A hotel room state reservation and rollback recovery method based on an OLTP architecture

Abstract

The application relates to a hotel room state reservation and rollback recovery method based on an OLTP architecture, which collects business operation atoms and context snapshots in a transaction initialization stage, generates a unique original transaction node set, further analyzes explicit and implicit causal dependencies between nodes, and constructs a dynamic causal time sequence graph atlas without loops. In a rollback scenario, the graph atlas is used to accurately trim the rollback influence range, safe and rollbackable nodes are screened according to business semantics and consistency rules, rollback action instruction streams are generated in reverse order of causality, and consistency simulation verification is carried out in a sandbox environment. According to the evaluation results, the rollback instruction stream is submitted to the database for execution in the form of an atomic transaction package only when the verification is passed, and fine rollback and resource lock release are realized. The scheme improves the accuracy of rollback and system data consistency in a concurrent scenario, and enhances the traceability and safety of abnormal processing.

Description

Technical Field

[0001] This invention relates to the field of transaction control and data consistency assurance technology in hotel management systems, and in particular to a method for hotel room status booking and rollback recovery based on an OLTP architecture. Background Technology

[0002] With the popularization of hotel information and intelligent management, hotel room reservation systems based on OLTP (Online Transaction Processing) architecture have become the mainstream technical solution for the hotel industry to achieve automated operation and high concurrency processing capabilities. In existing technologies, hotel room reservation typically adopts the standard database ACID transaction mechanism, supplemented by a rollback recovery module based on step compensation and log mirroring technology, aiming to ensure the atomicity, consistency and data security of business processes.

[0003] Current mainstream hotel management system transaction rollback mechanisms mostly rely on operation log recording and compensation logic. When a rollback is triggered, the previous state is restored by reversing the execution of logs or calling the corresponding compensation interface. Some advanced systems have also introduced distributed transaction managers and middleware to coordinate resource reclamation among various microservices, further improving end-to-end consistency and transaction isolation in high-concurrency environments. As business complexity increases, emerging technologies such as event sourcing and the Saga pattern have also been introduced into the rollback and recovery field to address asynchronous operations and cross-domain transaction scenarios.

[0004] Nevertheless, existing technologies still have significant shortcomings in the context of complex business flows and high-concurrency, multi-step operations. First, most rollback mechanisms are based solely on in-place state restoration (through persistent snapshots or forward operation mirrors), without providing structured and dynamic modeling of the execution direction and causal order of transaction operations. This leads to the rollback order being easily disrupted when booking involves multiple resources (such as room status, points accounts, coupons, and payments) with coexisting dependencies. This can easily cause serious anomalies such as resource leaks, dirty room status locks, and account inconsistencies, increasing the burden of subsequent data cleanup and manual verification.

[0005] Due to the aforementioned deficiencies, existing technologies urgently need a rollback and recovery method that can accurately and dynamically model the directionality and multi-level causal sequence of various transaction operations during the hotel room booking process. This would completely avoid typical consistency risks such as rollback sequence disorder, compensation traversal, and data hanging, and further improve the automatic fault tolerance capability and system robustness of the business process. Summary of the Invention

[0006] This application provides a hotel room status booking and rollback recovery method based on OLTP architecture, aiming to solve the problems mentioned in the background technology, such as the hotel room status rollback order being easily disordered, which can easily lead to resource leakage, dirty room status locks, and account inconsistencies.

[0007] This application provides a hotel room availability booking and rollback recovery method based on an OLTP architecture, specifically including:

[0008] S1: Obtain multiple business operation atoms and their context snapshots during the initialization phase of the hotel booking transaction, and generate an original transaction node set with a unique operation ID based on the business operation atoms and context snapshots;

[0009] S2: Perform causal dependency analysis on each node in the original transaction node set, identify explicit and implicit logical sequence constraints, and construct a dynamic causal time sequence graph;

[0010] S3: Based on the hotel's rollback trigger event, locate the set of terminal operation nodes in the dynamic causal time sequence graph, traverse in reverse along the directed edges to identify all affected reachable path nodes, perform irreversible and non-causal pruning on the reachable path nodes, and generate a simplified rollback candidate node sequence.

[0011] S4: Execute the topological reverse sorting algorithm on the simplified rollback candidate node sequence, verify the logical coherence, and generate the rollback operation instruction stream to be verified.

[0012] S5: Input the rollback operation instruction stream to be verified into the lightweight execution sandbox environment, and output a rollback feasibility assessment report containing the consistency verification status;

[0013] S6: Determine whether the consistency verification status in the rollback feasibility assessment report has passed. If it has passed, encapsulate the rollback operation instruction flow to be verified into an atomic transaction package. If it has failed, terminate the execution and generate an abnormal suspension record.

[0014] S7: Submit the atomic transaction package to the OLTP database engine to perform atomic write operations, output the transaction rollback completion log, and complete the hotel rollback recovery based on the transaction rollback completion log.

[0015] This application provides a hotel room availability booking and rollback recovery method based on an OLTP architecture, which has the following beneficial effects:

[0016] This invention constructs a new, efficient, secure, and scalable distributed transaction rollback mechanism. It not only breaks through the path dependency of traditional compensation transactions on the execution of forward process mirrors, but also provides a structured, traceable, and analyzable causal basis for exception handling. It effectively solves the core pain points of existing technologies, such as chaotic compensation logic, inconsistent states, and high system coupling, and significantly improves the reliability and maintenance efficiency of business transactions in a large-scale distributed environment of hotels. Attached Figure Description

[0017] Figure 1This is a main flowchart of a hotel room status booking and rollback recovery method based on an OLTP architecture.

[0018] Figure 2 This is a sub-flowchart of a hotel room availability booking and rollback recovery method based on an OLTP architecture.

[0019] Figure 3 This is another sub-flowchart of a hotel room availability booking and rollback recovery method based on an OLTP architecture. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0021] The following disclosure provides many different embodiments or examples for implementing different structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples; such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0022] like Figure 1 As shown, this application provides a hotel room availability booking and rollback recovery method based on an OLTP architecture, specifically including:

[0023] S1: Obtain multiple business operation atoms and their context snapshots during the initialization phase of the hotel booking transaction, and generate an original transaction node set with a unique operation ID based on the business operation atoms and context snapshots;

[0024] S2: Perform causal dependency analysis on each node in the original transaction node set, identify explicit and implicit logical sequence constraints, and construct a dynamic causal time sequence graph;

[0025] S3: Based on the hotel's rollback trigger event, locate the set of terminal operation nodes in the dynamic causal time sequence graph, traverse in reverse along the directed edges to identify all affected reachable path nodes, perform irreversible and non-causal pruning on the reachable path nodes, and generate a simplified rollback candidate node sequence.

[0026] S4: Execute the topological reverse sorting algorithm on the simplified rollback candidate node sequence, verify the logical coherence, and generate the rollback operation instruction stream to be verified.

[0027] S5: Input the rollback operation instruction stream to be verified into the lightweight execution sandbox environment, and output a rollback feasibility assessment report containing the consistency verification status;

[0028] S6: Determine whether the consistency verification status in the rollback feasibility assessment report has passed. If it has passed, encapsulate the rollback operation instruction flow to be verified into an atomic transaction package. If it has failed, terminate the execution and generate an abnormal suspension record.

[0029] S7: Submit the atomic transaction package to the OLTP database engine to perform atomic write operations, output the transaction rollback completion log, and complete the hotel rollback recovery based on the transaction rollback completion log.

[0030] Step S1: Obtain multiple business operation atoms and their context snapshots during the hotel booking transaction initialization phase, and generate an original transaction node set with unique operation IDs based on the business operation atoms and context snapshots. Specifically, this includes:

[0031] S1.1: Obtain multiple atomic data streams of business operations during the initialization phase of the hotel booking transaction, and perform hash encoding on each business operation atom based on the snowflake algorithm to generate a globally unique set of operation IDs as the core index key for subsequent node mapping.

[0032] The system acquires atomic data streams of business operations during the initialization phase of hotel booking transactions. These data streams include discrete operation instructions such as room status locking, points freezing, and coupon reservation. The received atomic business operations are standardized and parsed to extract the operation type code, target resource identifier, and trigger timestamp as basic metadata. Based on the Snowflake algorithm, the operation type code, resource identifier hash value, and nanosecond-level timestamp are selected as input parameters. SHA-256 hash encoding is used to perform mixed operations on these input parameters, generating a fixed-length hexadecimal string. Through truncation and checksum supplementation, the hash string is converted into a unique operation ID conforming to the UUID v4 standard. A unique operation ID is assigned to each atomic business operation, establishing a one-to-one mapping between operation instances and identifiers. An index dictionary using unique operation IDs as keys is constructed for rapid retrieval of subsequent nodes and causal chain tracing. By using hash encoding, the atomic business operations from the previous step are transformed into a globally unique set of operation IDs, achieving the expected technical effect of conflict-free identification and accurate mapping of transaction nodes in a distributed environment.

[0033] S1.2: Read the real-time room status resource status data associated with the unique operation ID set, and use the multi-version concurrency control mechanism to extract the version number information and mutex lock identifier information of the current room status data, so as to generate a room status version latch data packet containing precise status markers as the basic component of the context snapshot.

[0034] The system receives a set of unique operation IDs generated by S1.1 as index keys and iterates through each operation ID to locate its associated room status resource object. It calls the read interface of the multi-version concurrency control module to retrieve the current room status data row in memory based on the resource identifier in the operation ID. It extracts the global version number field stored in the data row, which represents the logical evolution sequence of the room status data. It obtains the visibility rules under the current transaction isolation level and verifies whether the extracted version number is visible to the current transaction. If the version number is not visible, it backtracks along the version chain to find the nearest previously visible version, ensuring consistency of the read state. It reads the mutex lock identifier field in the room status data row and parses the thread ID holding the lock and the locking timestamp. It determines whether the current lock state is an exclusive lock or a shared lock and records the lock granularity information, such as room level or room type level. It structurally encapsulates the extracted global version number and the parsed mutex lock identifier. It generates a room status version latch data packet containing precise status markers, which serves as a fundamental component of the context snapshot. Through the above processing method, the results of the previous step are transformed into room status indicators with spatiotemporal consistency, achieving the expected technical effect of accurately anchoring the rollback baseline data.

[0035] S1.3: Parse the business rule metadata in the atomic data stream of the business operation, and perform feature extraction processing on the operation type, dependency strength and reversibility features based on the predefined semantic tag classification module to generate a set of business semantic tags describing the business logic constraints to define the operation attributes.

[0036] The metadata structure in the atomic data stream of business operations is analyzed to extract operation type identifiers, resource scopes, and trigger condition fields. Based on a predefined semantic tag classification module, the extracted metadata features are vector-mapped to transform discrete business rules into standardized semantic feature vectors. The dependency strength coefficient of the operation type is calculated, and three levels—strong dependency, weak dependency, and no dependency—are defined according to business logic constraints, generating dependency strength tags. The reversibility characteristics of the operation are evaluated; combining resource locking status and external interaction records, it is determined whether the operation has lossless rollback capability, generating reversibility Boolean tags. The dependency strength tags and reversibility Boolean tags are integrated to construct a set of business semantic tags containing operation attribute descriptions. Through the above processing, the atomic operations obtained in the previous step are transformed into semantic tag data with logical constraint attributes, achieving a refined definition of the causal characteristics of business operations and providing accurate node attribute input for subsequent construction of dynamic causal time-series graphs.

[0037] S1.4: Integrate the unique operation ID set, room status version latch data packet and business semantic tag set, and use a structured data encapsulation protocol to bind and map the operation identifier, version status and semantic features to generate an original transaction context snapshot object containing complete context information.

[0038] S1.5: Traverse all generated original transaction context snapshot objects, and combine and encapsulate the operation atoms with their corresponding original transaction context snapshot objects based on memory object instantiation technology to generate a set of original transaction nodes with unique operation IDs and complete traceability as the final output of this step.

[0039] Step S2: Perform causal dependency analysis on each node in the original transaction node set, identify explicit and implicit logical sequence constraints, and construct a dynamic causal time sequence graph. Specifically, this includes:

[0040] S2.1: Obtain the business operation atoms and their associated business semantic tag data from the original transaction node set, and perform feature extraction processing on the business semantic tag data based on the predefined causal rule engine to generate a structured dependency feature vector set containing explicit dependency identifiers and implicit logical constraints.

[0041] Read the business semantic tags carried by each node in the original transaction node set, parse the operation type field, resource scope identifier and dependency strength enumeration value in the tag, and map them to the basic coordinate points in the multidimensional discrete feature space.

[0042] The predefined causal rule engine is invoked, and a standard causal template library containing business scenarios such as room status locking, payment deduction, and points freezing is loaded. Pattern matching is performed on the basic coordinate points to identify explicit dependency pairs with direct preceding relationships.

[0043] For nodes that do not match an explicit template, we analyze the intersection of the overlapping areas of their resource lock identifiers and the time window, and use the mutual information method to calculate the statistical correlation of different operation atoms at the resource competition level to quantify the strength of implicit logical constraints.

[0044] A structured dependency feature vector is constructed, explicit dependency identifiers are encoded into binary adjacency masks, and implicit logical constraint strengths are normalized into floating-point weights, forming a composite vector representation that integrates deterministic and probabilistic causal information.

[0045] Through the above processing method, the original transaction nodes generated in the previous step are transformed into a structured dependency feature vector set containing clear causal orientation and constraint weights, realizing the semantic dimensionality upgrade from isolated business operations to related causal networks, and providing accurate topological connection basis for subsequent dynamic graph construction.

[0046] S2.2: Read the explicit dependency identifier data from the structured dependency feature vector set, and use the directed graph adjacency matrix construction method to perform vertex mapping processing on the unique operation ID to generate an initial directed edge connection list that records direct preceding dependencies.

[0047] Read the explicit dependency identifier data from the structured dependency feature vector set, extract the list of tuples containing source operation ID and target operation ID, and use it as the basic edge set data for constructing the graph topology.

[0048] Perform hash mapping on the set of unique operation IDs to establish a bidirectional mapping dictionary from string-type operation IDs to integer-type vertex indices, ensuring contiguous storage and fast addressing of graph nodes in memory.

[0049] Initialize an N×N sparse adjacency matrix, where N is the number of unique operation IDs. The matrix elements are initially set to 0 and are used to record the direct prerequisite dependencies between nodes.

[0050] Iterate through each pair of source operation IDs and target operation IDs in the list of binary tuples, and obtain the corresponding row index i and column index j through the mapping dictionary.

[0051] Setting the element at position (i, j) in the adjacency matrix to 1 indicates the existence of a directed edge from the source node to the target node, meaning the source operation must be executed before the target operation.

[0052] The adjacency matrix is ​​checked for symmetry and self-loop detection. If a non-zero diagonal element or a bidirectional edge conflict is found, the direction of the edge is corrected according to the timestamp priority in the business semantic tag to eliminate logical contradictions.

[0053] The adjacency matrix is ​​serialized and encapsulated using the Sparse Matrix Compressed Storage Format (CSR) to generate an initial list of directed edges containing row offsets, column indices, and weights.

[0054] By using the above-described method for constructing a directed graph adjacency matrix, the structured dependency features from the previous step are transformed into an initial list of directed edges with a clear topological structure. This enables the digital modeling of explicit causal relationships between transaction operations, providing a definite graph structure foundation for subsequent implicit dependency derivation.

[0055] S2.3: Based on the initial list of directed edges and implicit logical constraint data, a transitive closure derivation mechanism is used to infer and calculate the indirect causal relationships between nodes, so as to generate a complete set of causal dependencies covering the logical sequence of the entire link.

[0056] Read the initial list of directed edges and implicit logical constraints to construct a node adjacency matrix to represent direct dependencies. Calculate the transitive closure of the adjacency matrix using the Warshall algorithm or a depth-first search strategy to deduce indirect causal paths between nodes. For any three operation nodes A, B, and C, if there exists a directed edge from A to B and a directed edge from B to C, a transitive causal dependency exists between A and C, and an implicit edge from A to C is added to the graph. Iteratively traverse all node combinations to complete dependency chains implicit but not explicitly declared by business rules, such as points freezing depending on order generation, and order generation depending on room status locking, thus deduce the full-chain relationship where points freezing indirectly depends on room status locking. Perform consistency checks on the generated transitive closure set to ensure that the newly added implicit edges do not violate the mutual exclusion constraints defined in the business semantic tags. Merge the direct dependency edges with the deduced indirect dependency edges to form a complete set of causal dependencies covering the entire lifecycle of a transaction. By using the transitive closure derivation mechanism, the local direct dependencies of the previous step are transformed into a complete set of global causal dependencies, enabling the accurate capture of implicit logical chains in complex business scenarios and providing a complete data foundation for subsequent graph pruning.

[0057] S2.4: Perform loop detection and redundant edge pruning on the complete set of causal dependencies, and eliminate invalid dependency paths by combining the room status version number consistency verification rules, so as to generate an acyclic and minimal dynamic causal time series graph infrastructure.

[0058] Perform loop detection and redundancy pruning on the complete set of causal dependencies, and eliminate invalid paths by combining room status version number consistency verification. Obtain the complete set of causal dependencies generated by S2.3, and map it into a directed graph adjacency list structure, where vertices are unique operation IDs and edges represent dependencies that must precede each other.

[0059] The directed graph is traversed using the Tarjan algorithm based on depth-first search (DFS), and each strongly connected component is computed. If a strongly connected component contains more than one node, a logical cycle is identified, and all edges within the cycle are marked as abnormal dependencies.

[0060] For abnormal dependencies in the tags, the timestamps and operation types in the business semantic tags are traced back to identify the race conditions or circular references that cause loops. Weak dependency edges are forcibly disconnected to eliminate loops and ensure the directed acyclic properties of the graph.

[0061] Transitive reduction is performed on the acyclic graph, and the reachability matrix is ​​calculated using the Warshall algorithm. If there is a direct edge from node A to node C and a path A→B→C also exists, then A→C is determined to be a redundant edge.

[0062] Remove all redundant edges, retain the minimum generating subgraph, reduce graph complexity, and improve the efficiency of subsequent traversals.

[0063] Read the room status version latch data packets associated with the remaining valid edges, and extract the room status version numbers and resource lock identifiers of the source node and the target node.

[0064] Construct version number consistency verification logic to determine whether the room status version after the source node operation is completed is strictly earlier than or equal to the expected room status version before the target node operation begins.

[0065] If the source node version number is greater than the expected version of the target node, or if there is a mutual exclusion conflict in the resource lock identifier, the dependency path is determined to be physically unreachable and marked as an invalid dependency path.

[0066] Remove all edges marked as invalid dependency paths and isolated nodes from the dynamic causal time-series graph infrastructure.

[0067] By employing the aforementioned loop elimination, redundancy pruning, and version consistency verification methods, the complete dependency set from the previous step is transformed into a loop-free, minimized, and physically consistent dynamic causal time sequence graph infrastructure, thereby ensuring the logical correctness and data consistency of the rollback path.

[0068] S2.5: Integrate the basic structure of the dynamic causal time series graph with real-time transaction evolution status data, and use in-memory graph database indexing technology to dynamically update the graph and encapsulate version snapshots to generate the final dynamic causal time series graph that supports concurrent queries and real-time evolution.

[0069] like Figure 2 As shown, step S3 involves: locating the set of terminal operation nodes in the dynamic causal time-series graph based on the hotel's rollback trigger event; identifying all affected reachable path nodes by traversing backwards along directed edges; performing irreversible and non-causal pruning on the reachable path nodes; and generating a simplified rollback candidate node sequence. Specifically, this includes:

[0070] S3.1: Obtain the set of terminal operation nodes generated by the rollback trigger event, and perform reverse breadth-first traversal processing on the set of terminal operation nodes based on the directed edge connection relationship of the dynamic causal time sequence graph to generate the original reachable path node set containing all logical upstream nodes.

[0071] Receive a set of terminal operation nodes identified by a rollback trigger event. This set contains terminal business atoms that have been successfully executed in the current transaction state, such as payment confirmation nodes or order generation nodes.

[0072] Based on the adjacency list storage structure of dynamic causal temporal graph, the vertex index of the terminal operation node in the graph is located, the reverse traversal queue is initialized, and the terminal node is enqueued.

[0073] Perform a reverse breadth-first traversal, retrieve the current node from the queue, query all incoming edges in the graph pointing to this node, and identify the direct predecessor dependent nodes.

[0074] The identified direct predecessor dependency nodes are marked as visited and added to the original reachable path node set, and then added to the traversal queue for processing at subsequent levels.

[0075] The above traversal process is executed recursively until the traversal queue is empty, ensuring that all logical upstream nodes connected to the terminal node indirectly or directly through directed edges are captured.

[0076] The original set of reachable path nodes is deduplicated, and duplicate node references caused by the convergence of multiple paths are removed to maintain the uniqueness of the set elements.

[0077] By using a reverse breadth-first traversal process, discrete terminal operation nodes are transformed into a set of original reachable path nodes containing complete logical dependency links, thereby achieving the expected technical effect of accurate full-domain coverage of the rollback impact range.

[0078] S3.2: Extract the business semantic tag data associated with each node in the original reachable path node set, and perform matching and verification processing on the business semantic tag data based on the predefined irreversible operation rule base to generate an irreversible node subset marked as irrevocable.

[0079] Extract the associated business semantic tag data of each node in the original reachable path node set to construct a tag feature vector sequence to be verified. Call a predefined irreversible operation rule library, which stores irreversible operation pattern matching rules based on regular expressions and state machine logic. Perform rule matching operations on each element in the tag feature vector sequence to identify operation nodes containing semantic identifiers such as 'third-party notification sent', 'physical voucher printed', or 'external payment channel cleared'. For successfully matched nodes, read their corresponding unique operation ID and timestamp information from the context snapshot to verify whether the operation has exceeded the system-set logical cancellation window period. If the operation is in an irreversible state or the window period has expired, mark the node as an irreversible node and record the specific irreversible reason code. Traverse all reachable path nodes, separating the nodes marked as irreversible from the original set to form an independent irreversible node subset. Through rule matching and state verification, the original reachable path nodes from the previous step are transformed into irreversible node subset data with clear reversibility attributes, achieving the expected technical effect of accurately removing invalid compensation operations in the rollback path.

[0080] For example, consider the reachable path set containing three nodes: 'lock room status', 'send confirmation SMS', and 'deduct points'. In the predefined rule base, 'send confirmation SMS' is defined as an irreversible operation, with a matching rule that the tag contains 'SMS_SENT'. When the system detects that the 'send confirmation SMS' node tag matches successfully and the difference between the current time and the sending time is greater than 0 seconds, it determines the operation to be irreversible. This node ID is added to the irreversible node subset. 'Lock room status' and 'deduct points' do not match any irreversible rules and are retained in the subsequent processing set. The final generated irreversible node subset only contains the 'send confirmation SMS' node ID, ensuring that subsequent rollback processes will not attempt to retract sent SMS messages, thus avoiding business logic conflicts.

[0081] S3.3: Obtain the difference data between the original reachable path node set and the irreversible node subset. Perform non-causal filtering on the difference data based on the timestamp window constraint and the logical dependency strength threshold to generate a valid causal node set after removing pseudo-dependent nodes that are sequential in time but have no logical dependency relationship.

[0082] Obtain the difference between the original set of reachable path nodes and the subset of irreversible nodes, and construct a pool of candidate rollback nodes to be filtered. This difference operation removes nodes marked as irreversible, ensuring that subsequent processing only targets atomic operations with logical reversibility.

[0083] For each candidate node in the difference set, the timestamp information from its context snapshot and the dependency strength field from its business semantic tag are extracted. Dependency strength quantifies the tightness of the logical constraints of the current node on its predecessor or successor nodes, and is a key indicator for distinguishing causal dependencies from simple temporal dependencies.

[0084] Calculate the time window deviation between the candidate node and the terminal trigger node. Set a dynamic time threshold; if the time deviation exceeds the preset reasonable business interaction range, determine that the node has no direct causal relationship with the current rollback event and mark it as a pseudo-dependent node.

[0085] A logical dependency strength threshold is introduced for secondary verification. A weighted scoring mechanism is used to evaluate the causal coupling degree between nodes, as shown in the following formula:

[0086]

[0087] in, The causal confidence score. For the intensity identifier value, For node timestamps, For the event timestamp, For the maximum allowed time window, and These are the weighting coefficients.

[0088] When the causal confidence score is lower than the preset minimum causal threshold of 0.8, the node is considered to have only a temporal relationship and no substantial logical dependency. Non-causal pruning is then performed to remove it from the candidate pool.

[0089] Traverse the remaining nodes and verify whether they still maintain connectivity with the terminal node. Remove successor nodes that are isolated due to upstream node pruning, ensuring that the remaining set of nodes is complete and closely related in the topology.

[0090] Through the dual filtering mechanism of time window constraints and logical dependency strength, the difference set data of the previous step is transformed into a set of effective causal nodes that eliminate false dependencies, thereby achieving precise slimming of the rollback path and significantly reducing the system load and data conflict risks caused by invalid rollback operations.

[0091] For example, in a hotel booking cancellation scenario, the terminal node is "payment failed," and the original reachable path includes "lock room," "freeze points," and "send SMS notification." The "send SMS" path, marked as irreversible, has already been removed in S3.2. For the "freeze points" node, its timestamp differs from the payment failure time by 2 seconds, indicating a high dependency strength. Its calculated S-value is 0.95, exceeding the threshold of 0.8, and is therefore retained. However, for an unrelated "query room rate history" node, although it occurs before payment, its dependency strength is 0, and the time deviation is large, resulting in an S-value of 0.1. It is judged as a pseudo-dependency and pruned. The final set of valid causal nodes only includes "lock room" and "freeze points," ensuring the accuracy and efficiency of the rollback operation.

[0092] S3.4: Read the resource lock identifier and room status version number information of each node in the valid causal node set, and perform conflict detection processing on the resource lock identifier and room status version number information based on the transaction consistency constraint model to generate a safe and rollbackable node list that eliminates the risk of concurrent conflicts.

[0093] Read the room status version number and resource lock identifier data of each node in the set of valid causal nodes, and use them as the initial input parameters for concurrent conflict detection.

[0094] Based on the transaction consistency constraint model, extract the real-time room status snapshot and active lock table status information from the memory of the current OLTP database.

[0095] Construct version number conflict verification logic to compare the consistency between the original room status version number recorded by the node and the latest version number in the database.

[0096] If the original version number is less than the current latest version number, it is determined that the room status was modified by other concurrent transactions during the transaction execution and marked as a version conflict node.

[0097] Parse the resource lock identifier and query the mutex lock holding status and waiting queue length of the corresponding room status ID in the global lock manager.

[0098] If an exclusive lock or shared lock not held by this transaction is detected in the target room state, and the lock holding time exceeds a preset threshold, it is marked as a lock contention conflict node.

[0099] A time window decay factor is introduced to perform secondary screening of lock contention conflict nodes, eliminating false alarm conflict records caused by instantaneous jitter.

[0100] By combining version conflict markers and lock contention conflict markers, a structured conflict detection dataset containing conflict type, conflict source transaction ID, and timestamp is generated.

[0101] Remove all nodes marked as conflicting from the set of valid causal nodes, and retain nodes without version bias and without lock contention.

[0102] Perform integrity checks on the remaining nodes to ensure that the removal operation does not break the necessary prerequisite dependencies of the causal chain.

[0103] By using the conflict detection and filtering methods described above, the valid causal nodes from the previous step are transformed into a list of safe and rollbackable nodes that exclude concurrent interference, thereby ensuring strong data consistency during the rollback execution process.

[0104] S3.5: Integrate the unique operation ID and context snapshot data from the list of safe rollbackable nodes, and perform ordered encapsulation processing on the unique operation ID and context snapshot data based on topology serialization to generate a simplified rollback candidate node sequence arranged strictly in reverse causal order.

[0105] Step S4: Execute a topological reverse sorting algorithm on the simplified rollback candidate node sequence to verify logical coherence and generate a rollback operation instruction stream to be verified. Specifically, this includes:

[0106] S4.1: Obtain each candidate node and its associated directed edges that must precede the relationship in the simplified rollback candidate node sequence. Perform reverse hierarchical partitioning on the candidate nodes based on the graph theory topological sorting algorithm to generate a preliminary reverse-order node list containing hierarchical index information.

[0107] Receive the simplified rollback candidate node sequence output by S3.5, and extract the unique operation ID and directed edge data associated with each candidate node in the dynamic causal time-series graph. Based on the topological sorting algorithm or depth-first search strategy in graph theory, construct a node in-degree statistics table and initialize the in-degree counter of all candidate nodes to zero. Traverse all directed edges in the candidate node set. For each edge from node A to node B, increment the in-degree value of node B to establish a complete dependency counting mapping relationship. Select nodes with an in-degree value of zero as the root node set of the current level. These nodes have no predecessor dependencies in causal logic or their predecessor dependencies have been pruned, and mark them as the first inversion level. Add the first inversion level nodes to the end of the preliminary inversion node list, and temporarily remove these nodes and all outgoing edges from the graph, while simultaneously updating the in-degree values ​​of the remaining nodes. Repeat the level filtering and removal operation, sequentially identifying the second inversion level, the third inversion level, until all nodes have been processed, ensuring that the dependency operations executed later are executed before the basic operations executed earlier. Each node in the list is assigned a hierarchical index identifier to record its relative position in the topological reverse order, forming a preliminary reverse-order node list containing hierarchical information. Through reverse hierarchical partitioning, the flattened candidate nodes from the previous step are transformed into a structured list with a strict causal order, achieving logical pre-sorting of the rollback execution order and providing an ordered data foundation for subsequent dependency conflict verification.

[0108] S4.2: Extract the original transaction context snapshot of each node in the preliminary reverse order node list, parse the set of preceding dependent operation IDs recorded therein, and construct an explicit causal dependency mapping matrix between nodes based on the set comparison logic to establish strict sequential execution constraints between nodes.

[0109] Obtain the original transaction context snapshot data of each node in the preliminary reverse node list. This snapshot contains the set of preceding dependent operation IDs and business semantic tags at the time of operation execution.

[0110] The extracted set of pre-dependent operation IDs is parsed to identify the logical relationship between each element in the set and the current node in the causal time sequence graph, and to distinguish between strong and weak dependency attributes.

[0111] Construct an M×M dimensional explicit causal dependency mapping matrix based on a unique operation ID, where M is the total number of nodes in the simplified rollback candidate node sequence, the matrix row index represents the dependent node, and the column index represents the dependent node.

[0112] Traverse the initial reversed node list. For any node a and its predecessor dependent node b, if node b exists in the set of predecessor dependent operation IDs of node a, mark the dependency weight value as 1 at the a-th row and b-th column of the mapping matrix; otherwise, mark it as 0.

[0113] The mapping matrix is ​​subjected to transitive closure verification using Boolean algebra operation rules to detect whether there are indirect dependency paths that are not covered by direct edges, ensuring that the matrix fully reflects the global causal constraints between nodes.

[0114] By removing rows and columns with all zeros through matrix sparsity, the matrix dimension is compressed to optimize the computational efficiency of subsequent traversal and verification, thereby generating a standardized explicit causal dependency mapping matrix.

[0115] The above processing method transforms the results of the previous step into structured explicit causal dependency mapping matrix data, digitally establishing strict sequential execution constraints between nodes, and providing an accurate data foundation for subsequent logical coherence verification.

[0116] S4.3: Use the causal dependency mapping matrix to traverse and verify the initial reverse node list, detect whether there are any pre-dependency conflicts that violate the "must precede" relationship, and dynamically rearrange the node order based on the conflict detection results to generate a logically coherent corrected reverse node sequence.

[0117] The process begins by reading the initial reverse-order node list and the causal dependency mapping matrix to construct a conflict detection queue based on topological hierarchy. The current node in the queue is traversed, and its set of preceding dependency operation IDs is extracted. The corresponding preceding node's index position in the current list is then retrieved from the mapping matrix. If any preceding node's index value is greater than the current node's index value, a reverse-order conflict is identified, and the node pair is marked as an object to be rearranged. The conflict frequency and dependency depth of all objects to be rearranged are calculated, generating a conflict weight vector. A local bubble sort is then performed on the initial reverse-order node list based on the conflict weight vector, moving preceding nodes with high dependency depths to the front of the list and following nodes with low dependency depths to the back. After each node position swap, the causal coherence score of the affected region is recalculated until all node index positions satisfy the preceding dependency constraints. Through this dynamic rearrangement process, the initial sequence with logical breaks is transformed into a corrected reverse-order node sequence that strictly conforms to the causal reverse order relationship, achieving logical self-consistency and execution safety for the rollback operation.

[0118] S4.4: Read the business semantic tags and unique operation IDs of each node in the reverse order node sequence, combine them with the preset rollback operation template library to perform instruction mapping and conversion processing, and instantiate the abstract node into an operation primitive carrying a specific resource lock identifier and room status version number to form a standardized rollback operation instruction unit.

[0119] Read the original transaction context snapshots of each node in the corrected reverse node sequence, extract the unique operation ID and business semantic tag data, and use them as the input source for instruction mapping transformation.

[0120] Retrieve the preset rollback operation template library, match the corresponding reverse operation primitive template based on the operation type field in the business semantic tag, and determine the specific execution logic for room status unlocking, points unfreezing, or coupon release.

[0121] Parse the resource lock identifier information in the context snapshot, and replace the generic resource placeholder in the template with the specific room number, inventory batch ID or account index key to achieve instantiation and binding of the operation object.

[0122] Extract the room status version number and the state value before the change from the snapshot, construct a version rollback parameter set, and ensure that the rollback operation strictly points to the data state before the transaction occurred to prevent overwriting of the new version written concurrently.

[0123] In conjunction with the atomicity constraint of the operation, a unique rollback signature is generated for each instantiated operation primitive. This signature is derived from the original operation ID, timestamp, and resource hash value through cryptographic calculation and is used for subsequent consistency verification.

[0124] The instantiated operation primitives, resource lock identifiers, version rollback parameters, and rollback signatures are encapsulated into standardized rollback operation instruction units, forming a unified and semantically clear execution object.

[0125] Through the above instruction mapping and transformation process, abstract logical nodes are transformed into standardized rollback operation instruction units carrying specific resource identifiers and version information, realizing semantic alignment from the causal graph logic layer to the database execution layer, and ensuring the accuracy and traceability of rollback operations.

[0126] S4.5: Integrate all standardized rollback operation instruction units, serialize and encapsulate them according to the order of the corrected reverse node sequence, add transaction boundary markers and integrity check codes to generate a rollback operation instruction stream to be verified that is strictly arranged in reverse causal order and has atomic execution characteristics.

[0127] Step S5: Input the rollback operation command stream to be verified into the lightweight execution sandbox environment, and output a rollback feasibility assessment report containing the consistency verification status. Specifically, this includes:

[0128] S5.1: Based on the operation atom identifier in the rollback operation instruction stream to be verified, extract the corresponding historical room status version number snapshot data and the baseline value of the points account balance before freezing. Use memory isolation mapping technology to construct a sandbox state image space independent of the production environment to generate a sandbox simulation context environment with initial consistency.

[0129] The system receives the instruction stream for the rollback operation to be verified, parses the set of operation atomic identifiers contained within, and uses this set as the index key for initializing the sandbox environment. Based on the operation atomic identifiers, it performs an exact match query in the historical state version library to extract the snapshot of the room status data version number before each operation execution and the baseline value of the user's points account balance before freezing. Using memory isolation mapping technology, an independent sandbox simulation context area is created in the application layer heap space. This area is physically isolated from the production database to ensure that the simulation operation does not pollute the live network data. The extracted historical room status version number snapshots are deeply cloned into the sandbox memory object to construct a virtual room status state tree, preserving the complete version chain structure information. The baseline value of the points account balance before freezing is loaded into the temporary account register in the sandbox and set as the initial available balance state, establishing the simulation baseline for fund changes. Read-write mutex locks are applied to the virtual room status state tree and the temporary account register in the sandbox to prevent concurrent threads from modifying the internal state of the sandbox during simulation, ensuring the atomicity and consistency of the simulation environment. Through the above processing, the instruction stream from the previous step is transformed into a sandbox simulation context environment with initial consistency, realizing the pre-rehearsal preparation of the rollback operation in a risk-free isolated space.

[0130] S5.2: Perform reverse release logic deduction on the room status resource lock identifier in the sandbox simulation context environment, and simulate the rollback operation of the room status version chain in combination with the dependency constraints in the business semantic tags, so as to generate a sandbox pre-simulation state dataset containing the virtual room status change trajectory.

[0131] The resource lock identifier and target room status version number carried by each operation unit in the rollback operation instruction stream to be verified are parsed, and the current lock status bitmap and version history list head pointer of the corresponding room status resource are extracted from the sandbox state image space.

[0132] Based on the topology in reverse order, the lock state of the first rollback node is reversed, the mutex lock flag is changed from "occupied" to "free", and the timestamp of the state change and the operation source ID are recorded to the temporary state register.

[0133] Read the business semantic tags associated with the node to identify whether it is a "strong dependency" or a "weak dependency". If it is a strong dependency, forcefully verify whether the preceding node has completed the rollback simulation in the sandbox to ensure that the integrity of the causal chain is not destroyed.

[0134] Based on the historical version number stored in the original transaction context snapshot, a virtual version node is constructed in the sandbox memory, and the parent pointer of the node is pointed to the previous valid version before the rollback, forming a temporary version backtracking branch.

[0135] The differential merging method is used to calculate the incremental difference between the current room status data and the historical version data. Only the fields that have changed (such as available quantity and lock status) are applied to the sandbox copy, while maintaining the reference consistency of unchanged fields to save memory overhead.

[0136] Update the global room status index table in the sandbox, map the affected room status IDs to the newly generated virtual version node, and mark this simulation operation as "pending confirmation" to prevent misreading by concurrent queries.

[0137] Traverse subsequent rollback nodes, repeatedly execute lock release, dependency verification and version rollback operations. Each operation is based on the sandbox state after the previous simulation to accumulate and extrapolate, forming a continuous state evolution sequence.

[0138] By using the above chain-like deduction process, the abstract rollback instruction is transformed into a specific memory state change trajectory, generating a sandbox pre-simulation state dataset containing a complete virtual room state change path, thus achieving accurate simulation and conflict prediction of the rollback process without side effects.

[0139] S5.3: Based on the virtual room status change trajectory in the sandbox pre-simulation state dataset, read the real-time access pointer of the current concurrent query request, and use a multi-version concurrency control conflict detection method to compare the room status version number after simulated rollback in the sandbox with the external concurrent read version number to generate a set of concurrent conflict markers that identify potential read-write conflicts.

[0140] Obtain the virtual room status change trajectory from the sandbox pre-running state dataset. This trajectory records the intermediate version number sequence and resource lock status changes generated in the simulation environment during the rollback operation. Parse the real-time access pointer carried by the current concurrent query request, extracting the target room status resource ID and the expected data version timestamp, as a benchmark for conflict detection. Employ the Multi-Version Concurrency Control (MVCC) conflict detection method, traversing each version node in the virtual room status change trajectory, and calculating the logical distance between the target version number after the simulated rollback and the concurrent read version number. Use the following formula to determine version compatibility:

[0141]

[0142] in, The conflict coefficient, This refers to the room state version number after the sandbox simulation rollback. For external concurrent requests, the current read version number, The system sets the maximum version tolerance window threshold. When the conflict coefficient exceeds the preset threshold of 1.0, the concurrent request is marked as having a read-write conflict risk. High-conflict-coefficient nodes are mapped to specific resource lock identifiers, generating structured data items containing conflict type, involved resource IDs, and conflict time windows. All structured data items are aggregated to form a set of concurrent conflict markers identifying potential read-write conflicts. Through the above MVCC version comparison and conflict coefficient calculation process, the sandbox pre-run state of the previous step is transformed into a quantified set of concurrent conflict markers, achieving accurate identification and isolation of potential data contention risks during rollback, ensuring that subsequent rollback execution does not disrupt the consistent view of the database.

[0143] S5.4: Perform boundary out-of-bounds verification processing on the abnormal items in the concurrent conflict marker set, integrate the points account balance recovery calculation results in the sandbox pre-run state dataset with the preset account threshold rules, and generate intermediate results of resource integrity verification containing numerical legality judgment conclusions.

[0144] Obtain anomaly data from the concurrent conflict marker set, parse the resource type identifiers and conflict operation IDs involved, and filter out the key conflict nodes involving changes in points account balances as the core objects for boundary verification.

[0145] Extract the balance values ​​of the points account after simulated rollback of the corresponding key conflict nodes in the sandbox pre-running state dataset, and read the minimum available balance lower limit and maximum credit limit upper limit parameters defined in the preset account threshold rule base.

[0146] Construct a balance validity verification logic, compare the simulated rollback balance value with the minimum available balance limit, and determine whether there is an account overdraft risk due to excessive rollback.

[0147] The balance value after the simulated rollback is compared with the maximum credit limit to determine whether there is an abnormal credit limit overflow caused by concurrent writes.

[0148] For nodes with numerical out-of-bounds risk, calculate the absolute value of the deviation and generate specific out-of-bounds type labels, including negative overdraft labels or positive overflow labels.

[0149] Integrate all verified node balance data with abnormal node data marked with out-of-bounds type to form a structured verification record containing numerical status, out-of-bounds identifier, and deviation amount.

[0150] Through the above boundary out-of-bounds verification process, the concurrent conflict flags from the previous step are transformed into intermediate results of resource integrity verification containing clear conclusions on the legality of numerical values. This enables refined control over the consistency of points account data during the rollback process and effectively prevents asset data errors caused by rollback logic defects.

[0151] S5.5: Based on the comprehensive logical state of the intermediate results of the resource integrity verification and the set of concurrent conflict markers, the state aggregation engine generates a rollback feasibility assessment report containing clear consistency verification pass or fail flags, which serves as the sole basis for subsequent atomic transaction package encapsulation decisions.

[0152] Step S6: Determine whether the consistency verification status in the rollback feasibility assessment report has passed. If it has, encapsulate the rollback operation instruction stream to be verified into an atomic transaction package. If it has not passed, terminate execution and generate an exception suspension record. Specifically, this includes:

[0153] S6.1: Parse the consistency check status field in the rollback feasibility assessment report to extract the Boolean check result identifier and the associated conflict detection details list, which will serve as the input conditions for subsequent logical branch judgments.

[0154] S6.2: Based on the extracted Boolean verification result, the execution condition routing determination is performed. If the identifier is true, the transaction encapsulation process is activated; if the identifier is false, the exception handling process is activated, so as to generate a definite execution path control signal.

[0155] Receive the Boolean check result identifier and associated conflict detection details list obtained from S6.1 parsing, as the basic input data for execution path determination.

[0156] The Boolean verification result identifier is read by logical status bits and mapped to internal system conditional routing control signals to establish the uniqueness of subsequent processing branches.

[0157] If the verification result is true, it indicates that no room status version conflict or resource integrity abnormality was detected in the sandbox simulation environment, and the system activates the transaction encapsulation process channel.

[0158] In the transaction encapsulation process channel, the atomic transaction package construction context is initialized, the data reference permissions of the rollback operation instruction stream to be verified are locked, and metadata binding is prepared.

[0159] If the verification result is marked as false, it indicates that there is a risk of concurrent read / write conflict or value out of bounds. The system will immediately block the transaction commit channel and activate the exception handling process channel.

[0160] In the exception handling process channel, extract the error type code, the memory snapshot pointer at the time of the conflict, and the set of affected node IDs from the conflict detection details list.

[0161] The extracted abnormal data is serialized and encoded to generate a structured abnormal suspension record object containing fault scene information, which is used to terminate the current rollback execution and retain audit clues.

[0162] By using a conditional routing decision mechanism, the verification result of the previous step is transformed into a definite execution path control signal, which enables atomic encapsulation preparation when the rollback is successful or abnormal suspension when it fails, thus ensuring the determinism and security of system state transitions.

[0163] S6.3: The activated transaction encapsulation process is used to structurally reorganize the rollback operation instruction flow to be verified, and the unique operation ID in the original transaction node set is combined with the rollback signature generated by the cryptographic hash algorithm to bind metadata, so as to construct an atomic transaction package carrying complete audit traceability information.

[0164] Receive the rollback operation instruction stream that has passed the sandbox verification, parse the operation primitive sequence, target resource identifier and expected state change value contained therein, and construct a temporary transaction buffer object in memory.

[0165] Traverse the original transaction node set, retrieve the corresponding unique operation ID based on the operation atom identifier in the instruction stream, and establish a reverse mapping index table between rollback operations and forward business operations.

[0166] The encrypted hash algorithm module is invoked, and SHA-256 is selected as the digest generation function. The serialized byte array of the rollback operation instruction stream to be verified, the current system timestamp, and the transaction session ID are concatenated into the original input data string.

[0167] A hash operation is performed on the original input data string to generate a fixed-length hexadecimal string as a rollback signature. This signature is used to ensure the integrity and tamper-proof capability of the rollback instruction during transmission and storage.

[0168] Create an atomic transaction package data structure, use the unique operation ID set as the traceability key, fill the rollback signature field into the integrity verification area, and encapsulate the rollback operation instruction stream to be verified into the payload data area.

[0169] The transaction type flag "ROLLBACK_COMMIT" and the global transaction ID are injected into the header of the atomic transaction packet, and a CRC32 checksum is appended to the tail to form a standardized binary data block with self-describing capabilities.

[0170] By using structured reorganization and metadata binding, the verification results of the previous step are transformed into atomic transaction packages carrying complete audit traceability information, thereby achieving eventual consistency encapsulation and non-repudiation guarantee before rollback execution.

[0171] S6.4: The response activation exception handling process serializes and encodes the concurrent conflict records in the conflict detection details list, generating an exception suspension record containing error type codes and on-site snapshot data, so as to terminate the current rollback execution and preserve the fault scene for manual intervention and analysis.

[0172] Receive the exception handling process signal activated by S6.2 and the concurrent conflict flag set generated by S5.3. The set contains conflict operation ID, conflict type enumeration value and real-time data snapshot pointer.

[0173] The concurrent conflict marker set is traversed and parsed to extract the original operation atom identifier and the current database version number from each conflict item, and a conflict feature vector is constructed.

[0174] A predefined error coding mapping table is used to convert conflict types (such as version rollback out of bounds, resource lock contention failure, negative points balance) into standardized hexadecimal error type codes.

[0175] The main status field is generated based on the error type code, which is used to quickly index the root cause category of the failure and ensure that the subsequent automated operation and maintenance system can read it by machine.

[0176] The memory snapshot serialization engine is invoked to read a partial view of the room status version chain and associated resource lock holder information in the sandbox environment at the time of the conflict.

[0177] The snapshot data is compactly encoded using the Protocol Buffers binary protocol, preserving the state differences and timestamp sequences before and after the operation, to form a field restoration data package.

[0178] The standardized error type codes and the on-site reconstruction data package are structurally assembled, and a global transaction tracking ID is added as a foreign key index.

[0179] Perform CRC32 integrity check calculation on the assembled data structure, generate a checksum field and append it to the end of the record to prevent silent errors in the storage medium.

[0180] The data block containing the error code, snapshot, and checksum is persistently written to the exception pending log table on the non-volatile storage medium.

[0181] Set the status flag of this record to "awaiting manual intervention" and trigger an asynchronous notification mechanism to send an alarm event to the operation and maintenance monitoring platform.

[0182] Through the above serialization encoding and persistence processing, the transient concurrent conflict situation is transformed into a traceable and reproducible structured exception suspension record, realizing the complete preservation and isolation of the fault situation, avoiding the pollution of production data by erroneous rollback operations, and providing a solid basis for subsequent manual auditing and system optimization.

[0183] S6.5: Output the decision execution result object based on the final form of the generated atomic transaction package or abnormal suspension record to complete the state mapping closed loop from simulation verification conclusion to actual execution action.

[0184] like Figure 3As shown, step S7 involves submitting the atomic transaction package to the OLTP database engine for atomic write operations, outputting a transaction rollback completion log, and performing hotel rollback recovery based on the transaction rollback completion log. Specifically, this includes:

[0185] S7.1: Perform atomic transaction package encapsulation on each operation unit in the rollback operation instruction stream to be verified, construct a standardized transaction commit object carrying complete causal traceability information based on the original operation ID and rollback signature, and generate an atomic transaction package with unique identification as the basic input for subsequent execution.

[0186] S7.2: Submit the atomic transaction package to the transaction scheduler of the OLTP database engine, and use the multi-version concurrency control mechanism to perform atomic write operations on the room status data version chain. By comparing the previous version number, the target data row is locked and incremental changes are applied to generate an updated room status data version chain containing the latest status flag.

[0187] The system receives the atomic transaction packet generated by S7.1 and parses the instruction stream of the rollback operation to be executed and the associated set of original operation IDs contained therein. It extracts the target room status resource identifier and the previous version number information to be rolled back for each operation unit in the instruction stream. A data row locking request based on a multi-version concurrency control mechanism is constructed, using the target room status resource identifier as the primary key index and injecting an optimistic lock condition into the previous version number verification field. The OLTP database engine's transaction scheduler receives the locking request and scans the current version chain head node of the room status data table. It compares whether the latest version number held by the current data row is consistent with the previous version number declared in the atomic transaction packet. If the version numbers match, it determines that there is no concurrent write conflict and immediately applies an exclusive write lock to the data row. If the version numbers do not match, it determines that there is a concurrent update, throws an optimistic lock failure exception, and terminates the current transaction branch. After acquiring the exclusive write lock, it reads the incremental change data carried in the atomic transaction packet, which contains the room status attribute values ​​and status flags to be restored. An atomic write operation is performed to update the content of the room status data row to the status specified by the incremental change data. Simultaneously, a new version number is generated, which is incremented based on the current maximum version number to ensure the monotonicity of the version chain. The newly generated version number is written to the version control field of the data row, completing the atomic switch of the data state. The exclusive write lock on this data row is released, allowing subsequent read and write requests to access the latest data version. The remaining operation units in the atomic transaction package are traversed, and the above version comparison, locking, writing, and unlocking process is repeated until all instructions have been executed. Through optimistic locking comparison and atomic write processing under the multi-version concurrency control mechanism, the atomic transaction package of the previous step is transformed into an updated room status data version chain containing the latest status flag, achieving the expected technical effect of strong consistency and zero loss of room status data rollback in high-concurrency scenarios.

[0188] S7.3: Based on the successful submission signal of the updated room status data version chain, the associated resource lock management module is triggered to execute the lock release logic, parse the resource lock identifier list carried in the atomic transaction packet and clear the corresponding mutex lock occupancy status one by one, so as to generate a resource lock release confirmation record in an idle and available state.

[0189] The system receives a successful execution signal from an atomic transaction packet and a list of associated resource lock identifiers. It parses the metadata structure of each lock object in the list, extracting key index fields including lock type, scope ID, and holder session ID. Based on the extracted index fields, it constructs a memory-level mutex lock mapping table, using a hash addressing algorithm to quickly locate currently occupied lock records. It verifies the consistency between the lock holder and the current rollback transaction session to prevent accidental releases. For verified lock records, it performs a state machine transition operation, changing the lock state from "exclusive lock" to "pending release," and records the timestamp of the state change as an audit basis. It iterates through the set of locks to be released, calling underlying concurrency control primitives to execute atomic unlock instructions, clearing database row-level or table-level lock markers, and updating the active transaction list in multi-version concurrency control to remove the current rollback transaction ID. It monitors the resource contention queue after lock release; if there are blocked transactions waiting for the resource, it wakes up the highest-priority waiting thread according to a fair scheduling policy, allowing it to regain the lock's usage right. It generates a structured confirmation record containing a list of released lock IDs, release time, and the number of woken-up transactions, marking all associated resources as idle and available. Through the above chained processing, the successful submission signal of the previous step is transformed into a resource lock release confirmation record, realizing the instant reclamation of room-state resources and zero deadlock risk in high-concurrency scenarios, and ensuring that the system throughput does not decrease due to long transaction rollback.

[0190] S7.4: Based on the execution result of the atomic transaction package and the resource lock release confirmation record, aggregate the original operation ID, rollback signature and timestamp information to construct a structured audit entry. Use append write mode to persistently store the structured audit entry in the transaction rollback completion log table to generate a final transaction rollback completion log with full-link audit traceability capabilities. OLTP completes the hotel's rollback recovery based on the transaction rollback completion log.

[0191] The system receives successful execution signals from atomic transaction packets and resource lock release confirmation records, extracting the original operation ID set, rollback signature hash value, and system timestamp accurate to milliseconds. It then performs structured mapping on the extracted multi-source heterogeneous audit metadata, using the original operation ID as the primary index key, the rollback signature as the integrity verification field, and the timestamp as the time-series sorting basis, according to a predefined audit log schema, to construct standardized key-value pair data units. Employing an append-only strategy, the system serializes the generated standardized key-value pair data units into binary streams in JSON or Protocol Buffers format, ensuring the atomicity and immutability of log write operations and avoiding performance degradation caused by random I / O. A dedicated audit log write channel is obtained through the database connection pool, and the serialized binary streams are batch-inserted into the transaction rollback completion log table. The underlying database WAL (Write-Ahead Log) mechanism ensures data persistence and prevents audit record loss due to system downtime. Based on the successful insertion feedback status, the system queries a historical causal time-series snapshot associated with the original operation ID, supplementing records with performance metrics such as the number of nodes involved in the rollback, the length of the pruning path, and the sandbox verification time. This forms a full-link audit entry encompassing both business semantics and system performance dimensions. Through the aforementioned structured aggregation and appended persistence processing, the execution result of the previous step is transformed into a final transaction rollback completion log with legal validity and technical traceability capabilities. This enables the hotel reservation system to achieve full lifecycle auditability of room status changes and rapid fault root cause localization in high-concurrency scenarios.

[0192] For those skilled in the art, various other corresponding changes and modifications can be made based on the technical solutions and concepts described above, and all such changes and modifications should fall within the protection scope of the claims of this invention.

[0193] Unless otherwise defined, the technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this application pertains. The terms “first,” “second,” “third,” and similar terms used in this patent application specification and claims do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an” or “a” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising” or “including” and similar terms mean that the elements or objects preceding “comprising” or “including” encompass the elements or objects listed following “comprising” or “including” and their equivalents, and do not exclude other elements or objects. The “multiple” mentioned in the embodiments of this application refers to two or more. A and / or B indicate three possibilities: A; B; and A and B.

[0194] The above description is merely an exemplary embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and such modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A hotel room status booking and rollback recovery method based on OLTP architecture, characterized in that, Specifically, it includes: S1: Obtain multiple business operation atoms and their context snapshots during the initialization phase of the hotel booking transaction, and generate an original transaction node set with a unique operation ID based on the business operation atoms and context snapshots; S2: Perform causal dependency analysis on each node in the original transaction node set, identify explicit and implicit logical sequence constraints, and construct a dynamic causal time sequence graph; S3: Based on the hotel's rollback trigger event, locate the set of terminal operation nodes in the dynamic causal time sequence graph, traverse in reverse along the directed edges to identify all affected reachable path nodes, perform irreversible and non-causal pruning on the reachable path nodes, and generate a simplified rollback candidate node sequence. S4: Execute the topological reverse sorting algorithm on the simplified rollback candidate node sequence, verify the logical coherence, and generate the rollback operation instruction stream to be verified. S5: Input the rollback operation instruction stream to be verified into the lightweight execution sandbox environment, and output a rollback feasibility assessment report containing the consistency verification status; S6: Determine whether the consistency verification status in the rollback feasibility assessment report has passed. If it has passed, encapsulate the rollback operation instruction flow to be verified into an atomic transaction package. If it has failed, terminate the execution and generate an abnormal suspension record. S7: Submit the atomic transaction package to the OLTP database engine to perform atomic write operations, output the transaction rollback completion log, and complete the hotel rollback recovery based on the transaction rollback completion log.

2. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 1, characterized in that, The context snapshot includes the room status version number, resource lock identifier, and business semantic tag.

3. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 1, characterized in that, The atomic transaction packet carries the original operation ID and the rollback signature.

4. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 2, characterized in that, S1 specifically includes: Obtain multiple atomic data streams of business operations during the initialization phase of a hotel booking transaction, and perform hash encoding on each atomic business operation to generate a set of globally unique operation IDs; Read the real-time room status resource status data associated with the unique operation ID set, extract the version number information and mutex lock identifier information of the current room status data, and generate a room status version latch data packet containing precise status markers as the basic component of the context snapshot; The business rule metadata in the atomic data stream of the business operation is parsed, and the operation type, dependency strength and reversibility features are extracted based on the predefined semantic tag classification module to generate a set of business semantic tags. The unique operation ID set, room status version latch data packet and business semantic tag set are integrated, and the operation identifier, version status and semantic features are bound and mapped using a structured data encapsulation protocol to generate the original transaction context snapshot object; Iterate through all generated original transaction context snapshot objects, and combine and encapsulate the business operation atoms with their corresponding original transaction context snapshot objects to generate a set of original transaction nodes.

5. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 4, characterized in that, The hash encoding process for each business operation atom is based on the snowflake algorithm.

6. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 4, characterized in that, The process of combining and encapsulating the business operation atom with its corresponding original transaction context snapshot object is specifically based on memory object instantiation technology.

7. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 4, characterized in that, S4 specifically includes: To obtain each candidate node and its associated directed edges in the simplified rollback candidate node sequence, perform reverse hierarchical partitioning on the candidate nodes to generate a preliminary reverse-order node list containing hierarchical index information. Extract the original transaction context snapshot of each node in the preliminary reverse node list, parse the set of preceding dependent operation IDs recorded therein, and construct an explicit causal dependency mapping matrix between nodes based on the set comparison logic; The corrected sequence of reverse nodes is obtained by traversing, verifying, and rearranging the initial reverse node list using a causal dependency mapping matrix. Read the business semantic tags and unique operation IDs of each node in the corrected reverse node sequence, perform instruction mapping and conversion processing, and instantiate the abstract node into an operation primitive to form a standardized rollback operation instruction unit; Integrate all standardized rollback operation instruction units, serialize and encapsulate them according to the order of the corrected reverse node sequence, add transaction boundary markers and integrity check codes to generate a rollback operation instruction stream to be verified.

8. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 7, characterized in that, The reverse hierarchical partitioning of candidate nodes is performed based on a graph theory topological sorting algorithm.

9. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 7, characterized in that, The step of traversing, verifying, and rearranging the initial reversed node list using a causal dependency mapping matrix to obtain the corrected reversed node sequence includes: The initial reverse node list is traversed and verified using a causal dependency mapping matrix to detect any pre-dependency conflicts that violate the "must precede" relation. Based on the conflict detection results, the node order is dynamically rearranged to generate a corrected reverse node sequence.

10. The hotel room status booking and rollback recovery method based on OLTP architecture according to claim 7, characterized in that, The operation primitive carries a specific resource lock identifier and a room status version number.

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