A room state event playback method based on event segment consistency checking

Abstract

The application discloses a room state event playback method based on event segment consistency checking, aiming at solving the problem of room state data time sequence confusion and logic conflict caused by asynchronous triggering and multi-channel collection, and the core scheme comprises the following steps: aggregating event streams and standardizing processing through hotel identification, room type identification and date dimension to generate an event segment to be checked; constructing original segment state fingerprints by using difference abstracts and state snapshots to realize quantitative detection and deviation measurement of event stream consistency; if deviation is found, correcting through a cause-effect dependency graph and a reordering priority weight vector to eliminate time sequence logic contradiction, finally generating a benchmark event segment that passes the checking and rebuilding a room state cache; in addition, through playback empirical package feedback and cluster analysis, the consistency checking rules are dynamically updated to realize adaptive iteration of the checking strategy, and the beneficial effects are that the room state data consistency is improved, complex conflict scenes are automatically processed, and the reliability and real-time performance of the hotel room state data are significantly enhanced.

Description

Technical Field

[0001] This invention relates to the field of event playback consistency verification technology, and in particular to a room state event playback method based on event segment consistency checking. Background Technology

[0002] Current hotel room status management systems widely employ multi-source event-driven state playback and consistency verification mechanisms to ensure accurate restoration and verification of room status data. In practical applications, room status events originate from multiple links, including polling refresh, asynchronous callbacks, and manual corrections. Furthermore, there are significant asynchronous and temporal uncertainties between the collection, storage, and playback stages of various events. To achieve data consistency control in a multi-source asynchronous environment, mainstream industry technologies typically employ streaming verification methods based on event reordering and field change summaries. Through event aggregation, time-series sliding windows, and conflict detection, these methods aim to minimize data inconsistencies caused by concurrency. In the event segment playback module, full cache reconstruction and action trajectory collection are typically used to reproduce and audit the evolution of room status. These methods help improve data consistency and reproducibility and have become a fundamental capability of hotel and guesthouse booking platforms, OTAs, and PMS systems.

[0003] Existing consistency verification technologies for room status events can be broadly categorized into two types. One type is characterized by field hash comparison and event timestamp sorting, focusing on pre-setting rules and static conflict determination. The other type, after event replay, employs log auditing and manual intervention to periodically correct known verification vulnerabilities. In recent years, with the increase in the number of event sources and the growing complexity of business scenarios, some solutions have attempted to introduce state differential snapshots, finite state machine modeling, or domain knowledge-based credibility weights to achieve more flexible control over the verification and replay process.

[0004] Typical event replay consistency verification systems in this field are suitable for applications such as automatic segmentation, asynchronous replay, and consistency checks of multi-source event streams related to room status. These systems typically involve a process of aggregation, structuring, replay, and difference verification of multi-source asynchronous streams, including polling, real-time notifications, and manual data entry. The systems generally rely on a static rule base to determine event priorities and perform partial deviation analysis after replay. While these solutions can cover general data restoration and conflict handling needs, in situations with a surge in abnormal patterns and complex event source interactions, the optimization of verification strategies often lags behind changes in the actual environment, limiting the flexibility and applicability of consistency control.

[0005] Existing technologies have significant shortcomings in the following aspects: There is a lack of a true closed-loop feedback path between the event segment consistency verification module and the playback results. Deviations that actually occur during playback can only be passively reported, failing to drive the strategy to evolve in reverse by automatically updating the verification rules.

[0006] Causal conflicts in multi-source asynchronous events mainly rely on manually set static rule pools, which are difficult to adapt to problems such as high concurrency, low confidence, and deterioration of source context in dynamic scenarios; they are slow to respond to newly emerging high-frequency failure modes, and the evolution of strategies is lagging behind, ultimately affecting the accuracy of room status data restoration and audit verifiability.

[0007] Traditional solutions either attempt to construct a global event causal graph, resulting in huge temporal and spatial complexity and system rendering costs, or rely too much on manual intervention and periodic data entry, with rigid verification logic and heavy maintenance burden, making it difficult to keep up with the evolution of system drift and business asynchronicity.

[0008] The lack of efficient quantitative basis for state differences means that the event verification results cannot be accurately correlated with the actual playback process data, resulting in unsatisfactory traceability, precision, and capability of the consistency judgment process. Summary of the Invention

[0009] This application provides a room status event playback method based on event segment consistency check, which aims to solve one of the problems or issues of the prior art mentioned in the background.

[0010] This application provides a method for replaying room-state events based on event segment consistency checks, specifically including: Acquire multi-source asynchronous room status event streams and generate event segments to be verified, including source type, collection batch number, link timestamp, and a summary of differences from the previous confirmed status; Perform a structure extraction operation on the event segment to be verified, parse out the initial state snapshot, the final state snapshot and the intermediate difference summary sequence, and construct the original segment state fingerprint; The original segment state fingerprint is subjected to pre-playback processing to reconstruct the local room state cache and output the pre-playback state trajectory. Then, a playback deviation metric is generated by comparing the pre-playback state trajectory with the original segment state fingerprint. For the event segments to be verified whose playback deviation metric has been determined to exceed the preset fault tolerance threshold, a temporary causal dependency graph is constructed, and a reordering priority weight vector is generated accordingly. The causal reordering correction process is performed using the reordering priority weight vector to generate a baseline event segment, which is then submitted to the formal replay queue. Perform a full replay operation on the benchmark event segment and record the time sequence of room opening and closing actions. At the same time, collect cache reconstruction time and actual room status trajectory data and encapsulate them to generate a replay evidence package. The replay evidence package, along with the original segment metadata, is written into the feedback knowledge base, and the feedback knowledge base is periodically scanned. Cluster analysis is used to generate a verification rule update strategy. The conflict marker set and source credibility decay model parameters are adjusted based on the verification rule update strategy.

[0011] The room state event replay method based on event segment consistency check provided in this application has the following beneficial effects: (1) By constructing a closed-loop feedback mechanism with bidirectional coupling of verification and playback, the accuracy and adaptability of hotel room status event consistency checks are significantly improved. Traditional methods usually use static rules to perform one-way verification of event streams, which is difficult to cope with complex temporal conflicts and state deviations caused by heterogeneous sources. Especially when facing scenarios with multiple intertwined events such as manual intervention, system callbacks and timed polling, misjudgments or missed detections are likely to occur. This solution uses state fingerprints as the core anchor point and introduces a lightweight pre-playback sandbox before the event segment is submitted. It reconstructs the local room status and generates pre-playback fingerprints. By comparing the original fingerprints with the pre-playback results, the deviation is initially screened. When an inconsistency exceeding the fault tolerance zone is detected, the intra-segment causal reordering mechanism is dynamically triggered. A temporary dependency graph is constructed based on the difference summary to identify the dominant change source event and adjust its priority weight in the verification. This effectively overcomes the consistency misjudgment problem caused by event temporal disorder or timestamp abnormalities in traditional methods, and greatly improves the identification accuracy and processing robustness of abnormal events.

[0012] (2) Based on the strategy self-evolution mechanism driven by the replay evidence package, the continuous optimization of the verification logic and the enhancement of the scenario adaptation capability are realized. Existing systems generally rely on manual configuration or offline analysis for updating verification rules, and lack the ability to automatically extract failure modes from actual operation data, resulting in rules lagging behind business changes. After each full replay is completed, this solution collects actual room status trajectory, action sequence chain and cache reconstruction time and other operation indicators to form a structured replay evidence package, and persists it to the feedback knowledge base along with metadata; the verification strategy engine periodically performs cluster analysis on the knowledge base, extracts high-frequency failure modes for repeated deviation events under the same target key, such as "manual correction of polling results that are earlier than their timestamp" or "callback events carrying reverse offset summaries", and automatically updates the conflict mark set and source credibility decay model accordingly, so that the system has the ability to learn from historical errors and optimize future judgments. This mechanism eliminates the need for high-overhead components such as external large-scale model inference or global logic clocks. It achieves a gradual improvement in verification capabilities through only lightweight rule fine-tuning, significantly enhancing the system's adaptability to dynamic and complex event streams and its long-term stability while ensuring low-latency processing.

[0013] The aforementioned technical methods collectively construct a self-evolving verification system aimed at state consistency and driven by empirical feedback. This system not only effectively solves key technical challenges such as unreliable room status transition paths, ambiguous causal relationships, and rigid rules in multi-source heterogeneous event environments, but also achieves a high degree of automation and intelligent upgrading of the verification process. Without increasing additional clock synchronization costs or relying on centralized arbitration mechanisms, the system forms a complete closed loop from anomaly detection and root cause localization to rule optimization through the synergy of fingerprint comparison, pre-replay verification, and strategy iteration. This significantly improves the reliability and timeliness of room status event handling, providing solid technical support for a high-concurrency, highly consistent hotel inventory management system. Attached Figure Description

[0014] Figure 1 This is the main flowchart of a room state event replay method based on event segment consistency checks.

[0015] Figure 2 This is a sub-flowchart of a room state event replay method based on event segment consistency checks.

[0016] Figure 3 This is another sub-flowchart of a room state event replay method based on event segment consistency checks. Detailed Implementation

[0017] Embodiments of the present invention are described in detail below. Examples of these embodiments 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.

[0018] 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.

[0019] like Figure 1 As shown, this application provides a method for replaying room status events based on event segment consistency checks, specifically including: S1: Obtain the multi-source asynchronous room status event stream and generate a verification event segment containing the source type, collection batch number, link timestamp, and a summary of the differences from the previous confirmed status; S2: Perform structure extraction operation on the event segment to be verified, parse out the initial state snapshot, final state snapshot and intermediate difference summary sequence, and construct the original segment state fingerprint; S3: Perform pre-playback processing on the original segment state fingerprint, reconstruct the local room state cache and output the pre-playback state trajectory, and then calculate and generate a playback deviation metric by comparing the pre-playback state trajectory with the original segment state fingerprint. S4: For the event segment to be verified whose playback deviation metric value has been determined to exceed the preset fault tolerance threshold, a temporary causal dependency graph is constructed, and a reordering priority weight vector is generated accordingly. S5: Perform causal reordering correction processing using the reordering priority weight vector, generate baseline event segments, and submit them to the formal replay queue; S6: Perform a full replay operation on the baseline event segment and record the timing chain of room opening and closing actions. At the same time, collect cache reconstruction time and actual room status trajectory data and encapsulate them to generate a replay evidence package. S7: Write the replay evidence package along with the original segment metadata into the feedback knowledge base, and periodically scan the feedback knowledge base to generate verification rules and update strategies through cluster analysis. S8: Adjust the parameters of the conflict marker set and source credibility decay model based on the verification rule update strategy.

[0020] Step S1: Obtain the multi-source asynchronous room status event stream and generate a verification event segment containing the source type, collection batch number, link timestamp, and a summary of differences from the previous confirmed status. Specifically, this includes: S1.1: Standardize and parse the multi-source asynchronous room status event stream, including polling triggers, callback notifications, and manual corrections, to extract a set of original event records containing hotel identifier, room type identifier, date dimension, source type, collection batch number, link timestamp, and status change content.

[0021] For multi-source asynchronous room status event signals transmitted from the acquisition end, the raw data stream, including polling triggers, callback notifications, and manual corrections, is read as the input object, and the event data packets are loaded into the parsing buffer queue in sequence according to the source type priority mapping table.

[0022] Perform unified field template mapping processing on the event data packets in the parsing buffer queue, and transform the original field names, location indexes and data types of events from various sources into a standardized field set structure to ensure that key fields such as hotel identifier, room type identifier, and date dimension are consistent across different source types.

[0023] The hotel identifier field in the standardized field set structure is validated for legality, and the character encoding, length and existence are checked. When an anomaly occurs, an anomaly flag attribute of source type is added for subsequent consistency checks.

[0024] Perform combined uniqueness verification on the room type identifier field and the date dimension field, generate a verification index for the combined field through hash pre-computation, and detect whether there are duplicate combinations in the same source event stream to trigger duplicate event overwrite flags.

[0025] Numerical parsing and time zone standardization are performed on the link timestamp field. The timestamp values ​​carried by different source types are converted into a unified reference time using the UTC standard time zone offset rule, and the original time values ​​are retained as the original field metadata.

[0026] Perform differential parsing on the status change content field to extract the change triple consisting of the change instruction, field name, and old and new values, and attach the collection batch number field to generate a structured event record unit.

[0027] Through the above standardized parsing process, the multi-source asynchronous room status event stream is transformed into a set of raw event records containing hotel identifier, room type identifier, date dimension, source type, collection batch number, link timestamp, and status change content, thus preparing the input for subsequent multi-dimensional hash mapping and aggregation.

[0028] For example, in a hotel room status event collection scenario containing polling triggers, callback notifications, and manual correction sources, the parsing buffer queue loads 96 event data packets at a time. The collection batch number for the polling trigger source is 20240601, the collection batch number for the callback notification source is 20240601-1, and the collection batch number for the manual correction source is 20240601-2. After standardized field template mapping, the hotel identifier field is UTF-8 encoded and its length is maintained at 8 characters. The room type identifier field has undergone uniqueness verification and no duplicate combinations were found. The link timestamp field is standardized using UTC±0, converting the original Beijing time zone time 2024-06-01 14:35:20 to UTC time 2024-06-01 06:35:20, while retaining the original time as the original metadata. The status change content field is parsed to generate a difference triple containing the field name "Available Quantity," the new value 5, and the old value 7, and appends the corresponding source type and collection batch number to form a complete event record unit. This set can be directly used as input to the multidimensional hash map during output, ensuring significantly improved logical isolation and data integrity in subsequent aggregation stages.

[0029] S1.2: Perform multidimensional hash mapping calculation based on the hotel identifier, room type identifier, and date dimension in the original event record set to generate a target key index sequence for logical isolation, and merge the original event records with the same target key index into the same event cluster to be processed.

[0030] Based on the hotel identifier, room type identifier, and date dimension in the original set of input events, a three-dimensional feature index vector is first constructed in memory to ensure that the value of each dimension is a parsed standardized text or numerical type, and this vector is used as the input data source for hash calculation.

[0031] A multidimensional hash mapping algorithm is used to perform key-value mapping processing on the above three-dimensional feature index vector. By combining hash functions, the single-dimensional hash values ​​of hotel identifier, room type identifier and date dimension are calculated respectively. Then, a globally unique target key hash value is generated by a combination of bitwise XOR and cyclic shift to improve the uniformity of hash distribution and avoid collision probability.

[0032] A hash bucket allocation operation is performed on the generated target key hash value. Logical isolation is achieved through a fixed bucket size and a dynamic overflow list structure, ensuring that event records with the same target key hash value are allocated to the same hash bucket.

[0033] Perform hash value equivalence checks within the bucket, establish a target key index sequence, group all original event records with the same hash value that have passed the equivalence check into the same event cluster to be processed, and maintain the original link timestamp attribute within the cluster for subsequent time-series sliding window processing.

[0034] Through the aforementioned multidimensional hash mapping and bucketing merging method, the standardized event records from the previous step are transformed into a target key index sequence with logical isolation and unique identification capabilities, thereby realizing the aggregation preparation stage of multi-source asynchronous room status events.

[0035] S1.3: Perform a time-series sliding window alignment operation using the link timestamps of each event within the event cluster to be processed, in order to determine the start and end boundaries of the event segment, thereby constructing an initial aggregated event segment covering a specific time window.

[0036] The input data includes clusters of events to be processed, generated by multidimensional hash mapping calculation and merging. Each event record carries a link timestamp as a basis for time-series positioning.

[0037] Based on the link timestamps of each event in the event cluster to be processed, a time series vector is constructed and arranged in ascending order of time to ensure that subsequent window positioning is not affected by the original arrival order.

[0038] A sliding window mechanism is used to perform interval scanning on the time series vector, and the window movement process is controlled according to the preset window length parameter and the window sliding step size. At each window position, events in that interval are marked as candidate aggregation objects.

[0039] The start and end times of the window coverage are calculated for the candidate aggregate object set, and the earliest and latest timestamps in the window are used as the start and end boundaries of the event segment, respectively, to ensure that the event segment covers a specific and continuous time interval.

[0040] A boundary determination formula is introduced to exclude candidate segments that span too large a time span by comparing the difference between the window boundaries and the maximum allowable interval.

[0041] The initial aggregation event segment is generated using window boundaries and candidate aggregation objects, and start and end time indices are attached to provide time interval constraints for subsequent state difference calculations.

[0042] By using the sliding window alignment method described above, the cluster of events to be processed is converted into an initial aggregated event segment covering a specific time window, thereby realizing the time-series structured aggregation of multi-source asynchronous room status events.

[0043] S1.4: Perform state difference calculation processing on adjacent events within the initial aggregated event segment to quantify the granularity of field changes of the current event relative to the previous confirmed state and generate a difference summary sequence, thereby improving the content integrity of the initial aggregated event segment.

[0044] S1.5: Perform a structure encapsulation operation based on the difference summary sequence and the original metadata to generate a standardized event segment to be verified that includes the source type, collection batch number, link timestamp, and difference summary with the previous confirmation status, which serves as the direct input object for the subsequent consistency check module.

[0045] Step S2: Perform a structure extraction operation on the event segment to be verified, parsing out the initial state snapshot, final state snapshot, and intermediate difference summary sequence, and constructing the original segment state fingerprint. Specifically, this includes: S2.1: Perform a full field comparison of the initial state snapshot and the final state snapshot contained in the event segment to be verified, identify the set of key fields in the room status attributes that have undergone numerical changes or state reversals, and calculate and generate key field change vectors based on the field change type and the difference between the old and new values ​​to quantify the static data drift amplitude of a single point event.

[0046] The initial state snapshot and final state snapshot contained in the event segment to be verified are loaded into the memory comparison module, and a mapping index between the initial state field matrix and the final state field matrix is ​​established to ensure the accuracy of field alignment.

[0047] Perform a numerical difference detection operation on each field in the mapping index. If the field type is numeric, calculate the difference between the final value and the initial value. If the field type is enumeration or state, determine the state flip and record the state flip type to capture full information on static data drift and logical state changes.

[0048] The difference magnitude and status reversal type are aggregated in the field coding order to generate a list of field change records. The source type and the business category to which the field belongs are marked in the list for subsequent key field filtering.

[0049] Based on the list of field change records, the set of fields in the room status attributes that have undergone numerical changes or status reversals are filtered according to the preset key field rule set. Redundant or non-critical information fields are removed to ensure that the set of key fields accurately reflects the changes in the core information of the room status.

[0050] The magnitude and type of change of each field in the selected set of key fields are encoded into multi-dimensional feature vector elements. A vector combination strategy is used to construct a key field change vector. Each dimension of the vector corresponds to a field change unit. The numerical part is used to quantify the magnitude of static data drift, and the state part is used to characterize the direction of logical change.

[0051] By using field comparison and encoding processing, the results of the previous step are transformed into key field change vectors, thereby enabling the static data drift amplitude of a single-point event to be characterized by a combination of numerical differences and state reversals.

[0052] For example, in a hotel's PMS system, when this step is performed on room type "A" on June 15, 2024, the initial state snapshot includes the fields: Available rooms = 20, Reservations locked = 2, Room status indicator = Open; the final state snapshot shows Available rooms = 15, Reservations locked = 3, Room status indicator = Closed. After field matrix mapping, it is detected that the difference in available rooms is 5, the difference in reserved rooms is 1, and the room status indicator flip type is Open → Closed. The filtering rules include Available rooms, Reservations locked, and Room status indicator in the key field set. The Available rooms change unit value is 5 and the direction is decreasing, the Reservations locked room change unit value is 1 and the direction is increasing, and the Room status indicator change unit direction is Closed. A key field change vector [5 (decrease), 1 (increase), off] is constructed. This vector will participate in the generation of semantic state transition path encoding and key field change hash sequence in subsequent steps. The verification results show that the vector can accurately reflect the static drift characteristics of the core state of the room state event segment. The application effect significantly improves the recognition accuracy of single-point event data drift in consistency verification.

[0053] S2.2: Based on the key field change vector and the action instructions in the intermediate difference summary sequence, the transition logic between each state node is connected in a linear time order. The discrete room status change records are converted into continuous state transition descriptors using the finite state machine mapping rule, thereby generating semantic state transition path codes that represent the integrity of business logic.

[0054] Based on the action instructions in the key field change vectors and intermediate difference summary sequences, a state node index table linearly sorted by time within the event segment to be verified is loaded as the input basis for state logic construction. The field identifiers and numerical change types of each key field change vector in the state node index table are parsed and mapped to action semantic codes for room status changes, establishing a one-to-one correspondence between action instructions and field changes. State node concatenation is executed according to the link time sequence upon which the action instructions depend, encapsulating the field change logic between adjacent nodes into state transition edges, forming a preliminary discrete state transition linked list. A finite state machine mapping rule base is invoked to perform state encoding transformation on each state transition edge in the preliminary discrete state transition linked list, mapping the room status change semantics to legal states in the finite state machine state set, and describing the transition relationships between nodes using state transition functions. A continuous state flow descriptor sequence is constructed based on the finite state machine state set and state transition functions, and a timestamp and source type identifier are appended to this sequence to ensure the integrity and traceability of the business logic. By using a finite state machine mapping process, the key field change vector and difference summary sequence from the previous step are transformed into semantically searchable state flow codes, thereby achieving a unique representation of the business logic integrity of the event segment to be verified.

[0055] S2.3: Perform serialization and concatenation operations on each state node and its associated key field change vector in the semantic state transition path encoding. Use a cryptographic hash algorithm to perform one-way compression calculation on the concatenated binary data stream to generate a fixed-length key field change hash sequence with anti-collision properties, so as to realize the digital fingerprint solidification of room status change content.

[0056] For each state node and its associated key field change vector in the semantic state transition path encoding, structured serialization processing is performed. The differences between the field values ​​of the state node descriptor and the corresponding change vector are converted into binary data fragments according to the preset field order. During the conversion process, the consistency of field data type and length is maintained to ensure reversible parsing.

[0057] The serialized state node data fragments are concatenated sequentially according to the node order in the path encoding. The location of the concatenated stream is achieved by embedding node separators and path position indexes during the concatenation process, and cross-boundary interference between different node data is avoided in subsequent hash calculations.

[0058] For the completed binary data stream, select an encryption hash algorithm according to the security level, preferably the SHA-256 or SHA-3 algorithm with good anti-collision performance, and perform length verification and entropy value determination before hash calculation to verify the integrity and randomness of the input stream.

[0059] The cryptographic hash algorithm is invoked to perform one-way compression calculation on the binary data stream, ensuring that the output hash value is mathematically irreversible, while maintaining a fixed-length format in memory for consistency comparison with other event segment hashes.

[0060] The generated fixed-length hash value is marked as a key field change hash sequence, and this sequence is solidified as a digital fingerprint of the room status change content for subsequent field-level consistency determination during playback verification.

[0061] By using serialization concatenation and encrypted hashing, the semantic state transition path generated in the previous step is converted into a collision-resistant fixed-length key field change hash sequence, thereby achieving structural solidification and consistent traceability of room status change content.

[0062] S2.4: Extract the link timestamps of all events within the event segment to be verified to form a time distribution set, calculate the interval variance between adjacent timestamps and the overall distribution dispersion, derive the disorder index of the time dimension using the information entropy calculation formula, and generate a timestamp distribution entropy value that reflects the intensity of multi-source concurrent conflicts to quantify the disordered timing characteristics of event arrival.

[0063] The process extracts the set of link timestamps within the event segment to be verified as input data, and performs time feature parsing to establish a time distribution set. For this time distribution set, interval calculations are performed on adjacent timestamps according to the order of event arrival. The difference between each pair of adjacent timestamps forms a time interval sequence, and the variance of this sequence is obtained using the variance calculation formula. Based on this variance index, the dispersion of the time interval sequence is calculated, and the difference between the global maximum and minimum intervals is divided by the mean to form the overall distribution dispersion. Using the interval variance and overall dispersion as input, a time series discrete feature vector is constructed, and the information entropy calculation formula is applied to derive an index of the disorder level in the time dimension. The information entropy value is output as the timestamp distribution entropy value and appended to the event segment's time series statistical feature set. Through this processing method, the timestamp data from the previous step is transformed into a quantifiable numerical index characterizing the intensity of multi-source concurrent conflicts, achieving accurate quantification of the disordered characteristics of event arrival times.

[0064] For example, in a hotel room status event consistency verification scenario, the event segment to be verified contains 15 link timestamps, in milliseconds. The time interval sequence obtained by calculating the sequential difference is [120, 140, 130, 900, 150, 125, 128, 132, 910, 145, 135, 127, 129, 134]. The interval variance is calculated using the variance formula, and the variance value is 101234.6 milliseconds. The overall dispersion is 4.13, obtained by dividing the difference between the maximum value (910) and the minimum value (120) by the mean (191.3). The frequency of each time interval is converted into a probability, for example, 120 milliseconds accounts for 1 / 15, 140 milliseconds accounts for 1 / 15, 900 milliseconds accounts for 1 / 15, and so on, forming a probability vector sequence of the frequency proportion of each time interval. Substituting these into the information entropy formula, the timestamp distribution entropy value is calculated to be 3.912. The timestamp distribution entropy value is marked as a high entropy value, indicating that there is a significant disorder feature in the event arrival sequence. The system encapsulates it as a temporal disorder feature in the subsequent consistency fingerprint fusion step, along with business logic features and data content features, to support the priority determination of playback deviation measurement and causal reordering.

[0065] S2.5: The generated semantic state transition path code, key field change hash sequence, and timestamp distribution entropy value are used as three-dimensional feature inputs. Multi-dimensional vector fusion encapsulation processing is performed to construct a composite data structure containing business logic features, data content features, and time-series statistical features. Finally, the original segment state fingerprint used to uniquely identify the consistency of the event segment is output.

[0066] like Figure 2 As shown, step S3: The original segment state fingerprint undergoes pre-playback processing to reconstruct the local room state cache and output the pre-playback state trajectory. Then, a playback deviation metric is calculated by comparing the pre-playback state trajectory with the original segment state fingerprint. Specifically, this includes: S3.1: Obtain the semantic state transition path encoding and key field change hash sequence contained in the original segment state fingerprint, construct a sandbox running space independent of the production environment using isolated memory mapping technology, and parse the semantic state transition path encoding into an executable room state change instruction set to initialize the sandbox context.

[0067] The input data includes the original segment state fingerprint generated in step S2. This fingerprint contains semantic state transition path encoding and key field change hash sequences, serving as a unique anchor point to identify the current room state change logic and data content. During sub-step S3.1, the semantic state transition path encoding is first used as parsing input. Through state node list extraction and action instruction mapping, discrete state descriptors are converted into an executable room state change instruction set, forming an instruction sequence mapping table. Isolated memory mapping technology is used to partition a sandbox runtime space independent of the production environment on a physical server or virtualization platform, providing a safe and non-production-interfering computing environment for instruction set execution. Following the sandbox memory mapping strategy, the key field change hash sequences are loaded into the read-only data area to ensure that the hash values ​​are not modified during pre-replay, thereby guaranteeing the integrity and reproducibility of the verification process. Based on the sandbox context initialization parameters, the instruction sequence is registered one by one to the sandbox event handler, and an execution time index is set for each instruction so that it can be executed in order of information entropy in subsequent steps. By binding and loading instruction sets and hash data, the multi-dimensional feature data from the previous step is transformed into operable logical execution objects, realizing the transition from static verification data to a dynamic pre-replay environment and providing a reliable execution foundation for subsequent state trajectory generation. Through isolated memory mapping and instruction set initialization, the original segment state fingerprint parsing results from the previous step are transformed into an executable room state change instruction set within the sandbox, achieving the expected technical effect of secure reconstruction of the local room state cache.

[0068] For example, in a hotel room status event consistency verification system, the semantic state transition path code of the original segment state fingerprint of a certain event segment to be verified is 256 bytes long, and the key field change hash sequence contains 32 fixed-length hash values, each hash value being 16 bytes long. During sub-step S3.1, the system parses the 256-byte path code into 20 room status change instructions, including operations such as opening, closing, adjusting, and inventory adjustment, and attaches a corresponding execution index to each instruction. The isolation sandbox allocates 64MB of memory space, of which 4MB is divided into a read-only area to store the key field change hash sequence, and the remaining space is used for cached state storage during execution. When each instruction is registered to the sandbox event handler, the execution time sequence is calculated based on the original position index in the path code, for example, arranged according to the timestamp distribution entropy value, with instructions having higher entropy values ​​executed first. During hash sequence loading, a memory verification algorithm is used to verify whether the hash value matches the value in the original fingerprint, and the memory page is frozen after a successful match to prevent any modification. After this embodiment is executed, a set of room status change instructions that can be directly invoked is formed in the sandbox running space. Its execution effect can stably generate the pre-playback state trajectory corresponding to the original segment state fingerprint in the subsequent S3.2. The verification results show that this method can significantly improve the accuracy and security of pre-playback under high concurrency.

[0069] S3.2: Based on the room status change instruction set and the difference summary sequence in the event segment to be verified, perform simulated write operations in the sandbox running space according to the timestamp distribution entropy value, reconstruct the local room status cache instance reflecting the theoretical evolution process of the event segment, and generate a pre-replay state trajectory containing intermediate state snapshots.

[0070] Within the sandbox operating space, the set of room status change instructions obtained from the parsing of the original segment status fingerprint and the sequence of differences in the event segments to be verified are used as input objects to form an executable structured change operation queue.

[0071] The operation queue is sorted according to the magnitude of the timestamp distribution entropy value, so that the multi-source events present a simulated execution order from high entropy value to low entropy value in the sandbox execution sequence, thereby maximizing the reproduction of the temporal chaos characteristics of concurrent conflicts.

[0072] In the sorted operation queue, each room status change instruction and its corresponding difference summary are read sequentially. A simulated write operation is performed on the local room status cache instance in the sandbox, overwriting the old value in the cache with the new value of the specified field in the instruction, and recording the link timestamp and change semantics of the write action.

[0073] After each simulated write, the cache snapshot module is called to perform full field serialization processing on the current local room status record instance, generate an intermediate state snapshot object, and attach its corresponding state transition path code and key field change hash identifier.

[0074] All intermediate state snapshots are aggregated in execution order to form a pre-replay state trajectory covering the theoretical evolution process of the event segment, providing a complete time-series data chain for subsequent verification state fingerprint calculation and deviation measurement.

[0075] By using the above-mentioned simulation writing and snapshot generation processing method based on timestamp distribution entropy value sorting, the room status change instruction set of the previous step is transformed into a pre-playback state trajectory containing state content and temporal characteristics, realizing the visualization and structured solidification of theoretical state evolution in the sandbox environment.

[0076] For example, in a hotel PMS data playback scenario, the room status change instruction set obtained from the original segment state fingerprint parsing contains 12 events. Each event in the difference summary sequence involves a change in the room availability field, with the new value differing from the old value by 1-5. The calculated timestamp distribution entropy value is 0.82, indicating a high degree of disorder in the event arrival order. After sorting the operation queue by entropy value, a high-concurrency conflict simulation sequence is generated, with the sandbox cache instance initially in the state of "Availability = 50". When the first instruction is executed, the field value changes from 50 to 48, and the link timestamp 1623456700 is recorded; the second instruction changes the value to 45, corresponding to the link timestamp 1623456702. Each write operation calls the snapshot module to generate a snapshot containing the state transition path encoding and hash identifier. For example, after the first write, the encoding is "STAY1→STAY2", and the hash identifier is a fixed-length hash value. The final generated pre-replay state trajectory contains 12 snapshots arranged in execution order, each with a timestamp. This trajectory can be used in subsequent steps to recalculate the sandbox verification state fingerprint and supports the calculation of replay deviation metrics, thereby significantly improving the accuracy and reproducibility of consistency verification.

[0077] S3.3: Perform feature extraction on the final state snapshot in the pre-replay state trajectory, recalculate the verification state fingerprint in the sandbox environment, and perform bit-by-bit XOR comparison between the key field change hash sequence in the verification state fingerprint and the corresponding sequence in the original segment state fingerprint to identify state inconsistencies.

[0078] When performing feature extraction on the final state snapshot in the pre-replay state trajectory, the final state snapshot data, which includes room status attributes, available quantity values, and state reversal indicators, is loaded into the parsing module of the feature extraction engine. According to the field mapping rules in the original segment state fingerprint generation process, the difference between the old and new values ​​of each key field involved in the final state snapshot is quantified and the corresponding key field change vector is generated.

[0079] The extracted key field change vectors are sequentially serialized and concatenated according to the state node order. This concatenation is combined with the action instructions of the intermediate difference summary to form a new room state change encoding stream. A hash algorithm is then called to calculate a fixed-length hash value sequence of this encoding stream, which serves as the content feature part of the verification state fingerprint in the sandbox environment.

[0080] Perform a bitwise XOR operation between the key field change hash sequence in the verification status fingerprint and the corresponding hash sequence in the original segment status fingerprint. Take the absolute value of each bit and use the bits with non-zero results as status inconsistency bits.

[0081] The bit difference statistical function is used to sum all inconsistent bits in the state to form a number index of inconsistent bits, and this index is used as one of the input parameters for subsequent deviation measurement.

[0082] Through the above feature extraction and XOR comparison processing, the structural features of the pre-playback final state snapshot are transformed into a verification state fingerprint. The state inconsistency position is used as the core data for quantifying consistency deviation, so as to achieve the expected technical effect of locating the difference between the sandbox playback result and the original segment baseline state.

[0083] For example, in a hotel room status replay verification scenario with 200 rooms, the key field change hash sequence length of the original segment status fingerprint is configured to be 1024 bits. The verification status fingerprint obtained after feature extraction and hash calculation from the pre-replay final state snapshot has a hash sequence that is inconsistent with the corresponding bits of the original segment status fingerprint at bits 58, 79, 301, 450, and 900. The number of inconsistent bits is 5, the total number of bits in the hash sequence is 1024, and the proportion of inconsistent bits is calculated to be 0.0049. In this scenario, the proportion of inconsistent bits is significantly lower than the preset fault tolerance threshold of 0.01, and the difference between the verification status fingerprint and the original segment status fingerprint is within an acceptable range. The difference proportion data output by the replay deviation measurement module can be directly used for subsequent deviation measurement calculation and marked as a logically self-consistent state in the rule engine to support the closed-loop feedback mechanism of consistency verification.

[0084] S3.4: Based on the proportion of inconsistent state bits and the deviation of timestamp distribution entropy, the weighted Euclidean distance algorithm is applied to calculate the multidimensional spatial distance between the pre-playback state trajectory and the original segment state fingerprint, thereby quantifying and generating a playback deviation metric that characterizes the severity of logical conflicts.

[0085] Based on the feature comparison results between the pre-replay state trajectory and the original segment state fingerprint, the inputs are the proportion of inconsistent state bits and the deviation of the timestamp distribution entropy value, forming two core statistical indicators for measuring the severity of conflict. In the calculation processing chain, firstly, the proportion of inconsistent state bits is used as a structured numerical feature of the content difference of the key field, mapped to the measurement coefficient of the key field change hash space to reflect the strength of the difference between the verification state fingerprint and the original segment state fingerprint at the data content level. Secondly, the deviation of the timestamp distribution entropy value is normalized and used as a numerical feature of temporal concurrency disorder, achieving dimensional alignment with the content difference measurement coefficient in the same vector space, ensuring that the two features can be weighted and combined in a unified distance calculation framework. Thirdly, a dimensional weight vector for weighted Euclidean distance is set, with weight allocation based on the proportion of the impact of content conflict and temporal conflict on logical consistency in the historical replay deviation samples, and the sensitivity to high conflict patterns is improved by configuring weight parameters. Finally, the proportion of inconsistent state bits and the timestamp entropy deviation value are used as two vector components to perform weighted Euclidean distance calculation. The calculated multidimensional spatial distance is then used as the playback deviation metric output, forming the final evaluation index for quantifying the severity of logical conflicts. Through the above weighted Euclidean distance processing method, the XOR comparison result and entropy deviation characteristics of the previous step are transformed into a numerical deviation index that can accurately characterize the intensity of logical conflicts in the event segment, realizing the input signal for predictive consistency verification and causal reordering correction.

[0086] For example, in the pre-replay processing of room status events in a hotel PMS system, the calculated percentage of inconsistent status bits is 0.35, the percentage of baseline content differences in the original segment status fingerprint is 0.20, the deviation of the timestamp distribution entropy value is 0.12, and the baseline timestamp distribution entropy value is 0.08. The weight parameters are configured as content difference weight 0.65 and temporal difference weight 0.35, resulting in a calculation of 0.1275. This value is determined to be higher than the system's pre-replay deviation tolerance threshold of 0.10, indicating that the event segment has significant logical conflicts in both content and temporal dimensions. The subsequent strategy engine will trigger a causal reordering process based on this deviation value. In this embodiment, by using a weighted Euclidean distance model for difference significance detection, the pre-quantification and identification of logical inconsistencies in multi-source concurrent event segments are achieved, effectively improving the conflict capture capability of the verification module in a high-concurrency environment.

[0087] S3.5: Compare the playback deviation metric with the preset fault tolerance threshold of 0.01. If the result is within the threshold, mark the event segment to be verified as logically consistent. If the result is outside the threshold, trigger the anomaly flag signal and use the playback deviation metric as the input for subsequent causal reordering correction.

[0088] like Figure 3As shown, step S4: For the event segments to be verified whose playback deviation metric values ​​have been determined to exceed the preset fault tolerance threshold, a temporary causal dependency graph is constructed, and a reordering priority weight vector is generated accordingly. Specifically, this includes: S4.1: Perform field change granularity parsing on the difference summary sequence, extract the state transition path code and key field change hash sequence corresponding to each room state event, so as to generate an atomic change feature set containing fine-grained operation semantics.

[0089] For event segments to be verified where the playback deviation metric has been determined to exceed the preset fault tolerance threshold, a difference summary sequence is loaded as the parsing input, and the parsing object is limited to the field change records of each event.

[0090] For each event in the difference summary sequence, perform field-level traversal of the field change records, extract the change type (numerical increment, decrement, state reversal, etc.) and change magnitude parameters of each field, and solidify them into a field change granularity parameter vector.

[0091] Based on the extracted field change granularity parameter vector, combined with the mapping relationship between the initial and final states of the event node within the segment, the state transition path encoding generation logic is invoked to encode and output the event change action path as a connectable sequence.

[0092] Perform an association retrieval on the generated state transition path code, match and extract the key field change hash sequence bound to the path code, and ensure that the digital fingerprint of the changed content is synchronously bound to the path semantics.

[0093] The field change granularity parameter vector, state transition path encoding, and key field change hash sequence are combined into triples according to the event node dimension to form an atomic change feature set containing fine-grained operation semantics, which serves as the basic input for subsequent conflict detection and causal dependency graph construction.

[0094] Through the above-mentioned field-level parsing and feature combination processing methods, the difference summary sequence results of the previous step are transformed into fine-grained operational semantic data adapted for conflict detection, thereby realizing the granularization of change content and feature standardization of multi-source asynchronous room status events.

[0095] S4.2: Perform conflict detection based on atomic change feature set and link timestamp distribution entropy value to identify abnormal event pairs with state overlap or logical mutual exclusion relationship, so as to generate a conflict mark set carrying conflict type identifier and reverse offset.

[0096] Based on the atomic change feature set generated by step S4.1 and the link timestamp distribution entropy value within the same event segment as input data, multi-source concurrent conflict detection technology is performed. The atomic change feature set is grouped according to field change categories, and the relative position difference of events within the same group in time sequence is calculated to reveal potential overlay relationships. State overlay judgment rules are applied to the grouping results. Based on the difference between the old and new values ​​of each field and the change range, the degree of overlay of the new value operation on the old value operation executed earlier is identified, and the overlay event pairs are marked as overlay conflicts. Logical mutual exclusion rule matching is performed on the state change type sequences of all event pairs, and event pairs with state flips or mutually exclusive states written to the same field are marked as mutually exclusive conflicts. The sequence offset of the marked conflict event pairs is calculated using the link timestamp distribution entropy value. Based on the sign and absolute value of the offset, the degree of reverse offset in time for the conflict event pair is determined, and a reverse offset identifier is generated for event pairs with an absolute offset value greater than the conflict critical time window calculated based on the entropy value. The conflict category identifiers, offset values, and reverse offset identifiers of overlapping and mutually exclusive conflict event pairs are merged into a conflict tag set, which serves as the original input parameters for subsequent causal dependency graph construction. Through the sequential execution of conflict detection, the atomic change feature set of the previous step is transformed into a conflict tag set with conflict type and time offset features, achieving complete identification and structured encoding of anomalous event pairs.

[0097] For example, a hotel room status event segment contains five atomic change operations, corresponding to modifications to the fields "Available Rooms" and "Reservation Status". Event E1 modifies the available rooms to 10 at timestamp 100, event E2 modifies the same field to 8 at timestamp 105, event E3 modifies the reservation status to TRUE at timestamp 102, event E4 modifies the reservation status to FALSE at timestamp 108, and event E5 modifies the available rooms to 9 at timestamp 101. After grouping the atomic change feature set by field, E1, E2, and E5 are grouped into the available rooms group, and E3 and E4 are grouped into the reservation status group. In the available rooms group, E2 overwrites the new value of E1, and its execution time is later than E1, marked as an overwriting conflict; the reservation status values ​​of E4 and E3 are mutually exclusive, marked as a mutual exclusion conflict. The link timestamp distribution entropy value is calculated to be 0.75, and the conflict critical time window is defined as 3 time units. The calculated offset is 5, which is greater than the critical window. Therefore, a reverse offset marker is added for the covering conflict. Similarly, the offset of E4 to E3 is 6, and a reverse offset marker is added for the mutual exclusion conflict. The final output conflict marker set contains two pairs of event conflicts, each pair of conflicts is accompanied by a type identifier and a reverse offset, providing accurate anomaly relationship data for the subsequent construction of the causal dependency graph in S4.3.

[0098] S4.3: Construct a directed acyclic graph mapping mechanism using conflict marker set and source type credibility decay model, map atomic change feature set as nodes and causal dependency relationship as edges to generate a temporary causal dependency graph that reflects the real business causal logic.

[0099] Based on the conflict type identifier and reverse offset information in the conflict marker set, and combined with the parameter matrix of the source type credibility decay model, the data structure of nodes and edges in the directed acyclic graph mapping mechanism is initialized. Each room state event in the atomic change feature set is mapped to a graph node, and the corresponding state transition path code, key field change hash value, and source type credibility weight factor are embedded in the node attributes. The conflict marker set is traversed, and for each pair of event nodes with overlapping or mutually exclusive relationships, their reverse offset values ​​are parsed and the causal order difference index is calculated. The dependency direction is determined using the following formula: Among them, t j With t i Let w represent the link timestamps of event j and event i, respectively. src The source type credibility weight coefficient is represented by the D value, which determines the start and end points of directed edges based on the sign and absolute value of the D value. Conflict type weighting is applied to all dependent edges, increasing the edge weight coefficient of mutually exclusive conflicts to 1.5 times that of covering conflicts to reflect the importance of mutual exclusion relationships for causal consistency in business logic. After the dependent edges are constructed, acyclicity verification and edge set pruning are performed on the graph structure, deleting closed-loop edges generated by errors or data noise and recording pruning log metadata. The pruned directed graph is returned as a temporary causal dependency graph object, with source type credibility decay parameters appended to the nodes, providing a complete causal logical topological foundation for subsequent topology analysis and priority weight calculation. By constructing a directed acyclic graph based on the conflict marker set and credibility model, the atomic change feature data from the previous step is transformed into a causal dependency topology that can be used for priority weight derivation, achieving quantification and structured modeling of the causal relationships of conflict events.

[0100] For example, for a hotel's room status event segment, the atomic change feature set includes event E1 (polling source, timestamp 1620000000), event E2 (manual correction source, timestamp 1620000100), and event E3 (callback source, timestamp 1620000050). The conflict marker set indicates that E2 covers E1 and has a reverse offset of -50, while E3 and E1 are mutually exclusive with a reverse offset of -10. The source type credibility decay model sets the weight of the polling source to 0.8, the weight of the manual correction source to 0.65, and the weight of the callback source to 0.7. In the dependency direction calculation, the causal difference index result of E2→E1 is negative and has a large absolute value, so the dependency edge direction is set to E2 pointing to E1; the causal difference index result of E3→E1 is positive and mutually exclusive, so the dependency edge weight coefficient is multiplied by 1.5 to obtain a strengthening edge weight of 1.2. The constructed temporary causal dependency graph includes nodes E1, E2, and E3, along with the aforementioned directed edges. After acyclicity verification, the graph structure was confirmed to be valid and used as input for subsequent priority weight calculations. In the performance verification, this dependency graph successfully identified E2 as the dominant change source during the topology analysis phase, and the strengthening of mutually exclusive conflict edge weights ensured that the priority weight distribution met expectations, significantly improving the logical correctness of subsequent corrections.

[0101] S4.4: Perform topology analysis and dominant change source identification calculation on the temporary causal dependency graph, and determine the critical path weight and corrected priority coefficient of each node in combination with the conflict type identifier to generate an initial reordering priority weight vector.

[0102] For the temporary causal dependency graph generated in step S4.3, topology analysis and dominant change source identification calculations are performed, provided that the input conditions include the atomic change characteristics of nodes, the direction information of dependent edges, and the conflict marker set. The directed acyclic graph is decomposed into node levels, with all nodes without in-degree assigned to the initial layer. Subsequent dependency levels are then parsed sequentially to generate a hierarchical topology sequence reflecting the depth distribution of causal chains. The critical path length index is calculated based on the hierarchical position of nodes and the number of dependent edges in the topology sequence, and the set of nodes containing the longest dependency chain is labeled as potential dominant change source candidates. The potential dominant change source candidate nodes are weighted according to the conflict type identifier. Nodes with state coverage conflict type receive an amplification coefficient in the critical path weight calculation, while nodes with logical mutual exclusion conflict type receive a decay coefficient. The initial corrected priority coefficient is calculated by multiplying the node weight coefficient by the critical path length, and a comparable weight vector distribution is formed through normalization. For weight normalization, the weights of different nodes are adjusted to a uniform scale to ensure comparability in the subsequent weighted fusion processing in S4.5. By decomposing the topology and identifying the dominant change source, the temporary causal dependency graph of S4.3 is transformed into an initial reordering priority weight vector, thereby achieving accurate quantification of the ordering criteria for conflict events.

[0103] S4.5: Based on the initial reordering priority weight vector and the playback deviation metric, a weighted fusion process is performed to dynamically adjust the weight distribution of high-conflict regions to eliminate temporal logic contradictions, so as to generate the final reordering priority weight vector used to guide causal reordering correction.

[0104] A joint weight adjustment model is established based on the initial reordering priority weight vector and the playback deviation metric. The priority weight vector output from the previous sub-step is used as the base weight input, and the playback deviation metric is used as the conflict intensity control factor input. The two input signals are normalized so that they can be fused and calculated in the common weight space.

[0105] The normalized base weight vector is multiplied and superimposed with the conflict intensity factor to form a pre-adjusted weight matrix for high-conflict regions. Each element in this matrix corresponds to the pre-adjusted weight value of an event node within the event segment.

[0106] Local gradient amplification is performed on the pre-adjusted weight matrix of high-conflict regions. Based on the critical path importance coefficient of each node in the temporary causal dependency graph, dynamic weight shift is applied to the conflict intensity factor to increase the priority of conflict core nodes in reordering.

[0107] During the fusion process, a weighting function is applied, and a non-linear weighting strategy is enabled for nodes whose conflict intensity exceeds a set threshold. For example, a Gaussian weight boosting function is used to amplify the node weights, so that the weight distribution is more in line with the causal sensitivity characteristics of the conflict area.

[0108] The adjusted weight values ​​are calculated using a weighted fusion formula. The final reordering priority weight vector is then output through this formula and input into the causal reordering correction module to optimize the weight distribution for high-conflict regions and eliminate temporal logic contradictions.

[0109] By using weighted fusion processing, the initial weight vector from the previous step is transformed into a conflict-aware final reordering priority weight vector, thereby achieving targeted optimization of causal reordering and elimination of logical contradictions.

[0110] Step S5: Perform causal reordering correction processing using the reordering priority weight vector, generate baseline event segments, and submit them to the formal replay queue. Specifically, this includes: S5.1: Based on the reordering priority weight vector and the difference summary sequence in the event segment to be verified, the modification granularity identifier and conflict marker attribute of each room state event are extracted, and a temporary causal dependency graph containing event nodes and directed dependency edges is constructed to quantitatively characterize the potential causal constraint relationship between multi-source asynchronous room state events.

[0111] Based on the reordering priority weight vector and the difference summary sequence in the event segment to be verified, the difference summary parser is invoked to extract the modification granularity identifier corresponding to each room state event, mapping the field-level state change range and field type to a quantifiable granularity vector. The modification granularity vector is combined with conflict marker attributes, and the conflict intensity of event nodes is signal-encoded according to the conflict type identifier, forming a set of node attribute tuples. These node attribute tuples are mapped to node objects in a temporary causal dependency graph, and a unique event sequence identifier is assigned to each node to ensure the traceability of the dependency graph. A set of directed dependency edges is generated based on the conflict marker attributes, mapping the overlay, mutual exclusion, or reverse time offset relationships between event nodes to edge objects in the graph structure. Each edge object carries a causal dependency weight to reflect the degree of priority influence. The set of node objects and the set of directed dependency edges are merged to construct a temporary causal dependency graph, and the integrity of the graph structure is verified to ensure that each node is within a reachable path. By constructing a temporary causal dependency graph, the reordering priority weight vector generated in the previous step is concretized into a causal constraint structure among multi-source asynchronous room-state events, realizing a graph model representation of conflict event serialization, and providing an executable data foundation for subsequent topology analysis and conflict resolution.

[0112] S5.2: Perform topological analysis on the temporary causal dependency graph, calculate the global temporal conflict index based on the weight values ​​of each event node in the reordered priority weight vector, and identify the root conflict event pairs that cause temporal logic conflicts and their reverse dependency paths.

[0113] The input conditions are a temporary causal dependency graph that has been constructed and the corresponding reordering priority weight vector. The execution object is a graph structure containing multiple event nodes and directed dependency edges.

[0114] For event nodes in the temporary causal dependency graph, establish a node weight matrix based on the weight vector values. Use the weight of each node as the priority basis for topological traversal to perform hierarchical decomposition operation. During the decomposition process, calculate the cumulative values ​​of node weights and edge weights on each causal path.

[0115] The cumulative values ​​of each path are normalized to a uniform scale, and the path conflict impact factor is calculated based on the location of nodes with conflict markers in the path. A composite index vector containing path weight distribution and conflict impact factor is constructed.

[0116] The global temporal conflict index is obtained by multiplying the conflict influencing factor and the weight distribution in the composite index vector. For paths with a global temporal conflict index greater than a preset threshold, reverse dependency chain analysis is performed. The root conflict event pairs that cause the conflict are identified by traversing the dependency edges in reverse, and the dependency paths of the event pairs are extracted in a structured manner.

[0117] The identified root cause conflict event pairs and their reverse dependency paths are encapsulated into conflict decomposition data packets, which serve as direct input for subsequent dynamic permutation operations.

[0118] By using global topology traversal and exponential calculation, the graph structure result from the previous step is transformed into a quantitative indicator of the severity of contradictions and information on the location of root causes of conflicts, thereby enabling precise extraction of the dependent paths for reordering correction.

[0119] For example, in a room status event segment verification of a hotel PMS system, the temporary causal dependency graph contains 12 event nodes and 15 directed edges, with the reordering priority weight vector distributed in the range [0.1, 0.9]. The maximum cumulative weight of the path is 4.7, the minimum is 1.3, and the conflict impact factor is between [0.2, 0.8]. After normalization, the global temporal conflict index is calculated using the formula. When the total number of paths P=10, the sum of ∑(w×c) is 5.6, and the index I=0.56, which is higher than the preset threshold of 0.5, triggering reverse dependency chain analysis. Reverse traversal reveals a state coverage conflict between the manually corrected event and the callback event. The dependency path length is 4, containing two high-weight nodes (weights 0.85 and 0.78). This path is extracted and encapsulated into a conflict decomposition data packet. Subsequent dynamic replacement can reorder according to weight priority, eliminating the temporal conflict on this path and making the event segment meet the consistency verification conditions.

[0120] S5.3: Utilizing root cause conflict event pairs and reverse dependency paths, combined with source type credibility decay model parameters, a dynamic permutation operation is performed on the arrival order of events within the event segment to be verified, generating a preliminary corrected event sequence that eliminates temporal logic contradictions.

[0121] Based on the root cause conflict event pairs and their reverse dependency paths, the link timestamps of the event nodes, the modification granularity identifiers, and the source type indexes are extracted to form a dynamic sorting input matrix.

[0122] The source type credibility mapping process is performed on the above dynamic sorting input matrix, and the source type of each event node is converted into a weight coefficient according to the credibility decay model parameters.

[0123] The weight coefficients are weighted and superimposed with the position vectors of the event nodes in the reverse dependency path to form a temporal permutation priority index matrix.

[0124] The local permutation operation of the event arrival sequence is performed based on the temporal permutation priority index matrix, and the effectiveness of the permutation strategy is confirmed by calculating the conflict reduction index of causal constraints between events.

[0125] The event sequence obtained by the permutation operation is mapped back to the event segment to be verified, generating a preliminary corrected event sequence containing the adjusted timing logic, and a permutation operation log index is attached.

[0126] Through the above processing method, the root cause conflict event pairs and reverse dependency path results of the previous step are transformed into event time sequence adjustment data with conflict reduction effect, so as to achieve the output of preliminary corrected event sequence to eliminate the contradiction of time sequence logic.

[0127] For example, in a hotel PMS room status event processing scenario, conflict detection identifies a manually corrected event node and a polling event node as a root cause conflict event pair. The reverse dependency path is correction → polling. The credibility of the manually corrected source is reduced to 0.65 according to the decay model, while the credibility of the polling source is 0.80. When constructing the dynamic sorting input matrix, the timestamp of the corrected event link is 2024-06-05T10:23:54, and the modification granularity is level 2. The timestamp of the polling event link is 2024-06-05T10:21:13, and the modification granularity is level 1. After mapping the credibility, the weight coefficients are 0.65 and 0.80, respectively. They are weighted and superimposed with the reverse dependency path position vector [1,0] to obtain the temporal permutation priority index matrix [0.65,0.80]. A local permutation operation was performed based on the priority index matrix. The original conflict intensity was calculated to be 0.92, the permutation conflict intensity to be 0.35, and the global maximum conflict intensity to be 0.92, resulting in a total of 0.619, which was deemed valid. The final generated preliminary correction event sequence was polling event → manual correction event, and a permutation log was output for subsequent consistency constraint verification. The application effect was a significant reduction in conflict intensity and an improvement in the coherence of state transitions within segments.

[0128] S5.4: Recalculate the state transition continuity index within the segment based on the preliminary correction event sequence, verify whether the corrected event flow meets the preset constraints, and generate the final consistency verification passed event segment if the verification is successful.

[0129] Based on the event node state codes and corresponding key field change hash sequences of the preliminary corrected event sequence, the coherence recalculation of state transitions between adjacent nodes in the sequence is performed. A chain-dependent traversal is used to aggregate the continuity scores of each node, and a source type credibility coefficient and conflict resolution weight are introduced in this scoring process to form a multi-dimensional state transition coherence index. The multi-dimensional state transition coherence index is compared with a preset set of constraint parameters, including a threshold for the allowed number of state coverages, an upper limit for the number of reverse dependency paths, and a timestamp deviation tolerance value. Matrix matching is used to verify whether the event flow meets all causal consistency judgment rules. For parts where the degree of temporal deviation needs to be quantified during the verification process, if the score value is lower than the preset consistency threshold, the event flow is marked as qualified. For the verified qualified event flow, consistency verification is performed through identifier appending processing, and the state transition coherence index is encapsulated into structured event segment metadata. This processing method transforms the preliminary correction event sequence from the previous step into a final consistency verification event segment that meets preset constraints, thereby achieving complete elimination of contradictions and replayable benchmarking of the timing logic of multi-source asynchronous room status events.

[0130] For example, under the date dimension target key of a hotel room type, the initial corrected event sequence contains 15 events. After reordering conflicting events, the continuity score has a source type credibility coefficient of 0.8 and a conflict resolution weight of 0.6. The recalculated coherence index yields a comprehensive index value of 3.24. The preset causal consistency threshold conditions are: a state coverage threshold of 2, a reverse dependency path upper limit of 1, and a timestamp deviation tolerance of 500ms. Applying the above formula, assuming the number of events is 15 and the reference timestamp is the timestamp of the first event in the sequence, the sum of squares of the difference quantization function between the event timestamp and the reference timestamp is calculated to be 640,000, resulting in a temporal consistency score of 45,714.3. This score is lower than the preset consistency threshold of 50,000, thus passing the verification, and the consistency verification passed event segment is output. After this event segment is submitted to the formal replay queue, it can accurately reproduce the room opening and closing trajectories in the Codis room status cache, without logical breaks caused by multi-source conflicts, significantly improving the verifiability and data consistency of room status replay.

[0131] S5.5: Encapsulate the consistency verification passed event segment into a base event segment object, attach the consistency verification passed identifier and reorder log metadata, and submit it to the formal replay queue to trigger the subsequent Codis room status cache reconstruction process.

[0132] Step S6: Perform a full replay operation on the baseline event segment and record the timeline of room opening and closing actions. Simultaneously, collect the cache reconstruction time and actual room status trajectory data, encapsulate them to generate a replay evidence package. Specifically, this includes: S6.1: Obtain the difference summary sequence and source type identifier in the baseline event segment, initialize the local room status cache instance in the sandbox environment based on the hotel identifier, room type identifier and date dimension target key, and load the initial state snapshot into the local room status cache instance to build the playback initial context environment.

[0133] S6.2: Read the local room status cache instance in the initial context environment of the replay, and execute the room status change instructions one by one according to the event order after causal reordering correction within the baseline event segment to update the room availability status in the local room status cache instance, and generate intermediate room status trajectory nodes with link timestamps at each state transition.

[0134] Read the local room state cache instance in the completed replay initial context environment, call the event sequence parser to parse the event order in the baseline event segment after causal reordering correction, and generate an execution queue containing event node identifiers, state transition path codes and link timestamps.

[0135] Based on the timestamp field of the execution queue, the instruction scheduler determines the execution priority of events, loads the corresponding room status change instructions into the cache update pipeline according to the time sequence, and establishes a mapping table between instructions and target room status fields to ensure accurate location of updates.

[0136] In the cache update pipeline, the room status writer is called to read the field modification granularity description of each room status change instruction in turn, match the corresponding field position in the local room status cache instance, and perform status overwrite, add, delete or flip operations to complete the real-time update of the room availability status.

[0137] After each state update is completed, the trajectory node generator is called to extract a snapshot of the current room state cache instance, bind the state transition path code with the link timestamp, generate intermediate room state trajectory node objects and write them to the trajectory record buffer.

[0138] The trajectory recording buffer continuously accumulates intermediate room state trajectory nodes in the order of event execution completion, forming a complete state evolution chain within the segment, providing basic data for subsequent room closing and opening action recognition and time consumption calculation.

[0139] By using the above processing method, the initial playback context environment of the previous step is transformed into multi-time-series node state evolution data, realizing the parallel advancement of state update and trajectory generation, and ensuring the continuity and traceability of the playback process.

[0140] For example, in the baseline event segment with hotel identifier HT001, room type identifier STD01, and date dimension 2024-06-08, the event sequence contains 8 room status change instructions corrected for causal reordering, including 4 room closing operations and 4 room opening operations. The event sequence parser generates an execution queue with link timestamps ranging from 1686200000 to 1686203600 seconds. The instruction scheduler loads the change instructions sequentially into the cache update pipeline according to their timestamps. The room status writer performs increment / decrement operations on the available quantity field of the local room status cache instance; for example, the room opening instruction increases the field value by 3, and the room closing instruction decreases the field value by 2. The trajectory node generator generates intermediate room status trajectory nodes after each update and binds the timestamp with the state transition path code, writing them into the trajectory recording buffer. The final trajectory recording buffer contains 8 node objects, covering the entire process of the room status gradually changing from an initial available quantity of 20 to a final available quantity of 21. During this process, the state update and trajectory generation are completed simultaneously, ensuring that the playback path accurately reproduces the actual room state evolution.

[0141] S6.3: Monitor the state transition path encoding in the intermediate room state trajectory nodes, identify the key event nodes that trigger the room closing or opening action, extract the source type, collection batch number and conflict marker information of the key event nodes, and assemble them into a time sequence chain of room closing and opening actions in chronological order.

[0142] Using the intermediate room state trajectory nodes after causal reordering correction as input conditions, the node-based state transition parsing framework is invoked to perform node-by-node semantic matching operations on the state transition path encoding within each trajectory node, in order to determine whether the state node corresponds to the system-defined room closing action code or room opening action code.

[0143] The path codes of key event nodes that are determined to be room closing or room opening actions are submitted to the action classifier module, which calls the source type parser to parse its source type field and uses the collection batch parsing unit to read the collection batch number, forming a two-dimensional identifier of source type and batch number.

[0144] In the metadata reading results of the key event node, call the conflict mark extractor to read the conflict mark value generated by the node in the consistency verification phase, and package it with the source type and collection batch number into a structured action tuple.

[0145] Perform a timeline sorter operation on the link timestamps of all structured action tuples, and arrange the closing and opening action event nodes in chronological order to form an action sequence under strict time constraints.

[0146] The sorted action sequence is called to perform chain assembly processing by calling the action sequence chain generator to generate a room opening and closing action sequence chain containing action type, source type, batch number and conflict flag, and output to the subsequent time consumption record and replay evidence package encapsulation logic.

[0147] By using path coding semantic matching, source and batch parsing, conflict marker extraction, and time sequence sorting, the intermediate room status trajectory node results of the previous step are transformed into a time sequence chain of room opening and closing actions that can support auditing and strategy optimization, thereby achieving causal verifiability and full path recording of room status actions.

[0148] S6.4: Record the entire process timestamp from loading the initial state snapshot to completing the final room status change instruction execution, calculate the cache reconstruction time index, and perform structured association processing on the cache reconstruction time index and the actual room status trajectory data formed by aggregating intermediate room status trajectory nodes.

[0149] During the full replay of the baseline event segment, after loading the initial state snapshot into the local room status cache instance to form the initial replay context, it is necessary to obtain the system timestamp when the first room status change instruction was executed as the operation start time.

[0150] Read the system timestamp when the last room status change instruction is completed, and call the high-precision time difference calculation module to perform difference calculation on the start time and end time to form the original value of cache reconstruction time.

[0151] The original value of cache reconstruction time is processed to unify the unit and standardize the format, converting the millisecond-level time into a uniform second-level value and retaining three decimal places to adapt to the input requirements of the performance evaluation module.

[0152] The room status trajectory aggregation engine is invoked to sort and aggregate the state transition path codes, link timestamps, and action type information in the intermediate room status trajectory node sequence into actual room status trajectory data entities in chronological order.

[0153] Perform structured association processing to associate the normalized cache reconstruction time index with the actual room status trajectory data entity according to the target key index of the baseline event segment, and generate a composite playback performance record object with dual characteristics of time consumption performance and state change.

[0154] Through the above chain processing method, the intermediate room state trajectory node data output from the previous step is transformed into a composite performance record containing time consumption indicators and trajectory information, realizing the synchronous quantification of the timeliness and state evolution path of the playback process.

[0155] For example, during the playback of the baseline event segment with hotel identifier HZ001, room type identifier DLX01, and date dimension 2024-07-15, the timestamp of the first instruction executed after loading the initial state snapshot is 1689450000123 milliseconds, and the timestamp of the final instruction completion is 1689450003123 milliseconds. The cache reconstruction time, calculated using high-precision time difference, is 3.000 seconds. This time value is formatted and retained to three decimal places as 3.000 seconds. During playback, the trajectory aggregation engine collects a sequence containing 12 intermediate room status trajectory nodes. Each node contains a state transition path code, a link timestamp (millisecond precision), and a corresponding action type (room closed / room open / no action). After aggregation, the actual room status trajectory data entity is formed. The structured association operation binds the time consumption index of 3.000 seconds to the trajectory data entity to the target key HZ001-DLX01-2024-07-15. The corresponding composite performance record object achieves the effect of significantly shortening the time consumption and ensuring complete traceability of the trajectory path in subsequent performance analysis, ensuring that there is sufficient empirical basis for the consistency verification strategy update.

[0156] S6.5: Obtain the time sequence chain of room opening and closing actions, cache reconstruction time indicators, and actual room status trajectory data. Combine the original segment metadata of the baseline event segment to perform serialization encapsulation operations, generate a playback evidence package containing complete playback process features, and write it to the feedback knowledge base interface.

[0157] Step S7: Write the replay evidence package along with the original segment metadata into the feedback knowledge base, and periodically scan the feedback knowledge base, using cluster analysis to generate verification rule update strategies. Specifically, this includes: S7.1: Perform structured mapping processing on the actual room status trajectory data and original segment metadata in the playback evidence package to construct a multi-dimensional feature index record containing state fingerprint deviation values, source type distribution vectors and timestamp entropy value sequences, providing a standardized data base for subsequent knowledge storage.

[0158] S7.2: Based on multi-dimensional feature index records, perform time-series alignment and anomaly labeling fusion operations to generate a composite training sample set with conflict scenario labels, ensuring that the data stored in the feedback knowledge base can accurately reflect the true causal deviation of multi-source asynchronous events under a specific target key.

[0159] A time-series alignment operation based on target key grouping is performed on the state fingerprint deviation value, source type distribution vector and timestamp entropy value sequence contained in the multidimensional feature index record. A strict time sequence index is established within the same target key group to ensure that the event trajectory has a unified time reference frame during the comparison process.

[0160] By using a sliding time window to divide the timestamp entropy value sequence of each group, and matching the state fingerprint deviation value with the corresponding source type distribution vector within the window, the time coordinate standardization of cross-source events and the location of the degree of causal deviation are realized.

[0161] Anomaly detection rules are applied to the matching results within the window. Nodes with deviation values ​​exceeding the preset tolerance range are marked as abnormal nodes and an anomaly category identifier is attached. Anomaly categories include types such as coverage conflict, reverse dependency, and missed update. This identifier serves as the original basis for generating subsequent conflict scenario labels.

[0162] A fusion matrix is ​​constructed to integrate the deviation values, anomaly category identifiers, source type distributions, and timestamp information of abnormal nodes into a composite feature vector. This composite feature vector is then formed by concatenating multi-dimensional vectors to ensure that the training samples can simultaneously reflect both data content features and temporal statistical features.

[0163] Label mapping is performed in the fusion matrix, and each composite feature vector is classified into the corresponding conflict scenario label set according to the anomaly category identifier, generating a composite training sample set with conflict scenario labels, and using this sample set as the core data unit for the feedback knowledge base.

[0164] Through the above processing method, the multi-dimensional feature index records of the previous step are transformed into a composite training sample set with conflict scenario labels, so as to realize the accurate reflection of the degree of causal deviation of the data in the feedback knowledge base for multi-source asynchronous events.

[0165] For example, in a hotel PMS system, for a multi-dimensional feature index record with the target key "Hotel A-Room Type X-2024-06-15", the status fingerprint deviation value is 0.024, the source type distribution vector is [polling: 0.5, callback: 0.3, manual correction: 0.2], and the timestamp entropy value sequence is in the interval [0.75, 0.80]. Using a time-series alignment parameter with a sliding window length of 5 minutes and a step size of 2 minutes, the sequence is divided into windows. Within the third window, a node with a deviation value of 0.038 that exceeds the tolerance threshold of 0.03 is detected and marked as an anomaly category "coverage conflict". This node integrates the deviation value, anomaly category identifier, source distribution, and window start time to construct a composite vector [0.038, coverage conflict, [0.5, 0.3, 0.2], 2024-06-1510:12:00]. The composite vector is assigned to the conflict scenario label "Covering Conflict," forming a composite training sample set containing samples of 45 different target keys. After being stored in the database, this provides a sufficient labeled data foundation for subsequent density peak clustering. In this embodiment, the conflict scenario label fused by anomaly markers accurately covers key causal deviation nodes in the multi-source event chain, significantly improving the accuracy of subsequent high-frequency failure mode identification.

[0166] S7.3: Density peak clustering is used to calculate the spatial distribution of conflict scene labels in the composite training sample set to extract high-frequency failure mode clusters with significant aggregation characteristics, thereby quantitatively identifying typical consistency disruption patterns such as manual correction of hysteresis coverage or callback timestamp reverse offset.

[0167] Spatial feature mapping is performed on the conflict scene labels that have been fused in the composite training sample set. The multidimensional feature index records under the same label are converted into numerical vector representations containing state fingerprint deviation values, source type distribution vectors and timestamp entropy values, so as to ensure that the conflict scenes have a consistent measurement benchmark in high-dimensional space.

[0168] Based on the above spatial feature representation, the distance matrix required for density peak clustering is constructed, and the weighted Euclidean distance is used to measure the similarity between samples. The weight factor is determined by the normalization coefficient of the state fingerprint deviation value and the timestamp entropy value.

[0169] Local density calculations are performed on the distance matrix, and kernel functions are used to evaluate the number of neighbors and relative distance distribution of each sample within a radius threshold range, generating two core index sequences: local density and minimum distance.

[0170] After normalizing the local density and minimum distance indices, peak points are identified in the two-dimensional index space as cluster centers, forming the initial cluster division results for high-density regions.

[0171] Perform a sample backtracking inclusion operation on the cluster partitioning results, and classify isolated conflict scenario label samples into the corresponding clusters according to the nearest cluster center principle to ensure that the high-frequency failure mode clusters cover all conflict scenario label samples.

[0172] The cluster division results obtained by density peak clustering are used to transform the composite training sample set into a high-frequency failure mode cluster set, thereby realizing the quantitative identification of typical consistency destruction patterns such as manual correction of lag coverage or callback timestamp reverse offset.

[0173] Through the above clustering process, the composite training sample set from the previous step is transformed into pattern cluster data containing high-density distribution features, thereby achieving significant clustering identification of failure modes and providing structured input for regular gap analysis.

[0174] For example, in the feedback knowledge base of a hotel PMS system, 500 composite training samples are stored for the same target key (Hotel A + Standard Room + 2024-06-25). The state fingerprint bias value ranges from 0.15 to 0.65, the source type distribution vector consists of polling, callback, and manual correction events, and the timestamp entropy value ranges from 0.35 to 1.20. After converting the samples into spatial feature representations, the weighted Euclidean distance weighting factor is set to 0.6 for the state fingerprint bias value and 0.4 for the timestamp entropy value, and the radius threshold is set to 0.5. In the normalized local density and minimum distance two-dimensional space, peak samples with a local density greater than 0.8 and a minimum distance greater than 0.6 are identified as cluster centers, forming cluster A containing a manually corrected lag coverage pattern and cluster B containing a callback timestamp reverse offset pattern. The number of samples in the cluster centers are 12 and 9, respectively. Isolated samples were assigned to their corresponding clusters according to the nearest cluster center rule, resulting in cluster A having a total of 180 samples and cluster B having a total of 140 samples. The remaining samples were assigned to the low-density cluster C. This clustering result significantly improved the accuracy and coverage of conflict pattern recognition in subsequent rule gap analysis.

[0175] S7.4: Perform rule gap analysis based on the statistical distribution characteristics of high-frequency failure mode clusters to generate a set of candidate verification rule update strategies that include new conflict marking conditions, dynamic time window sliding step size, and source credibility decay coefficient, thus completing the transformation from data insight to logical rules.

[0176] An input feature matrix is ​​constructed based on the statistical distribution characteristics of high-frequency failure mode clusters. The rows of the matrix represent the frequency of different failure modes occurring under the same target key, while the columns represent indicators such as source type, timestamp entropy, and state fingerprint deviation. This feature matrix is ​​input into a rule gap analysis engine, where feature importance measurement is performed. The contribution of each indicator to failure mode identification is calculated using a combination of mutual information and analysis of variance. The indicator contribution results are used to match and retrieve existing verification rule bases, identifying uncovered conflict marking conditions and extracting corresponding source type combinations and time-series anomaly distribution features. Based on the extracted time-series anomaly distribution features, a dynamic time window generation module is invoked to calculate the sliding step size parameter for the adapted failure modes. Based on the conflict frequency of source type combinations, an update coefficient is derived using a credibility decay model. The newly added conflict marking conditions, the dynamic time window sliding step size Δ, and the source credibility decay coefficient α are encapsulated into a candidate verification rule update strategy set. This processing method transforms the clustering results of the previous step into a dataset that can be directly used for strategy iteration, realizing the transformation from data insight to logical rules.

[0177] S7.5: Based on the candidate verification rule update strategy set, perform validity verification and version merging operations to output the final effective verification rule update strategy, ensuring that the newly generated strategy can be directly applied to the consistency verification process of subsequent multi-source asynchronous room status event streams.

[0178] The strategy set is updated based on candidate verification rules. The newly added conflict marking conditions, dynamic time window sliding step size and source credibility decay coefficient in the strategy set are loaded as the initial inputs for the verification process.

[0179] The inputs for verification processing are grouped according to rule type. The sandbox instance of the consistency verification engine is called to perform replay simulation of similar historical event segments in an isolated environment and generate a verification deviation estimation matrix for each candidate rule.

[0180] Using the numerical results in the bias estimation matrix and a preset validity threshold, the fit of each candidate rule on the typical failure mode cluster is calculated using a decision formula.

[0181] Rules with a fit metric above the threshold are marked as adoptable, and rules below the threshold are marked as discarded. Adoptable rules are then added to the version merge queue.

[0182] In the version merging queue, conflict resolution is performed according to rule category and update time primary key. Rules with logical mutual exclusion in the same category are prioritized and selected to retain the version with high adaptability.

[0183] Perform consistency checks and engine compatibility tests on the merged rule set to ensure that the new version of the rules does not have execution errors or performance degradation under the current engine configuration, and generate version control metadata at the same time.

[0184] The rule set that passes validity verification and conflict resolution is encapsulated into the final effective verification rule update strategy object and written into the strategy storage module for direct loading and use in subsequent consistency verification processes of multi-source asynchronous room status event streams.

[0185] Through the above-mentioned validity verification and version merging process, the candidate strategy set from the previous step is transformed into a final effective rule update strategy that is free of logical conflicts and has verified adaptability, thereby achieving controllability of rule updates and immediate application effects.

[0186] For example, in a hotel PMS room status event synchronization system with 500 rooms, the candidate verification rule update strategy set includes 3 new conflict marking conditions, 2 dynamic time window sliding steps, and 1 source credibility decay coefficient. The system loads this strategy set, groups it, and performs sandbox playback verification on high-frequency failure mode clusters from the past 90 days. The total deviation D is 120, while the total deviation E is 280 without the strategy applied. The number of strategy verification samples T is 40. Substituting these values ​​into the formula yields a fit of -4. Analysis reveals that some candidate rules have increased deviation, and the overall fit is below the set threshold of -2, leading to their elimination. The retained rules are merged, and after conflict resolution, a strategy version containing only 1 conflict marking condition and 1 time window sliding step is generated. After engine compatibility testing, the final effective strategy object is generated and written to the strategy storage module. In subsequent room status event consistency verification, this strategy version significantly improves the accuracy of causal reordering under high-concurrency multi-source event streams, enhancing the verification strength of the consistency between the playback results and the actual room status trajectory.

[0187] Step S8: Adjust the parameters of the conflict marker set and source credibility decay model based on the verification rule update strategy. Specifically, this includes: S8.1: Parse and process the high-frequency failure mode feature set generated by cluster analysis in the feedback knowledge base, and extract specific failure scenario labels containing manually corrected timestamps later than polling coverage timestamps or callback difference summaries and link timestamp reverse offsets, in order to construct the input vector for the verification rule update strategy to guide parameter adjustment.

[0188] S8.2: Based on the specific failure scenario labels in the input vector of the verification rule update strategy, perform conflict marking logic reconstruction operation, dynamically modify the event source type priority mapping table and timestamp tolerance threshold parameter to generate an optimized conflict mark set that adapts to the current high-frequency failure mode.

[0189] When performing the conflict marking logic reconstruction operation, the input condition is the verification rule update strategy input vector output by step S8.1. This input vector contains specific failure scenario labels extracted through high-frequency failure mode clustering analysis. Based on this input vector, the failure scenario labels are first processed by semantic feature mapping, parsing the label content into three types of structured features: source type, time offset mode, and conflict coverage logic, to construct an intermediate feature set for rule adjustment. For the source type feature, the source priority mapping control module is called to perform weight increment / decrement operations on the priority mapping table for source type nodes that exhibit stronger coverage or greater deviation contribution in high-frequency failure modes, thereby increasing or decreasing the decision weight of this source type in conflict marking. For the time offset mode feature, the time difference statistics module is used to calculate the interval offset of the link timestamp set corresponding to the failure scenario, and a tolerance threshold update formula is adopted. The calculation result of this formula is used as a new parameter of the timestamp tolerance threshold and written into the corresponding time-series tolerance configuration field in the conflict marking conflict mark set. To address the characteristics of conflict coverage logic, and combining changes in source type priority with timestamp tolerance update results, a logical relationship reconstruction process is performed. Causal paths in failure modes that are prone to coverage errors are added to the decision condition set with mutually exclusive rules to constrain coverage behavior under the same target key. Through the aforementioned feature parsing, priority adjustment, tolerance threshold update, and logical mutual exclusion addition processes, the input vector from the previous step is transformed into a conflict flag set containing an optimized source priority mapping table and updated timestamp tolerance parameters, achieving an optimized decision logic for the current high-frequency failure modes.

[0190] S8.3: Utilize the time-series anomaly distribution features identified in the optimized conflict marker set to perform time window sliding step size calculation processing, and dynamically adjust the sliding granularity based on the rate of change of the timestamp distribution entropy value of event segments with multiple deviations under the same target key to generate a time window sliding strategy parameter set.

[0191] S8.4: Based on the effective observation interval determined by the time window sliding strategy parameter group, perform the derivation of the source confidence decay rate, and refit the exponential decay coefficient by combining the contribution weights of different source types in the failure mode to generate the updated source confidence decay model parameter matrix.

[0192] Based on the effective observation interval determined by the time window sliding strategy parameter set, the number of events occurring in each source type within this observation interval and the corresponding number of conflict markers are extracted to form a source event conflict index matrix. Contribution weight calculation is performed on the source event conflict index matrix, mapping the frequency of occurrence of each source type in high-frequency failure modes and the magnitude of the state deviation it causes to a contribution weight value using a normalized ratio. The contribution weight values ​​are then calculated with the corresponding elements of the initial source credibility decay coefficient. An exponential fitting method is used to establish a decay model of source credibility changing with time and conflict frequency, incorporating the timestamp distribution entropy value within the observation interval as a perturbation factor during model fitting. The decay rate coefficient is refitted based on the contribution weight value and the proportion of conflict events within the observation interval, using multiple regression analysis to simultaneously consider the impact of conflict frequency and state deviation magnitude on the decay rate. The fitted decay rate coefficient is combined with the initial credibility coefficient of each source type to form an updated source credibility decay model parameter matrix. Through exponential decay model refitting, the output parameters of the previous step are transformed into a credibility parameter matrix for dynamic response to high-frequency failure modes, achieving adjustment of source reliability in the consistency verification strategy.

[0193] For example, in the room status event consistency verification of a hotel PMS system, the time window sliding strategy parameter group sets the effective observation interval to 4 hours, and the source types include three categories: polling trigger, callback notification, and manual correction. Within this observation interval, there are 50 polling trigger events with 8 conflict markers, 40 callback notification events with 12 conflict markers, and 20 manual correction events with 15 conflict markers. The contribution weights calculated based on the number of conflict markers and the state deviation amplitude are 0.32, 0.45, and 0.23, respectively. The initial source credibility coefficients are 0.95, 0.92, and 0.88, respectively. Using the timestamp distribution entropy value of 0.76 within the observation interval as a perturbation factor, the exponential decay model formula is used to calculate the decay rate coefficient λ for each source. The fitted value for polling trigger is 0.08, the fitted λ value for callback notification is 0.11, and the fitted value for manual correction is 0.14. The source credibility decay parameter matrix was constructed by combining the λ value with the initial credibility coefficient as [[0.95,0.08],[0.92,0.11],[0.88,0.14]]. In the subsequent consistency verification, it was verified that this matrix can significantly improve the accuracy of conflict detection and the correctness of the baseline segment submission, and reduce the cumulative effect of state deviation under high concurrency.

[0194] S8.5: The optimized conflict mark set, time window sliding strategy parameter group, and updated source credibility decay model parameter matrix are integrated and written into the consistency verification engine configuration register to complete the self-evolutionary iterative deployment of the consistency verification strategy for real-time verification processing of subsequent multi-source asynchronous room status event streams.

[0195] In summary, this invention can construct a real-time closed-loop feedback mechanism for verification and playback. Through bidirectional data flow based on event segment state fingerprints and playback empirical data, it enables dynamic self-evolution of consistency verification strategies. This mechanism not only requires rapid identification of high-frequency deviations and abnormal patterns, but also the ability to perform real-time soft adjustments to conflict marking conditions, verification window parameters, and source credibility decay models in the verification rule base. This results in a low-maintenance, highly adaptive, and strongly verifiable event consistency verification system, better meeting the refined consistency assurance requirements in multi-source asynchronous high-concurrency environments.

[0196] 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.

[0197] 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.

[0198] 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 method for replaying room-state events based on event segment consistency checks, characterized in that, include: Acquire multi-source asynchronous room status event streams and generate event segments to be verified, including source type, collection batch number, link timestamp, and a summary of differences from the previous confirmed status; Perform a structure extraction operation on the event segment to be verified, parse out the initial state snapshot, the final state snapshot and the intermediate difference summary sequence, and construct the original segment state fingerprint; The original segment state fingerprint is subjected to pre-playback processing to reconstruct the local room state cache and output the pre-playback state trajectory. Then, a playback deviation metric is generated by comparing the pre-playback state trajectory with the original segment state fingerprint. For event segments to be verified whose playback deviation metric has been determined to exceed the preset fault tolerance threshold, a temporary causal dependency graph is constructed, and a reordering priority weight vector is generated accordingly, specifically: The difference summary sequence is parsed at the field change granularity to extract the state transition path code and key field change hash sequence corresponding to each room state event, and generate an atomic change feature set containing fine-grained operation semantics; Based on the atomic change feature set and the link timestamp distribution entropy value, conflict detection is performed to identify abnormal event pairs with state overlap or logical mutual exclusion relationships, and a conflict mark set carrying conflict type identifier and reverse offset is generated. A directed acyclic graph mapping mechanism is constructed using the conflict marker set and the source type credibility decay model. The atomic change feature set is mapped as nodes and the causal dependency relationship is mapped as edges, generating a temporary causal dependency graph that reflects the real business causal logic. Perform topology analysis and dominant change source identification calculation on the temporary causal dependency graph, determine the critical path weight and corrected priority coefficient of each node by combining the conflict type identifier, and generate an initial reordering priority weight vector. The weighted fusion process is performed based on the initial reordering priority weight vector and the playback deviation metric, and the weight distribution of high-conflict regions is dynamically adjusted to eliminate temporal logic contradictions, generating the final reordering priority weight vector to guide causal reordering correction. The causal reordering correction process is performed using the reordering priority weight vector to generate a baseline event segment, which is then submitted to the formal replay queue. Perform a full replay operation on the benchmark event segment and record the time sequence of room opening and closing actions. At the same time, collect cache reconstruction time and actual room status trajectory data and encapsulate them to generate a replay evidence package. The replay evidence package, along with the original segment metadata, is written into the feedback knowledge base, and the feedback knowledge base is periodically scanned. Cluster analysis is used to generate a verification rule update strategy. The conflict marker set and source credibility decay model parameters are adjusted based on the verification rule update strategy.

2. The method for replaying room status events based on event segment consistency check according to claim 1, characterized in that, The original segment state fingerprint undergoes pre-playback processing to reconstruct the local room state cache and output the pre-playback state trajectory. Then, a playback deviation metric is calculated by comparing the pre-playback state trajectory with the original segment state fingerprint. Specifically, this includes: Obtain the semantic state transition path code and key field change hash sequence contained in the original segment state fingerprint, construct a sandbox running space independent of the production environment using isolated memory mapping technology, and parse the semantic state transition path code into an executable room state change instruction set to initialize the sandbox context. Based on the room status change instruction set and the difference summary sequence in the event segment to be verified, simulated write operations are performed in the sandbox running space according to the timestamp distribution entropy value to reconstruct a local room status cache instance that reflects the theoretical evolution process of the event segment and generate a pre-replay state trajectory containing intermediate state snapshots. Feature extraction is performed on the final state snapshot in the pre-replay state trajectory to recalculate the verification state fingerprint in the sandbox environment. The key field change hash sequence in the verification state fingerprint is XORed bitwise with the corresponding sequence in the original segment state fingerprint to identify inconsistencies in the state. Based on the proportion of inconsistent state bits and the degree of deviation of timestamp distribution entropy, the weighted Euclidean distance is applied to calculate the multidimensional spatial distance between the pre-playback state trajectory and the original segment state fingerprint, thereby quantifying and generating a playback deviation metric that characterizes the severity of logical conflicts. The playback deviation metric is compared with a preset fault tolerance threshold. If the result is within the threshold, the event segment to be verified is marked as logically consistent. If the result is outside the threshold, an anomaly flag signal is triggered and the playback deviation metric is used as the input for subsequent causal reordering correction.

3. The method for replaying room status events based on event segment consistency check according to claim 1, characterized in that, The process of performing causal reordering correction using the reordering priority weight vector to generate a baseline event segment and submit it to the formal replay queue specifically includes: extracting the modification granularity identifier and conflict marker attribute of each room state event based on the difference summary sequence in the reordering priority weight vector and the event segment to be verified, and constructing a temporary causal dependency graph containing event nodes and directed dependency edges to quantitatively characterize the potential causal constraint relationship between multi-source asynchronous room state events.

4. The method for replaying room status events based on event segment consistency check according to claim 3, characterized in that, Also includes: A topological analysis process is performed on the temporary causal dependency graph. Based on the weight values ​​of each event node in the reordering priority weight vector, a global temporal conflict index is calculated to identify the root conflict event pairs that cause temporal logic conflicts and their reverse dependency paths.

5. The method for replaying room status events based on event segment consistency check according to claim 4, characterized in that, It also includes: using the root cause conflict event pair and the reverse dependency path, combined with the source type credibility decay model parameters, to perform a dynamic permutation operation on the arrival order of events within the event segment to be verified, generating a preliminary corrected event sequence that eliminates temporal logic contradictions.

6. The method for replaying room status events based on event segment consistency check according to claim 5, characterized in that, Also includes: Based on the preliminary corrected event sequence, the state transition continuity index within the segment is recalculated, and the corrected event flow is verified to meet the preset constraints. If the verification is successful, the final consistency verification successful event segment is generated.

7. The method for replaying room status events based on event segment consistency check according to claim 6, characterized in that, Also includes: The consistency verification passed event segment is encapsulated into a base event segment object, and a consistency verification passed identifier and reorder log metadata are attached. This object is then submitted to the formal replay queue to trigger the subsequent Codis room status cache reconstruction process.

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