digital asset event units.

[0008] Once the set of digital asset event units is written into the platform log record structure, the data associated with the digital asset identifier is retrieved, and the state association calculation is performed on multiple event units corresponding to the same digital asset identifier. A state evolution chain structure reflecting the digital asset processing process is constructed according to the time stamp.

[0009] After the state evolution chain structure is formed, related data from multiple external business record sources are retrieved, and consistency comparison is performed on state nodes involving the same subject identifier. When it is detected that the transaction confirmation state has been established but the resource occupancy state has not been updated synchronously, a corresponding collaborative abnormal node is generated through preset state conflict identification rules.

[0010] Once a collaborative anomaly node is identified, user interaction records and operation trajectory data are retrieved, semantic matching is performed on the interaction text records, and a correspondence between the interaction behavior and the time position of each node in the state evolution chain structure is established to identify collaborative intervention nodes that require manual confirmation during the digital asset processing process.

[0011] Once the collaborative intervention node is determined, the processing trigger sequences of the transaction processing unit, resource scheduling unit, and revenue processing unit are rearranged.

[0012] Preferably, the process of constructing a set of digital asset event units is as follows:

[0013] The business request messages from the access platform are split into fields to extract user entity information, digital asset identifier, asset occupancy identifier, transaction confirmation identifier, and request timestamp.

[0014] The fields obtained from the splitting are standardized and organized according to the preset message field structure rules, and missing fields are removed through the field validation mechanism;

[0015] The standardized entity identifier is bound to the digital asset identifier to form a basic asset event index, and a corresponding set of status tags is established based on the transaction confirmation identifier and the asset occupancy identifier.

[0016] Based on the status tag set and timestamp information, multiple field records corresponding to the same digital asset identifier are encapsulated into a digital asset event unit;

[0017] All event units are categorized and stored according to their asset identifiers to construct a digital asset event unit set.

[0018] Preferably, the process of retrieving data associated with the digital asset identifier is as follows:

[0019] Write the set of digital asset event units into the platform log record structure and generate a unique log index identifier for each event unit;

[0020] Retrieve corresponding asset registration information and historical transaction records from the asset registration database based on the digital asset identifier;

[0021] Read transaction execution data associated with digital asset identifiers, retrieve resource usage records and scheduling history data of the corresponding digital assets, and obtain time information of resource release or usage;

[0022] Asset registration information, transaction execution data, and resource scheduling records are uniformly organized to form a set of associated data corresponding to digital asset identifiers.

[0023] Preferably, the process of constructing a state evolution chain structure reflecting the digital asset processing process according to time stamps is as follows:

[0024] The event units in the digital asset event unit set are sorted according to their corresponding time stamps to form an initial time series;

[0025] Extract the transaction confirmation status, resource occupancy status, and asset change status from the sorted event units, and perform correlation calculations on the relationship between the changes in each status according to the status transition rules;

[0026] Establish state connection relationships between adjacent event units according to the order of state changes, and generate a node sequence describing the state change path;

[0027] In the node sequence, the corresponding state type, occurrence time and trigger source are recorded for each state node, and a complete state evolution chain structure is constructed based on the connection relationship between state nodes.

[0028] Preferably, the process of performing consistency comparison on state nodes involving the same subject identifier is as follows:

[0029] Based on the subject identifier in the state evolution chain structure, retrieve business record data related to the subject from multiple external business record sources;

[0030] Extract transaction records, asset registration records, and processing log information corresponding to digital asset identifiers from various business record sources. Match the extracted data with state nodes in the state evolution chain structure in chronological order and calculate the consistency matching degree between the records corresponding to each node.

[0031] Based on the consistency matching results, state nodes that deviate from external business records are filtered out and marked as potentially inconsistent nodes.

[0032] Preferably, the process of generating corresponding collaborative abnormal nodes through preset state conflict identification rules is as follows:

[0033] Analyze potential inconsistencies in the state evolution chain structure and extract the transaction confirmation time and resource usage update time corresponding to the nodes;

[0034] According to the preset state sequence rules, the sequential relationship between the transaction confirmation state and the resource occupation state is determined, and it is identified whether there is a situation in the state evolution chain where the transaction confirmation state has appeared but the resource occupation state is missing or delayed.

[0035] When it is determined that the transaction confirmation state has been formed but the resource occupancy state has not appeared in the corresponding state stage, the node is marked as a state conflict candidate node.

[0036] Based on the preset state conflict identification rules, the candidate nodes for state conflict are classified by conflict type and marked as cooperative abnormal nodes in the state evolution chain structure.

[0037] Preferably, the process of performing semantic matching on interactive text records is as follows:

[0038] Based on the subject identifier recorded in the collaborative anomaly node, retrieve the interaction records within the corresponding time interval;

[0039] Extract user-submitted text information, operation requests, and system feedback information from the interaction log and form a set of interactive texts;

[0040] The interactive text set is segmented and semantic vector encoded to generate corresponding text semantic feature vectors.

[0041] The text semantic feature vector is matched and calculated with the preset business semantic template, and the semantic matching result associated with the collaborative abnormal node is output.

[0042] Preferably, the process for identifying collaborative intervention nodes requiring manual confirmation during digital asset processing is as follows:

[0043] Extract the corresponding interaction time stamps from the semantic matching results and align them with the time stamps of each node in the state evolution chain structure;

[0044] Calculate the time interval between the interaction time marker and the time of each state node, and select the node with the smallest time interval as the associated node;

[0045] In the state evolution chain structure, establish the association identifier between interactive behavior and state node, count the number and semantic type of interactive behavior corresponding to the same state node, and calculate the corresponding intervention requirement score.

[0046] When the intervention demand score exceeds the preset intervention threshold, the status node is marked as a collaborative intervention node that requires manual confirmation.

[0047] Preferably, the process of rearranging the processing trigger sequences of the transaction processing unit, resource scheduling unit, and revenue processing unit is as follows:

[0048] Identify the associated business processing stages based on the position of the collaborative intervention node in the state evolution chain structure;

[0049] Extract the execution records of the transaction processing unit, resource scheduling unit, and revenue processing unit involved in the business processing stage;

[0050] Based on the status type and conflict identification information of the collaborative intervention nodes, the trigger priority between each processing unit is recalculated, and the processing trigger sequence of each processing unit is rearranged according to the new trigger priority.

[0051] A multi-service collaborative processing system for digital assets, comprising:

[0052] Message decomposition module: Parses and associates the subject identifier, asset occupancy identifier, and transaction confirmation identifier in the business request message to construct a set of digital asset event units;

[0053] Trajectory Construction Module: Performs state calculations on multiple event units corresponding to the same digital asset identifier, and constructs a state evolution chain structure for the digital asset processing process based on time stamps;

[0054] Conflict identification module: retrieves multiple external business record source data, performs consistency comparison on status nodes, and generates collaborative abnormal nodes according to status conflict identification rules;

[0055] Interaction Analysis Module: Performs semantic matching on user interaction records and operation trajectory data, and identifies collaborative intervention nodes based on the time position of interaction time markers and state evolution chain nodes;

[0056] Scheduling rearrangement module: rearranges the processing trigger sequences of transaction processing unit, resource scheduling unit, and revenue processing unit.

[0057] Compared with existing technologies, the present invention provides a digital asset multi-service collaborative processing system and method, which has the following beneficial effects:

[0058] This invention unifies the parsing of business request messages and constructs a set of digital asset event units. This allows data from different business sources to form a unified event record after entering the platform. Through state association calculations, a state evolution chain structure reflecting the entire digital asset processing process is constructed, enabling continuous tracking of the state changes of the same digital asset at each stage of transaction confirmation, resource occupancy, and revenue processing. By retrieving data from external business record sources and comparing the consistency of state nodes under the same entity identifier, and combining this with preset state conflict identification rules, collaborative anomaly nodes are automatically generated. This allows for timely identification of business conflicts caused by asynchrony between transaction and resource states. After anomaly identification, the system analyzes user interaction records and operation trajectory data to establish a correspondence between interactive behaviors and state nodes, accurately locating collaborative intervention nodes requiring manual confirmation. The triggering order of transaction processing units, resource scheduling units, and revenue processing units is dynamically adjusted, enabling the platform to prioritize the execution of key processing units and complete state correction when multiple business state conflicts occur. This improves the consistency, reliability, and processing efficiency of multi-business collaborative processing of digital assets. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the method of the present invention;

[0060] Figure 2 This is a schematic diagram of the structure of the present invention. Detailed Implementation

[0061] 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.

[0062] Example 1: Please refer to Figure 1 As shown in the figure, a digital asset multi-service collaborative processing method in an embodiment of the present invention includes the following steps:

[0063] S1: Perform unified parsing of business request messages, associate the parsed subject identifier, asset occupancy identifier, and transaction confirmation identifier with fields, and construct a set of digital asset event units.

[0064] The process of constructing a set of digital asset event units in S1 is as follows:

[0065] The system performs field splitting on business request messages from the access platform to extract user entity information, digital asset identifier, asset occupancy identifier, transaction confirmation identifier, and request timestamp. A message parsing component is deployed at the platform access layer to perform structural parsing on business request messages from transaction terminals or business interfaces. Based on a predefined message structure template, the component performs field splitting on the message content and converts the message content into a key-value pair data structure. During parsing, user entity information, digital asset identifier, asset occupancy identifier, transaction confirmation identifier, and request timestamp are extracted from the message using field name matching rules. User entity information includes user account identifier or entity number, and digital asset identifier includes asset code or resource number. When duplicate fields with the same name or fields whose types do not conform to the preset format are detected in the message, the parsing component automatically records the anomaly and outputs the field splitting results.

[0066] The fields obtained from the splitting are standardized and organized according to the preset message field structure rules, and missing fields are removed through the field validation mechanism. After the field splitting is completed, the split field data is input into the field standardization processing module. The fields are formatted according to the pre-established message field structure rules. For example, the time field is uniformly converted to the standard timestamp format, and the subject identifier is uniformly converted to the platform's unified encoding format. At the same time, the integrity of key fields is checked through the field validation mechanism. When the user subject information, digital asset identifier or transaction confirmation identifier is detected to be missing, the corresponding record is marked as invalid data and removed from the processing flow through the field validation program. For data records with slight deviations in field format but which can be repaired through rule conversion, the field validation mechanism will be used to correct the data.

[0067] The standardized subject identifier and digital asset identifier are bound together to form a basic asset event index, and a corresponding set of status tags is established based on the transaction confirmation identifier and asset occupancy identifier. After the field standardization is completed, the subject identifier and digital asset identifier are combined and encoded to generate a unique basic asset event index, which is used to identify the business processing relationship between the same subject and the same digital asset. The business events are classified according to the value status of the transaction confirmation identifier and the asset occupancy identifier. For example, when the transaction confirmation identifier is in the confirmed state and the asset occupancy identifier is in the occupied state, the event is marked as the transaction completed state. When the transaction confirmation identifier is in the pending confirmation state, it is marked as the transaction processing state. A corresponding status tag is generated for each business record, and the status tag is written into the event record structure to form a tag set reflecting the asset processing status.

[0068] Based on the status tag set and timestamp information, multiple field records corresponding to the same digital asset identifier are encapsulated into a digital asset event unit. After generating the status tag set, the records are grouped according to the digital asset identifier, and the business records corresponding to the same digital asset identifier are sorted according to the timestamp. The sorted records are combined and encapsulated according to the preset event encapsulation structure. During the encapsulation process, the time continuity detection logic is used to determine whether there is an obvious time disorder between the records. When the time order is abnormal, the record order is readjusted through the sorting algorithm to ensure the continuity of data in the time dimension within each digital asset event unit.

[0069] All event units are categorized and stored according to asset identifiers to construct a digital asset event unit set. After the event units are encapsulated, all event units are grouped and organized according to digital asset identifiers, and multiple event units corresponding to the same asset identifier are stored in a unified data set structure. During the storage process, a fast retrieval index is built for each asset identifier through a set index mechanism, and the number, time distribution, and status distribution of event units are statistically detected. When duplicate records are detected for event units under the same asset identifier, duplicate records are deleted through an event deduplication algorithm, and the earliest valid record is retained.

[0070] S2: After the set of digital asset event units is written into the platform log record structure, the data associated with the digital asset identifier is retrieved, the state association calculation is performed on multiple event units corresponding to the same digital asset identifier, and a state evolution chain structure reflecting the digital asset processing process is constructed according to the time stamp.

[0071] The process of retrieving data associated with digital asset identifiers in S2 is as follows:

[0072] The digital asset event unit set is written into the platform log record structure, and a unique log index identifier is generated for each event unit. After the digital asset event unit set is constructed, the event units are written into the log record structure in batches through the platform log management module. The log record structure uses a time-partitioned log storage table or a distributed log queue for storage. During the writing process, a unique log index identifier is generated for each event unit. The index identifier is generated by combining and encoding the digital asset identifier, the subject identifier, and the event timestamp, and a fixed-length index value is formed by a hash algorithm. After the index is generated, the current log index is checked for duplicates with existing log indexes through an index verification program. When a duplicate index is detected, a new index identifier is generated by recalculating the time series offset value to ensure that each event unit has a unique and searchable identifier in the log structure.

[0073] The system retrieves corresponding asset registration information and historical transaction records from the asset registration database based on the digital asset identifier. After the log is written, the system receives the digital asset identifier through the asset data retrieval module and sends a query request to the asset registration database. The asset registration database pre-establishes an asset registration table and a transaction history table with the digital asset identifier as the primary key. Based on the digital asset identifier, the system performs a primary key query operation to obtain the registration information of the corresponding asset, including basic information such as asset creation time, asset type, asset owner, and current status. At the same time, the system retrieves the transaction history records corresponding to the digital asset identifier from the historical transaction table and sorts them according to the transaction time field. During the data reading process, the system checks the number of records and the integrity of the fields to determine whether there are any missing or abnormal records. When missing registration information or incomplete transaction records are detected, the system re-executes the query operation through the database compensation query mechanism.

[0074] The system reads transaction execution data associated with digital asset identifiers, retrieves resource occupancy records and scheduling history data for the corresponding digital assets, and obtains the time information of resource release or occupancy. After obtaining asset registration data, it reads transaction execution data corresponding to digital asset identifiers from the transaction processing system through the business execution interface, including information such as transaction execution status, execution node number, and execution completion time. It retrieves resource occupancy records and scheduling history data related to the digital asset through the resource scheduling system interface. The resource occupancy records include resource number, occupancy status, and occupancy time, while the scheduling history data includes resource allocation time and resource release time. During the data reading process, a time consistency detection logic is used to perform a preliminary comparison between transaction execution time and resource occupancy time. When a time gap or sequence abnormality is detected in the resource occupancy record, the corresponding scheduling record is reread through log backtracking to ensure that the obtained resource usage information remains complete and continuous in the time dimension.

[0075] Asset registration information, transaction execution data, and resource scheduling records are uniformly organized to form a set of associated data corresponding to digital asset identifiers. After completing the reading of various types of data, the asset registration information, transaction execution data, and resource scheduling records are uniformly structured through the data integration module. Data from different sources are merged and organized according to digital asset identifiers. During the organization process, all data records are sorted, and a source tag is added to each data record according to its source. The data consistency detection logic checks whether there are obvious conflicts between transaction execution records and resource occupancy records, such as situations where the transaction has been completed but the resource has not been occupied, or the resource has been released but the transaction status is still in execution. For data records with obvious contradictions, an anomaly marker is recorded through a marking mechanism, but the original data is retained.

[0076] The process of constructing a state evolution chain structure reflecting the digital asset processing process according to time stamps in S2 is as follows:

[0077] The event units in the digital asset event unit set are sorted according to their corresponding timestamps to form an initial time series. After obtaining the digital asset event unit set, the timestamp field recorded in each event unit is read through the time sorting module, and all event units are sorted in ascending order using a timestamp-based sorting algorithm. During the sorting process, the time field is uniformly converted to a unified format, converting the time formats generated by different source systems into the platform's standard timestamp format. Then, a time series list is generated using quicksort or stable merge sort algorithms. After sorting, the sequence is checked for integrity through a time continuity detection program. When obvious abnormal time records are detected, the original records are reread and a secondary sorting correction is performed through log tracing, thereby forming an initial time series that reflects the chronological order of events.

[0078] The system extracts the transaction confirmation status, resource occupancy status, and asset change status from the sorted event units, and performs correlation calculations on the relationship between the states according to the state transition rules. After the initial time series is generated, the system extracts the transaction confirmation status field, resource occupancy status field, and asset change status field from each event unit, and performs unified encoding processing on each status field according to the pre-set status encoding table. The system also performs correlation calculations on the relationship between the states of adjacent event units according to the preset state transition rules. For example, when the transaction confirmation status changes from pending confirmation to confirmed and the resource occupancy status changes from idle to occupied, it is determined to be a complete transaction execution state transition. During the calculation process, the system verifies whether the state changes conform to the business process through the state consistency detection logic. When abnormal situations such as state jumps or state rollbacks are detected, the abnormal state is recorded through a marking mechanism.

[0079] Based on the order of state changes, state connections are established between adjacent event units to generate a node sequence describing the state change path. After completing the state association calculation, the link construction module establishes state connection edges between adjacent event units based on their positional relationship in the time series, and converts each event unit into a corresponding state node. The state nodes are then connected sequentially in chronological order to form a node sequence, where each connection represents a state change process. When constructing node connections, a connection integrity detection logic checks for isolated nodes or interrupted connections. If a node is not correctly connected, the connection is restored by re-matching adjacent time nodes, thereby generating a node sequence structure that reflects the asset state change path.

[0080] In the node sequence, the corresponding state type, occurrence time, and trigger source are recorded for each state node. A complete state evolution chain structure is constructed based on the connection relationship between state nodes. After the node sequence is generated, node attribute information is added to each state node, including a state type field, a state occurrence time field, and a trigger source field. The trigger source is used to record whether the state change is triggered by a transaction operation, resource scheduling operation, or automatic system processing. A chain data structure is constructed based on the connection relationship between nodes. The predecessor and successor node information of each node is recorded in the chain structure index table. During the chain structure generation process, the entire chain is traversed and checked through the chain integrity detection logic. When a chain break or node duplication is detected, it is repaired by reconnecting adjacent nodes or deleting duplicate nodes, forming a state evolution chain structure that can continuously record the process of digital assets changing from the initial state to the current state.

[0081] S3: After the state evolution chain structure is formed, retrieve the associated data from multiple external business record sources, perform consistency comparison on the state nodes involving the same subject identifier, and when it is detected that the transaction confirmation state has been established but the resource occupancy state has not been updated synchronously, generate the corresponding collaborative abnormal node through the preset state conflict identification rules.

[0082] The consistency comparison process for state nodes involving the same subject identifier in S3 is as follows:

[0083] Based on the entity identifier in the state evolution chain structure, business record data related to the entity is retrieved from multiple external business record sources. After the state evolution chain structure is completed, the entity identifier recorded by each state node in the chain structure is read through the consistency verification module, and the entity identifier is used as the query keyword to initiate a data retrieval request to multiple external business record sources, including the transaction processing system database, the asset registration system database, and the business processing log database. The index query is executed based on the entity identifier to obtain all business record data associated with the entity, and the query results are initially filtered to retain only the records that are related to the current digital asset identifier. During the retrieval process, the data integrity detection logic is used to determine whether there are missing or abnormal records in the query results. When it is detected that the number of returned records is significantly less than the historical statistical range, the data retrieval is executed again through the backup query interface or the log backtracking mechanism.

[0084] The system extracts transaction records, asset registration records, and processing log information corresponding to digital asset identifiers from various business record sources. The extracted data is then matched against state nodes in the state evolution chain structure in chronological order, and the consistency matching degree between records corresponding to each node is calculated. After data retrieval, the system extracts transaction records, asset registration records, and business processing log information corresponding to digital asset identifiers from various business record sources. The extracted data undergoes field standardization to unify the field structures of different source systems into a unified platform format. Records are matched one by one against state nodes in the state evolution chain structure. During the matching process, a field comparison algorithm is used to calculate the consistency of key fields such as transaction status, resource occupancy status, and asset status. The consistency matching degree is generated by statistically analyzing the percentage of matches between state node field values ​​and external record field values. Simultaneously, a time consistency detection logic is used to determine whether the difference between the external record time and the state node time is within a reasonable business range, thus ensuring that the matching process considers both state consistency and time rationality.

[0085] Based on the consistency matching results, state nodes that deviate from external business records are filtered out and marked as potential inconsistent nodes. After the consistency matching degree is calculated, the consistency matching degree corresponding to each state node is statistically analyzed by the result filtering module, and the nodes are classified according to the matching results. For nodes with significantly fewer matching fields than other nodes or with inconsistent states of key fields, they are identified as state nodes with business record deviations. During the identification process, the rule judgment program further verifies whether the deviation is caused by time delay or system update lag. When it is confirmed that the deviation is not caused by normal delay, the corresponding state node is marked as a potential inconsistent node.

[0086] The process of generating corresponding collaborative abnormal nodes in S3 based on preset state conflict identification rules is as follows:

[0087] Analyze potential inconsistencies in the state evolution chain structure and extract the transaction confirmation time and resource occupation update time corresponding to the node. After detecting a potential inconsistency node, the anomaly analysis module reads and processes the node and its adjacent nodes, extracts the transaction confirmation time field from the node attribute field, and queries the resource occupation record corresponding to the digital asset identifier in the resource scheduling record table. Read the resource occupation update time from the record. During the reading process, the field integrity detection logic checks whether the two time fields are missing or have abnormal format. When a field is missing, the original business record is reread through log backtracking.

[0088] According to the preset state sequence rules, the sequential relationship between the transaction confirmation state and the resource occupation state is determined, and it is identified whether there is a situation in the state evolution chain where the transaction confirmation state has appeared but the resource occupation state is missing or delayed. After obtaining relevant time information, the system reads the pre-configured state sequence rules, which are used to describe the order in which the states of digital assets appear in the normal business process. For example, the resource occupation state should appear before or simultaneously with the transaction confirmation state. The system retrieves the resource occupation state node corresponding to the current node in the state evolution chain structure, and compares the appearance order of the transaction confirmation state and the resource occupation state according to the node time information. When it is detected that the corresponding resource occupation state node does not appear in the chain structure or its appearance position is significantly later than the transaction confirmation state node, the state sequence abnormality information is recorded.

[0089] When it is determined that the transaction confirmation state has been formed but the resource occupation state has not appeared in the corresponding state stage, the node is marked as a state conflict candidate node. After the order determination is completed, the state order abnormality information is confirmed by the conflict candidate identification module. The resource scheduling record is checked to see if there is a resource occupation record corresponding to the digital asset identifier but not yet written into the chain structure. When it is confirmed that there is no corresponding record, the current transaction confirmation node is marked as a state conflict candidate node. During the marking process, the node is attached with a candidate conflict flag through the node identifier field and the node number is written into the candidate node list. At the same time, the position of the node in the chain structure is confirmed again by the chain structure traversal program to avoid duplicate marking or misjudgment.

[0090] According to the preset state conflict identification rules, the candidate nodes for state conflicts are classified by conflict type and marked as cooperative abnormal nodes in the state evolution chain structure. After obtaining the candidate nodes for state conflicts, the conflict identification module calls the preset state conflict identification rules to determine the conflict type of the nodes. The rules include types such as resource occupation missing conflict, state update order abnormal conflict, and state record incomplete conflict. By reading the state field and time field of the candidate node and its adjacent nodes, the node state combination relationship is analyzed, and the corresponding conflict type is determined according to the rule matching result. After the classification is completed, the node conflict type information is written into the node attribute field, and the node is marked as a cooperative abnormal node in the state evolution chain structure.

[0091] S4: Once a collaborative anomaly node is identified, retrieve user interaction records and operation trajectory data, perform semantic matching on the interaction text records, and establish a correspondence between interaction behaviors based on the interaction time markers and the time positions of each node in the state evolution chain structure to identify collaborative intervention nodes that require manual confirmation during the digital asset processing process.

[0092] The process of performing semantic matching on interactive text records in S4 is as follows:

[0093] Based on the subject identifier recorded in the collaborative abnormal node, the interaction records within the corresponding time interval are retrieved; after identifying the collaborative abnormal node, the subject identifier and the time interval information corresponding to the abnormal node recorded in the node attributes are read, and the index retrieval is performed in the platform interaction log database using the subject identifier as the query condition. The interaction log database is used to store the user's operation records, message interaction records and system prompt information in the business system. All interaction records of the subject are filtered out based on the subject identifier, and the records are time-filtered based on the time interval corresponding to the abnormal node, retaining only the interaction data that occurred within the preset analysis interval before and after the abnormal node;

[0094] The system extracts user-submitted text information, operation requests, and system feedback information from the interaction records to form an interactive text set. After obtaining the target interaction records, the data parsing module performs field parsing on each record, extracting the user-input text information field, operation request field, and system feedback field from the record structure. The extracted text content is standardized, including character encoding unification, invalid character filtering, and duplicate content removal. During the processing, the text validity detection logic determines whether the text content is empty or contains only meaningless characters. When invalid text is detected, the corresponding record is removed. After completing the field extraction and filtering, the user-input text, operation request description, and system feedback information are combined and organized in chronological order to construct an interactive text set containing multiple text records.

[0095] The interactive text set is segmented and semantically encoded to generate corresponding text semantic feature vectors. After forming the interactive text set, each text content in the set is segmented. The text is segmented into words using a pre-built domain dictionary, and stop words and irrelevant words are filtered out. The text content is vectorized and encoded using a pre-trained text representation model to generate semantic feature vectors that reflect the semantic features of the text. During the encoding process, the vector generation detection logic checks whether the dimension of the generated vectors conforms to the standard format set by the system, and abnormal vectors are re-encoded to ensure that each interactive text corresponds to a semantic feature vector with a consistent structure.

[0096] The text semantic feature vector is matched with a preset business semantic template to calculate the semantic matching result associated with the collaborative abnormal node. After generating the semantic feature vector, a pre-established business semantic template library is read to describe semantic features such as operation intentions, transaction confirmation behaviors, or resource scheduling requests in typical business scenarios. The similarity between the text semantic feature vector and the template semantic vector is calculated, and the matching result is obtained through the vector similarity calculation method. During the matching process, templates with high similarity are selected through the matching validity detection logic, and the corresponding template type and matching score are recorded. The matching result is associated with the collaborative abnormal node and stored, and the corresponding semantic matching information is attached to the abnormal node record.

[0097] The process of identifying collaborative intervention nodes that require manual confirmation during digital asset processing in S4 is as follows:

[0098] Extract the corresponding interaction time markers from the semantic matching results and align them with the time markers of each node in the state evolution chain structure. Calculate the time interval between the interaction time markers and the times of each state node, and select the node with the smallest time interval as the associated node. After the time alignment data table is generated, calculate the time difference between each interaction time marker and the times of each node in the state evolution chain structure, and record the calculation results as the time interval between the interaction time and the node time. Select the node with the smallest time interval from the corresponding node set as the associated node of the interaction behavior through the minimum interval filtering logic. During the filtering process, check whether the selected node is in a business stage near the interaction time through the time rationality detection logic. For example, determine whether the node belongs to the transaction processing stage or the resource scheduling stage. When it is detected that the minimum interval node does not match the business stage, match again through the second smallest interval node, thereby improving the accuracy of the identification of the associated node of the interaction behavior.

[0099] In the state evolution chain structure, an association identifier is established between interactive behaviors and state nodes. The number and semantic type of interactive behaviors corresponding to the same state node are counted, and the corresponding intervention requirement score is calculated. After completing the association matching between interactive behaviors and nodes, an interactive association identifier is written for the corresponding node in the state evolution chain structure, and the semantic type information of the interactive behavior is recorded. All interactive behaviors associated with the same node are counted, and the number of interactive behaviors and the occurrence frequency of various semantic types are calculated, such as user confirmation request semantics, transaction anomaly consultation semantics, or operation failure prompt semantics. Based on the statistical results, the node is scored according to the intervention evaluation rules. The scoring criteria include the number of interactive behaviors, the proportion of abnormal semantic occurrences, and the concentration of interactive behaviors in time. During the scoring calculation process, the statistical verification logic is used to verify whether there are duplicate counts or missing data in the statistical results to ensure that the scoring results are accurate and reliable.

[0100] When the intervention demand score exceeds the preset intervention threshold, the status node is marked as a collaborative intervention node requiring manual confirmation. After obtaining the intervention demand score, the score result is compared with the intervention judgment rules preset by the system. When the score result meets the manual intervention judgment conditions, the manual confirmation identifier field is written for the corresponding node in the state evolution chain structure, and the node is recorded as a collaborative intervention node. The node identifier, associated interaction information and score result are written into the intervention node record table, and a processing prompt message is sent to the manual processing module through the platform alarm interface. During the writing process, the node status verification logic is used to confirm again that the node is not currently marked as an intervention node to avoid duplicate marking, thereby completing the process of identifying collaborative intervention nodes requiring manual confirmation.

[0101] S5: Once the collaborative intervention node is determined, the processing trigger sequences of the transaction processing unit, resource scheduling unit, and revenue processing unit are rearranged.

[0102] The process of rearranging the processing trigger sequences of the transaction processing unit, resource scheduling unit, and revenue processing unit in S5 is as follows:

[0103] Based on the position of the collaborative intervention node in the state evolution chain structure, identify the associated business processing stages; extract the execution records of the transaction processing units, resource scheduling units, and revenue processing units involved in the business processing stages; recalculate the trigger priority between each processing unit according to the state type and conflict identification information of the collaborative intervention node, and rearrange the processing trigger sequence of each processing unit according to the new trigger priority; read the state type field and conflict identification information of the collaborative intervention node, where the state type indicates that the current intervention belongs to one of the following: abnormal transaction state, resource occupation conflict, or abnormal revenue distribution; the conflict identification information records the identifier of the processing unit involved in the conflict and the identifier of the conflict object, and compares the information with the execution time, resource object identifier, and execution result in the stage execution record table. Correlation analysis is performed. When an incomplete order or status rollback record is detected in a transaction processing unit, the trigger priority of the transaction processing unit is raised to the highest level. When a resource object identifier of a resource scheduling unit is found to be consistent with the resource object identifier referenced by other processing units and the resource status is occupied, the priority of the resource scheduling unit is set higher than that of the revenue processing unit. If the conflict identifier points to revenue sharing or settlement anomalies, the priority of the revenue processing unit is increased and it is adjusted to be executed after the transaction processing unit. After the priority calculation is completed, the transaction processing unit, resource scheduling unit, and revenue processing unit are sorted from high to low according to the priority value to generate a new processing trigger sequence, so as to ensure that status conflicts in the business processing process can be resolved first and the continuity of the business process can be restored.

[0104] Example 2: Figure 2As shown, a digital asset multi-service collaborative processing system includes:

[0105] Message decomposition module: Parses and associates the subject identifier, asset occupancy identifier, and transaction confirmation identifier in the business request message to construct a set of digital asset event units;

[0106] Trajectory Construction Module: Performs state calculations on multiple event units corresponding to the same digital asset identifier, and constructs a state evolution chain structure for the digital asset processing process based on time stamps;

[0107] Conflict identification module: retrieves multiple external business record source data, performs consistency comparison on status nodes, and generates collaborative abnormal nodes according to status conflict identification rules;

[0108] Interaction Analysis Module: Performs semantic matching on user interaction records and operation trajectory data, and identifies collaborative intervention nodes based on the time position of interaction time markers and state evolution chain nodes;

[0109] Scheduling rearrangement module: rearranges the processing trigger sequences of transaction processing unit, resource scheduling unit, and revenue processing unit.

[0110] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0111] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for multi-service collaborative processing of digital assets, characterized in that, Includes the following steps: The business request messages are uniformly parsed, and the obtained subject identifier, asset occupancy identifier and transaction confirmation identifier are associated to construct a set of digital asset event units. Once the set of digital asset event units is written into the platform log record structure, the data associated with the digital asset identifier is retrieved, and the state association calculation is performed on multiple event units corresponding to the same digital asset identifier. A state evolution chain structure reflecting the digital asset processing process is constructed according to the time stamp. After the state evolution chain structure is formed, related data from multiple external business record sources are retrieved, and consistency comparison is performed on state nodes involving the same subject identifier. When it is detected that the transaction confirmation state has been established but the resource occupancy state has not been updated synchronously, a corresponding collaborative abnormal node is generated through preset state conflict identification rules. Once a collaborative anomaly node is identified, user interaction records and operation trajectory data are retrieved, semantic matching is performed on the interaction text records, and a correspondence between the interaction behavior and the time position of each node in the state evolution chain structure is established to identify collaborative intervention nodes that require manual confirmation during the digital asset processing process. Once the collaborative intervention node is determined, the processing trigger sequences of the transaction processing unit, resource scheduling unit, and revenue processing unit are rearranged.

2. The method for multi-service collaborative processing of digital assets according to claim 1, characterized in that, The process of constructing a set of digital asset event units is as follows: The business request messages from the access platform are split into fields to extract user entity information, digital asset identifier, asset occupancy identifier, transaction confirmation identifier, and request timestamp. The fields obtained from the splitting are standardized and organized according to the preset message field structure rules, and missing fields are removed through the field validation mechanism; The standardized entity identifier is bound to the digital asset identifier to form a basic asset event index, and a corresponding set of status tags is established based on the transaction confirmation identifier and the asset occupancy identifier. Based on the status tag set and timestamp information, multiple field records corresponding to the same digital asset identifier are encapsulated into a digital asset event unit; All event units are categorized and stored according to their asset identifiers to construct a digital asset event unit set.

3. The method for multi-service collaborative processing of digital assets according to claim 2, characterized in that, The process of retrieving data associated with digital asset identifiers is as follows: Write the set of digital asset event units into the platform log record structure and generate a unique log index identifier for each event unit; Retrieve corresponding asset registration information and historical transaction records from the asset registration database based on the digital asset identifier; Read transaction execution data associated with digital asset identifiers, retrieve resource usage records and scheduling history data of the corresponding digital assets, and obtain time information of resource release or usage; Asset registration information, transaction execution data, and resource scheduling records are uniformly organized to form a set of associated data corresponding to digital asset identifiers.

4. The method for multi-service collaborative processing of digital assets according to claim 3, characterized in that, The process of constructing a state evolution chain structure that reflects the digital asset processing process based on time stamps is as follows: The event units in the digital asset event unit set are sorted according to their corresponding time stamps to form an initial time series; Extract the transaction confirmation status, resource occupancy status, and asset change status from the sorted event units, and perform correlation calculations on the relationship between the changes in each status according to the status transition rules; Establish state connection relationships between adjacent event units according to the order of state changes, and generate a node sequence describing the state change path; In the node sequence, the corresponding state type, occurrence time and trigger source are recorded for each state node, and a complete state evolution chain structure is constructed based on the connection relationship between state nodes.

5. The method for multi-service collaborative processing of digital assets according to claim 4, characterized in that, The process of performing consistency comparison on state nodes involving the same subject identifier is as follows: Based on the subject identifier in the state evolution chain structure, retrieve business record data related to the subject from multiple external business record sources; Extract transaction records, asset registration records, and processing log information corresponding to digital asset identifiers from various business record sources. Match the extracted data with state nodes in the state evolution chain structure in chronological order and calculate the consistency matching degree between the records corresponding to each node. Based on the consistency matching results, state nodes that deviate from external business records are filtered out and marked as potentially inconsistent nodes.

6. The method for multi-service collaborative processing of digital assets according to claim 5, characterized in that, The process of generating corresponding collaborative abnormal nodes based on preset state conflict identification rules is as follows: Analyze potential inconsistencies in the state evolution chain structure and extract the transaction confirmation time and resource usage update time corresponding to the nodes; According to the preset state sequence rules, the sequential relationship between the transaction confirmation state and the resource occupation state is determined, and it is identified whether there is a situation in the state evolution chain where the transaction confirmation state has appeared but the resource occupation state is missing or delayed. When it is determined that the transaction confirmation state has been formed but the resource occupancy state has not appeared in the corresponding state stage, the node is marked as a state conflict candidate node. Based on the preset state conflict identification rules, the candidate nodes for state conflict are classified by conflict type and marked as cooperative abnormal nodes in the state evolution chain structure.

7. A method for multi-service collaborative processing of digital assets according to claim 6, characterized in that, The process of performing semantic matching on interactive text records is as follows: Based on the subject identifier recorded in the collaborative anomaly node, retrieve the interaction records within the corresponding time interval; Extract user-submitted text information, operation requests, and system feedback information from the interaction log and form a set of interactive texts; The interactive text set is segmented and semantic vector encoded to generate corresponding text semantic feature vectors. The text semantic feature vector is matched and calculated with the preset business semantic template, and the semantic matching result associated with the collaborative abnormal node is output.

8. A method for multi-service collaborative processing of digital assets according to claim 7, characterized in that, The process of identifying collaborative intervention nodes that require manual confirmation during digital asset processing is as follows: Extract the corresponding interaction time stamps from the semantic matching results and align them with the time stamps of each node in the state evolution chain structure; Calculate the time interval between the interaction time marker and the time of each state node, and select the node with the smallest time interval as the associated node; In the state evolution chain structure, establish the association identifier between interactive behavior and state node, count the number and semantic type of interactive behavior corresponding to the same state node, and calculate the corresponding intervention requirement score. When the intervention demand score exceeds the preset intervention threshold, the status node is marked as a collaborative intervention node that requires manual confirmation.

9. A method for multi-service collaborative processing of digital assets according to claim 8, characterized in that, The process of rearranging the processing trigger sequences of the transaction processing unit, resource scheduling unit, and revenue processing unit is as follows: Identify the associated business processing stages based on the position of the collaborative intervention node in the state evolution chain structure; Extract the execution records of the transaction processing unit, resource scheduling unit, and revenue processing unit involved in the business processing stage; Based on the status type and conflict identification information of the collaborative intervention nodes, the trigger priority between each processing unit is recalculated, and the processing trigger sequence of each processing unit is rearranged according to the new trigger priority.

10. A digital asset multi-service collaborative processing system, applied to the method described in any one of claims 1-9, characterized in that, include: Message decomposition module: Parses and associates the subject identifier, asset occupancy identifier, and transaction confirmation identifier in the business request message to construct a set of digital asset event units; Trajectory Construction Module: Performs state calculations on multiple event units corresponding to the same digital asset identifier, and constructs a state evolution chain structure for the digital asset processing process based on time stamps; Conflict identification module: retrieves multiple external business record source data, performs consistency comparison on status nodes, and generates collaborative abnormal nodes according to status conflict identification rules; Interaction Analysis Module: Performs semantic matching on user interaction records and operation trajectory data, and identifies collaborative intervention nodes based on the time position of interaction time markers and state evolution chain nodes; Scheduling rearrangement module: rearranges the processing trigger sequences of transaction processing unit, resource scheduling unit, and revenue processing unit.

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