An adaptive parsing and reconciliation method for multi-platform e-commerce bills

Abstract

This invention discloses an adaptive parsing and reconciliation method for e-commerce multi-platform bills, belonging to the field of e-commerce bill reconciliation technology. The method includes: circuit breaking and generating a frozen sample batch after detecting structural mismatch at the access gateway; extracting calibration anchors based on business serial numbers and timestamps, and performing identity residual verification on candidate mappings in conjunction with the total accounting benchmark; compiling the convergence results into a static parsing rule image and hot-writing the main parsing configuration; encapsulating normal discrepancies into embodied work orders and completing rights confirmation through phantom soft tokens, while forming unresolved unknown anomaly suspended data clusters from non-converged residuals; performing spatiotemporal topological clustering on the suspended data clusters and writing the macroscopic tolerance benchmark coefficient back to the identity residual constant pool. This method can suppress parsing interruptions caused by hidden interface modifications, reduce asynchronous reconciliation concurrency conflicts, and narrow the range of long-tail dead account blind spots caused by new charging rules.

Description

Technical Field

[0001] This invention relates to the field of e-commerce bill reconciliation technology, specifically an adaptive parsing and reconciliation method for bills from multiple e-commerce platforms. Background Technology

[0002] Billing data processing and reconciliation control are generally applied to the accounting processes between e-commerce platforms, merchant operation systems, payment and settlement systems, warehousing and distribution systems, and financial accounting systems. These processes include order receipt, platform service fee settlement, refunds, marketing subsidy allocation, inventory deduction matching invoice records, and subsequent accounting entries. With the increasing prevalence of multi-platform operations and distributed system architectures, upstream and downstream systems typically generate different transaction records from different service nodes. The reconciliation platform collects and compares these records according to different reconciliation rules and outputs the discrepancies, which are then manually verified or handled as an anomaly.

[0003] Chinese patent document CN114078045A describes a reconciliation method, system, electronic device, and storage medium. When a reconciliation task begins, it listens for event messages. Upon receiving the event message, the reconciliation platform retrieves the source data, target data, and reconciliation rules based on the theme. The source and target data are then used as parameters within the reconciliation rules to obtain the reconciliation result. The document also describes the process of first installing the reconciliation task, configuring the source data interface service, the target data interface service, and the corresponding reconciliation rules around the event theme. When the reconciliation rules need to be modified, they can be modified using a dynamically loaded programming language, and the modified rules can be dynamically loaded into the platform without restarting the platform. To avoid inaccurate source and target data due to incomplete business processes, the document allows for retrieving interface data at preset intervals after receiving event messages, and generates a failure log upon reconciliation failure. Overall, it is an event-driven reconciliation platform organized around event themes, interface services, and rule configuration.

[0004] While these technical approaches can address the lack of real-time updates in scheduled polling, their operation still relies on a relatively stable correspondence between the event theme, interface data structure, and reconciliation rules. In multi-platform e-commerce invoices, fields in external platform invoices may change due to variations in settlement methods, marketing overlaps, and cost splitting methods. Even if the interface call succeeds, field semantics can lead to column shifts, single-column splits, and multi-column merging. In such cases, the original rules are still set according to the existing theme and interface parameters. Delayed fetching only solves the problem of incomplete business processing and cannot completely resolve the inaccuracies caused by changes in field meaning. Moreover, when invoices contain cost formats that are not pre-configured, such as cross-store allocation and compound deductions, the system can only continue to generate abnormal results and rely on manual rule reconfiguration. In high-concurrency asynchronous reconciliation scenarios, the long-term accumulation of abnormal records can amplify state drift and processing conflicts in the review process.

[0005] Therefore, the key technical challenge is how to maintain the continuity of reconciliation processing under the conditions of frequent changes in billing structures across multiple platforms and the existence of unknown billing. Summary of the Invention

[0006] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides an adaptive parsing and reconciliation method for e-commerce multi-platform bills. It extracts calibration anchors based on business transaction numbers and timestamps, and performs identity residual verification on candidate mappings in conjunction with the total accounting benchmark. The convergence results are compiled into a static parsing rule image and hot-overwritten with the main parsing configuration. Normal discrepancies are encapsulated into embodied work orders and confirmed using phantom soft tokens. Simultaneously, non-convergent residuals are grouped into unresolved, unknown, and abnormal suspended data clusters. Spatiotemporal topological clustering is performed on these suspended data clusters, and the macroscopic tolerance benchmark coefficient is written back to the identity residual constant pool. This method can suppress parsing interruptions caused by hidden interface modifications, reduce asynchronous reconciliation concurrency conflicts, and narrow the long-tail dead account blind spot caused by new charging rules; thus solving the technical problems described in the background section.

[0007] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: An adaptive parsing and reconciliation method for e-commerce multi-platform bills, executed by a reconciliation server, includes: calculating the structural mismatch amount for the incoming bill messages, and cutting off the corresponding reading channel when the structural mismatch amount reaches the preset throttling condition, extracting abnormal source samples to form a frozen sample batch and loading it into an isolation verification sandbox, and outputting bill data according to the current parsing configuration when the structural mismatch amount does not reach the preset throttling condition. Based on the frozen sample batch extraction of business serial number and UTC timestamp as calibration anchor, the benchmark total amount is obtained by calling ERP accounting data, generating a candidate mapping draft matrix composed of field offset index, column position adjacency index and amount direction index, calculating the double debit and credit identity residual for each candidate mapping draft, retaining the target mapping draft that meets the convergence condition, and outputting the mapping draft that does not meet the convergence condition as a residual matrix. The target mapping draft is compiled into a static parsing rule image, written to the backup publication area, and the current parsing configuration is replaced by atomic pointer switching, so that the truncated channel can restore the output of normal comparison drop data stream; The normal comparison difference data stream is encapsulated into an embodied work order and bound to a ghost soft token. When the confirmation callback is received and the ghost soft token and version stamp are verified, the accounting field is atomically updated and the ghost soft token is cancelled. When the verification fails, the embodied work order is frozen. Based solely on the residual matrix, unresolved unknown anomaly suspended data clusters are formed. Topological clustering is performed according to time association, store domain association, and residual vector association to generate macro tolerance benchmark coefficients. These macro tolerance benchmark coefficients are then written as constant patches into the compound debit-credit identity residual constant pool for subsequent frozen sample batch verification.

[0008] Furthermore, the structure mismatch is calculated for the access billing message, including: normalizing the field name fragments, delimiter types, field length boundaries, and empty field placeholder patterns according to the receiving order to generate a structure fingerprint string; and performing a fixed hash operation on the structure fingerprint string to obtain the current structure hash signature. The current structure hash signature is compared bit by bit with the archived structure hash signature, and the structure mismatch is obtained by combining the field out-of-bounds count. When the structure mismatch reaches the preset cutoff condition, the mutation channel identifier is written and a frozen sample batch is formed. When the preset cutoff condition is not reached, the current parsing configuration is maintained.

[0009] Furthermore, the extraction of frozen sample batches and the generation of calibration anchors include: selecting consecutive abnormal rows, insertion abnormal rows and rollback retransmission rows in a sliding window based on the same mutation channel identifier, and encapsulating the original byte sequence, delimiter, empty field placeholder and field offset table into a read-only shadow copy. In the read-only shadow copy, candidate columns are filtered according to the business serial number rule and the UTC timestamp rule, and a calibration anchor point is formed when the columns in the same message line are simultaneously matched and pass the stability check of adjacent message lines.

[0010] Furthermore, the target mapping draft is compiled into a static parsing rule image, including: converting the column position, amount direction, and default fallback relationship in the target mapping draft into a set of field addressing edges, a set of amount direction edges, and a set of exception jump gates; and writing the set of field addressing edges, a set of amount direction edges, and a set of exception jump gates, along with the platform domain code, bill type, rule version number, and rollback pointer, into the version description header of the standby release area; When the grayscale batch review passes, the atomic pointer is switched; when the review fails, the current parsing configuration is maintained and the rollback pointer is returned.

[0011] Furthermore, the formation of unresolved unknown anomaly suspended data clusters and the writing of constant patches include: extracting high-order algebraic residual matrices, deriving time-series vectors, and store domain attribution keys from the residual matrix to form unresolved unknown anomaly suspended data clusters; The unresolved unknown anomaly data clusters are associated by time, store domain, and residual vector to generate homogeneous anomaly clusters; based on the homogeneous anomaly clusters, the unique patch slots corresponding to the platform domain code, store domain, and charging factor name are located, and constant patches are written to the mirror area before the version pointer is flipped.

[0012] Furthermore, the preset interception conditions are given by the header drift determination chain: first, the text header of the access bill message is subjected to case unification, whitespace folding and delimiter merging, and then the cosine similarity between the current text header and the historical stable header is calculated. When the cosine similarity is lower than the warning threshold, the corresponding reading channel is truncated and a frozen sample batch is formed. When the cosine similarity is not lower than the warning threshold, the billing data continues to be output according to the current parsing configuration.

[0013] Furthermore, the generation of the candidate mapping draft matrix adopts a two-track branch: when the cloud semantic branch is available, the column adjacency score between the field name fragment and the target business semantic slot is extracted and written into the candidate mapping draft matrix; When the semantic branch in the cloud is unavailable, extract the column stability scores corresponding to the length of the numeric string, the sign bit, and the empty field marker, and write them into the candidate mapping draft matrix; both branches are merged with the field offset table using the same column index as the key.

[0014] Furthermore, atomically updating accounting fields and canceling phantom soft tokens includes: first generating a lock level difference based on the amount difference and version stamp in the normal comparison gap data stream; When the lock level difference reaches the exclusive threshold, after verifying the phantom soft token, request a database row-level exclusive lock and perform accounting field updates; when the lock level difference does not reach the exclusive threshold, retain the phantom soft token and complete the accounting field updates by spinning according to the version snapshot.

[0015] Furthermore, the topology clustering and constant patch writing of unresolved unknown anomaly suspended data clusters include: when the core load of the topology evolution engine reaches a preset load threshold, compressing the time window into a short window of the most recent half hour, and transferring the constant patch to the queue to be signed while keeping the current constant pool unchanged; When the core load does not reach the preset load threshold, maintain the current sliding time window and continue to perform constant patch mirror area writing and version pointer flipping.

[0016] Furthermore, a specific work order must include at least an order key, a store key, an amount difference, a direction indicator, a callback address, a version stamp, and a work order status; the work order status is limited to pending confirmation, writing to the database, confirmed, frozen, and void; When the reconciliation server generates a embodied work order, receives a confirmation callback, and completes the update of accounting fields, it writes the corresponding work order status into the status field of the embodied work order.

[0017] Furthermore, the confirmation callback should at least include the work order key, token number, version stamp, callback time, and confirmation code; When the work order status is pending confirmation and the phantom soft token and version stamp verification pass, the reconciliation server returns a receiving status and triggers an update of the accounting fields; when the verification fails, it returns a reject status and switches the embodied work order to frozen.

[0018] Furthermore, the version description header includes at least the platform domain code, billing type, rule version number, generation time, target mapping draft identifier, and rollback pointer; when the reconciliation server writes to the standby release area, performs atomic pointer switching, and rolls back the current parsing configuration, it generates release log records containing version description header fields and release status fields, respectively.

[0019] Furthermore, when unresolved unknown anomaly suspended data clusters are written to the event bus, their message fields include at least the time period identifier, rule mismatch vector, store hash identifier, cluster sequence number, and topic identifier; The fields of a constant patch should include at least the patch identifier, the charging factor name, the applicable platform domain, the applicable store domain, the old constant value, the new constant value, and the rollback slot; and the corresponding patch status records should be output before and after writing to the mirror area.

[0020] (III) Beneficial Effects This invention provides an adaptive parsing and reconciliation method for bills from multiple e-commerce platforms, which has the following beneficial effects: By collecting heterogeneous data and performing preprocessing such as circuit breaking and interception of underlying features, billing messages with structural hash addressing mismatches are first frozen and isolated in the verification sandbox, preventing abnormal formats from directly reaching the backbone parsing pipeline and preventing the cleaning link from crashing and the spread of abnormal batches from the source.

[0021] Through core feature extraction and layer-by-layer deduction using basic financial identity algorithms, a rigid correspondence is established between business transaction numbers and timestamps and the total amount of the deep accounting benchmark. Candidate mappings are no longer constrained by header guessing but by the identity constraint of business-finance consistency, increasing the determinism of heterogeneous bill parsing. Adaptive static calibration dimensionality reduction and boundary overflow defense lines are used to define the converged target mapping draft as a static parsing rule image and hot-overwrite the main parsing configuration, balancing small-sample trial and error in abrupt situations with high-throughput processing capabilities after recovery.

[0022] The release of basic command responses is separated from the deep, high-order unknown residual pool. Normal comparison discrepancies are transformed into embodied work orders and bound to phantom soft tokens, forming a lightweight closed loop for rights confirmation callbacks. This avoids the risks of dirty writes and duplicate write-offs in asynchronous reconciliation. The non-convergent residual matrix is ​​decrypted into unresolved, unknown, and suspended anomaly data clusters, retaining the high-order algebraic residual matrix, derived time-series vectors, and store domain attribution keys. This creates high-quality and sustainably accumulated anomaly material for subsequently finding hidden charging rules, preventing long-tail bad debts from directly impacting manual reconciliation.

[0023] By reverse-evolution and global reconstruction of high-order topological clustering based on suspended state characteristics, the macro-tolerance benchmark coefficient is written back to the constant pool of the double-entry accounting identity formula, forming a two-way closed loop of pre-analysis, adaptive calibration and back-end evolution, gradually eliminating the analysis blind spots caused by unknown new business regulations. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the overall process of an adaptive parsing and reconciliation method for multi-platform e-commerce bills according to the present invention. Figure 2 This is a schematic diagram of the process of generating variant channels and frozen samples in this invention; Figure 3 This is a schematic diagram of the extraction of absolute calibration anchor points for two-dimensional business and the process of identity trial calculation and diversion in this invention; Figure 4 This is a schematic diagram of the dimensionality reduction compilation and static hot overwrite process of the convergent candidate mapping draft in this invention; Figure 5 This is a schematic diagram illustrating the splitting process between the normal difference confirmation branch and the unknown difference suspension branch in this invention. Figure 6 This is a schematic diagram of the spatiotemporal topology evolution and constant patch write-back process of unresolved unknown anomaly suspended data clusters in this invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figures 1-6 This invention provides an adaptive parsing and reconciliation method for multi-platform e-commerce bills, including: The following actions are executed collaboratively by the data access gateway server, session keep-alive proxy, verification sandbox server, and cold buffer storage server. First, high-frequency mutation messages are extracted from the backbone parsing chain, and upstream connections, anomaly source samples, and subsequent business and financial deduction boundaries are simultaneously fixed. In particular, covert modifications to multi-platform bills often manifest as broken header structures, field shifts, and boundary violations. If the old rules are still applied at the entry point, any subsequent identity calculations will be built on the corrupted data base.

[0027] Step 1: When the external billing interface undergoes a hidden structural modification, first cut off the mutation channel by triggering the structural hash signature mismatch, then extract abnormal source samples in a controlled depth and encapsulate them into a frozen sample batch, and the upstream session is not reclaimed, separating the normal data that can be circulated from the abnormal data that needs to enter the verification sandbox.

[0028] E-commerce platform billing messages are not static objects. The same platform may subtly change field order, placeholders for empty fields, and the way amount columns are split under different settlement dates, store groups, and marketing campaigns. If the data access gateway server still uses the existing parsing configuration tree, it may misidentify a single amount column as a memento, or even write the entire batch of messages with misplaced fields into subsequent links, making it impossible for the backend to determine whether the error originated from the interface, mapping, or database table. Therefore, Step 1 does not undertake the task of interpreting business meaning, but instead establishes an entry-level physical watershed: as soon as the structure hash signature is found to no longer match the existing template, the system marks the connection session as a mutated channel, preventing it from continuing to penetrate the backbone pipeline with the old rules. Simultaneously, the system does not immediately close the upstream session, but instead provides a sampling window for the subsequent verification sandbox to maintain the original context. Its principle is not the traditional retry after an error, but rather splits connection maintenance and data freezing into two parallel links. The former serves session continuity, and the latter serves the purity of abnormal samples. The two links converge at the entry point to output a single batch of frozen samples.

[0029] In this implementation, the input object is the raw bill message stream from the e-commerce platform interface. The message format can be a text frame carried by delimiters or an array frame with field names. The execution body first completes the receiving buffer, field offset expansion and structure hash signature comparison by the data access gateway server, then the session keep-alive proxy maintains the upstream idle window according to the same connection handle, then the verification sandbox server receives the frozen sample batch, and finally the cold buffer storage server takes over the temporary storage when memory is tight.

[0030] The entire action chain follows the sequence of trigger determination - throttling and freezing - isolation loading - keep-alive continuation - degradation and outgoing. The output of the previous action is always the direct input boundary of the next action: only connection handles marked as mutation channels will generate frozen sample batches, only frozen sample batches will be loaded into the verification sandbox, and only connection handles whose verification sandbox construction window has not yet ended will be continued by the session keep-alive agent.

[0031] In this process, after reading each frame of the original billing message, the data access gateway server does not immediately interpret the business fields. Instead, it normalizes the header characters, separator positions, field length boundaries, and null value placeholder patterns into a structural hash signature. Then, the gateway compares the current structural hash signature bit by bit with the archived structural hash signature corresponding to the platform and billing type, and uniformly converts any found field out-of-bounds, type out-of-bounds, and position out-of-bounds errors into out-of-bounds unit counts. This ensures that any covert alterations manifest as a calculable trigger value at the front end, rather than tracing the source after an error occurs in the back end.

[0032] Preferably, the gateway completes the comparison and throttling within the same event loop, preventing the mutated channel from continuing to push subsequent frames into the old parser before the comparison is completed. Wherein: Where: structural mismatch tensor : Indicates the overall degree of mismatch between the current original billing message and the archived structure hash signature, with a value ranging from a real number greater than or equal to 0; matching bit length : Indicates the number of times the current structure hash signature and the archive structure hash signature are consistent in the same order. The value is a positive integer and does not exceed the number of archive bits. Archive bit length : Represents the total number of bits in the hash signature of the archive structure, with a value range of positive integers; number of out-of-bounds units. : Represents the total number of cells in the current original billing message that have exceeded the field length, field type, or field position limits; the value must be an integer greater than or equal to 0; trigger threshold : Indicates the preset threshold for circuit breaker action, and its value range is a real number greater than 0; Current number of fields : The number of fields in the expanded original billing message, taken as a positive integer to suppress misleading results from different message lengths; Structure mismatch tensor Crossing the startup threshold Subsequently, if the structure hash signature of the current billing message is found to be inconsistent with the structure hash signature of the archive, or if a field is out of bounds or a column is shifted, the data access gateway server will issue a structure hash mismatch exception signal and record the current connection handle as a mutated channel.

[0033] As this criterion continues, the gateway immediately sends subsequent read requests belonging to that mutated channel to the circuit breaker, rather than to the main parser. The backbone pipeline still processes packets that have not entered the mutated channel, while the mutated channel remains fixed at the ingress boundary, preventing the misalignment field from propagating to subsequent segments.

[0034] The trigger-type thresholds, release-type thresholds, and write-back-type thresholds described in this article are not manually input but are generated by binding the platform domain code, bill type, and version number. For each type of threshold, a stable sample set is first established, consisting of historical bills that have completed rights confirmation, have not triggered circuit breakers, and have not entered the pending unknown anomaly data cluster. Then, a historical sequence is formed for the judgment quantity corresponding to the threshold, and an initial threshold value is obtained according to the preset threshold generation rules. Once the initial threshold value is generated, it is written into the version description header and corresponds one-to-one with the platform domain code, bill type, and version number. During runtime, the threshold value is only allowed to be updated without changing the formula structure, and the updated threshold value should still be bound to the original platform domain code and bill type.

[0035] The version description header must include at least the following fields: platform domain code, bill type, rule version number, generation time, convergence candidate mapping draft identifier, field offset summary, rollback pointer, and release status. The statically resolved rule map must include at least the set of field addressing edges, the set of amount direction edges, the set of default rollback edges, and the set of exception jump gates. After the backup release area is written, the data access gateway server first verifies whether the platform domain code, bill type, and rule version number in the version description header are closed, and then performs an atomic pointer flip, causing the entry parsing thread to switch entirely to the new release area. The old release area remains in a read-only rollback state until it passes the gray-scale release review in the new release area and is not allowed to be overwritten.

[0036] For platforms where generating structured hash signatures is inconvenient, for example, the text header can first be normalized, delimited, and whitespace folded before being handed over to the cosine drift monitor; once the drift result crosses the same trigger threshold... The system then performs the same mutation channel interception action as described above. Therefore, although the structure hash signature mismatch and text header drift use different pre-detection methods, their output objects always remain the same mutation channel identifier and the same connection handle.

[0037] Once a mutation channel is registered, the data access gateway server does not move all the messages of that channel into the verification sandbox. Instead, the sample source selector first extracts abnormal source samples that can represent the form of the stealth modification from the same connection handle.

[0038] The verification sandbox requires a small number of original samples sufficient to reproduce structural fractures, rather than the full message with unbounded expansion. Without prior depth constraints, the verification sandbox could be dragged into another congested main chain by high-frequency mutation flows. Preferably, the sample source selector simultaneously extracts consecutive abnormal lines, insertion abnormal lines, and backtracking retransmission lines, maintaining their order in the original receive buffer. This ensures that in step two, during subsequent extraction of the two-dimensional service absolute calibration anchor point, the true field adjacency and temporal adjacency relationships can still be observed. Specifically: Where: cutoff batch depth : Indicates the number of rows of outlier samples included in the frozen sample batch, with a value range of a closed interval. Integers within; anomalous aggregation factor : Indicates the arrival density of abnormal rows in the variant channel within the current sampling window, with a value range greater than 1. Real numbers; tolerance for accidental collisions : This represents the upper bound of tolerance for the sample source selector to misinterpret occasional noise as a stable stealth modification mode. The value range is a real number greater than 0 and less than 1. Among them, the number of abnormal rows : This refers to the number of message lines marked as abnormal under the current mutation channel identifier; it is obtained by accumulating the count within the sampling window using the sample source selector; sampling window duration. : Refers to the length of time covered by the current round of sampling executed by the sample source selector; obtained by the difference between the start and end timestamps of the sampling.

[0039] The cutoff depth is obtained based on the above relationship. Subsequently, the gateway extracts the corresponding number of anomaly source samples from the mutation channel and encapsulates the original byte sequence, delimiter, empty field, field offset table, and connection handle together into a frozen sample batch. The frozen sample batch is then sent to a verification sandbox, which is separate from the main parser in address space. The verification sandbox uses a read-only loading method to create a shadow copy, without performing any rearrangement, pruning, or type rewriting on the original bytes. During the generation of the field offset table, the data access gateway server writes the start byte, end byte, delimiter type, and empty field marker for each column into the sequential index area. When loading the frozen sample batch, the verification sandbox first verifies whether the sequential index area is closed with the original byte length before opening it for reading in step two. For text frames carried by delimiters, the field offset table records column boundaries; for array frames, the field offset table records key name positions and key-value spans. Both types of messages are ultimately merged into the same frozen sample batch handle.

[0040] Preferably, the verification sandbox carries the frozen sample batch through a memory-mapped file. The shadow copy holds the field offset index but not the write handle to the write-back backbone pipeline. Therefore, even if step two is subsequently performed in the verification sandbox, it will not pollute the entry buffer. Taking a daily settlement message temporarily modified by the platform as an example, the commission column, which was originally adjacent to the business order number, was split into two amount columns, and the refund mark was moved to the end. After the data access gateway server identifies the mismatch in the structure hash signature, it extracts the abnormal source sample, which is a mixture of consecutive abnormal rows and rollback retransmission rows, from the same connection handle, encapsulates it into a frozen sample batch, and sends it into the verification sandbox. The message after entering the verification sandbox still maintains the original column order and the original empty fields. Therefore, the subsequent steps see the isolated original form, rather than a copy that has been reorganized by the entry logic.

[0041] Therefore, the depth of the interception batch Together with the frozen sample batch, they form the trial boundary of step two, which both suppresses the loading scale of the verification sandbox and preserves the original form of the anomalous source samples.

[0042] If the upstream e-commerce platform reclaims the connection according to the existing idle time limit while the mutation channel is blocked and the frozen sample batch is still loading, the context of the same batch of secretly modified packets will be cut off. Although subsequent steps may obtain the frozen sample batch, they will lose the opportunity to continue fetching adjacent samples or obtaining supplementary frames. To avoid this interruption, the session keep-alive proxy sends a keep-alive packet to the upstream interface along the same connection handle during the verification sandbox construction. The application layer response status of the keep-alive packet is set to HTTP202Accepted.

[0043] The session keep-alive proxy does not forge the upstream response status code in the request message; instead, it sends a lightweight keep-alive request along the existing connection handle. This lightweight keep-alive request includes at least a session identifier, keep-alive type, and local timestamp, but does not carry business billing fields. The upstream interface returns its native success response when the connection remains active; the status code of this native success response is any one of HTTP200, HTTP202, or HTTP204. The data access gateway server only records the status code and connection keep-alive field in the upstream native success response and updates the connection liveness time accordingly. If no upstream native success response is received for two consecutive keep-alive cycles, the circuit breaker remains in the open state, and subsequent read flows corresponding to the mutated channel identifier are stopped.

[0044] The data access gateway server first performs pre-normalization processing on the original billing message. This pre-normalization processing includes: standardizing field name case, folding consecutive whitespace into a single delimiter, compressing consecutive delimiters into a single delimiter marker, uniformly marking empty fields as fixed placeholders, and numbering fields according to the order of receipt. After pre-normalization, a structured fingerprint string is formed by concatenating field name fragments, delimiter types, field length boundaries, and empty field placeholder patterns. A fixed hash operation is then performed on this structured fingerprint string to obtain a structured hash signature. This fixed hash operation remains unchanged under the same platform domain code and billing type.

[0045] Preferably, the session keep-alive agent and the circuit breaker share the same connection index table, so the keep-alive action only applies to registered variant channels and does not interfere with the settlement rhythm of normal channels. Wherein: Where: Keep-alive interval : Represents the time interval between two consecutive keep-alive messages, and its value is a real number greater than 0; upstream idle cutoff window : Indicates the duration for which the upstream interface allows the connection to remain idle without new service packets; the value is a real number greater than 0; upstream idle cutoff window Read the connection timeout configuration or session configuration from the upstream interface; Sandbox construction window : Represents the time required for the verification sandbox to receive the frozen sample batch and load it into read-only mode; a real number greater than or equal to 0; safety shrinkage amount. : This represents the lead time reserved to mitigate network round-trip jitter, thread scheduling delays, and gateway queuing; it is greater than or equal to 0 and less than 0. Real number; safe shrinkage amount This is a fixed value, indicating that it includes network round-trip jitter and thread scheduling lag.

[0046] When the host machine detects that the stack utilization has reached the preset avalanche threshold, the verification sandbox will no longer accept new frozen sample batches. The data access gateway server will then transfer any frozen sample batches that have not yet been loaded to the cold buffer ring. The cold buffer ring consists of sequentially written solid-state disk segments connected end to end. The header of each segment records the mutation channel identifier and the original offset, the body records the original frozen sample batch text, and the tail records the verification summary. Once the verification sandbox is restored to usability, the samples will be re-loaded in segment order.

[0047] Preferably, the data access gateway server, session keep-alive proxy, and cold buffer ring are deployed on the same access node, while the verification sandbox resides in a separate, isolated address space. The available memory share of the verification sandbox is one-eighth to one-fifth of the available memory on the access node, ensuring that the frozen sample batch retains its original byte format without encroaching on the resident space of the main parser. Message reception can be accomplished by an NIO event loop, eBPF hooks, or a protocol proxy with equivalent functionality. The output objects of this implementation include a mutation channel identifier, a frozen sample batch handle, a field offset table, and session keep-alive status bits.

[0048] Finally, four results can be checked by replaying mutation messages from the same platform: whether the mutation channel was separately intercepted at the inlet, whether the frozen sample batch maintained its original column order in the validation sandbox, whether the upstream connection persisted during the validation sandbox construction, and whether the backbone pipeline did not come into contact with the frozen anomalous source samples. The beneficial effects obtained along this action chain are clearly corresponding: the session keep-alive proxy addresses context interruption, the cold buffer ring addresses sample preservation during memory avalanches, and the read-only loading of the validation sandbox addresses secondary contamination of the inlet buffer. These three, in conjunction with the aforementioned structure hash triggering and batch depth control, enable the frozen sample batch output from step one to both be replayed and sent to step two.

[0049] The following actions are completed collaboratively by the verification sandbox server, ERP accounting server, candidate mapping solver, and session keep-alive agent. Step two takes the frozen sample batch, field offset table, mutation channel identifier, and session keep-alive status bit output from step one. Instead of attempting to directly restore the full invoice, it first extracts a two-dimensional business absolute calibration anchor point within the verification sandbox that is sufficient to cross-domain index the ERP accounting base. Then, it generates a candidate mapping dictionary draft matrix around this anchor point and pushes the double-entry debit accounting identity formula onto the front-end trial chain as the residual decision benchmark.

[0050] Therefore, the text layer column name guessing, the amount layer column position matching and the business and finance layer benchmark comparison are converged on the same algebraic verification chain. Step three receives the convergent candidate mapping draft, and step four receives the non-convergent residual matrix.

[0051] Step 2: Freeze the absolute calibration anchor points of the two-dimensional business in the sample batch to obtain the benchmark total amount. Use the candidate mapping dictionary draft matrix to drive the trial calculation of the double-entry accounting identity formula. The convergent candidate mapping draft and the non-convergent residual matrix can be separated from the validation sandbox.

[0052] Step one has already frozen the original billing message corresponding to the variant channel identifier within the verification sandbox. Therefore, step two does not deal with a mixed site-wide transaction history, but rather a frozen sample batch with known boundaries, undetermined column positions, and pending business meaning. If columns are still guessed directly based on manual experience at this point, and candidate columns are mistakenly merged into revenue items or cross-store allocation items are mistakenly treated as main transaction amounts, any subsequent reconciliation actions will be based on incorrect mappings.

[0053] To avoid this kind of front-end guessing pollution, step two should select two-dimensional business absolute calibration anchors that do not drift with implicit changes to the table headers. Prioritize combinations of business serial numbers and UTC timestamps, as business serial numbers can cross-domain index settlement records in the ERP accounting base and can re-pin split return messages from the same store and order chain back to the point of occurrence. Only after the two-dimensional business absolute calibration anchors are closed across the frozen sample batch, field offset table, and ERP accounting server return results can the subsequent candidate mapping dictionary draft matrix have an external benchmark, rather than relying on text inference.

[0054] In this embodiment, the verification sandbox server first scans the frozen sample batch column by column according to the field offset table, and extracts the two-dimensional business absolute calibration anchor point using the 24-bit fixed-length business serial number rule and the UTC timestamp format rule. The candidate mapping solver then initiates a read-only query to the ERP accounting server with the two-dimensional business absolute calibration anchor point to obtain the baseline total amount corresponding to the business serial number and the UTC timestamp. After that, the cloud semantic branch or the local discrete fingerprint branch generates a candidate mapping dictionary draft matrix. The candidate mapping solver substitutes the matrix into the compound debit and credit accounting identity formula one by one, performs residual approximation and convergence judgment, and sends the convergent candidate mapping draft to step three, and sends the non-convergent residual matrix to step four.

[0055] Specifically, the frozen sample batch first generates two-dimensional business absolute calibration anchor points, then generates the benchmark total amount, then constrains the candidate mapping dictionary draft matrix, and finally forms the converged and non-converged results after the split. If the previous step is not valid, the next step will not be started. Therefore, there is no inverted processing of guessing the mapping first and then supplementing the benchmark.

[0056] The candidate mapping dictionary draft matrix is ​​a two-dimensional sparse matrix. The matrix row index corresponds to the input column to be judged in the frozen sample batch, and the matrix column index corresponds to the target business semantic slot. Each matrix element records at least: column index, original field name fragment, positive / negative direction of amount, empty field marker, number string length, column adjacency score, and column stability score. Before generating the matrix, the business serial number column and UTC timestamp column are fixed according to the field offset table and do not participate in subsequent enumeration. The candidate mapping solver only performs column and direction swaps within the remaining amount column set; after each swap, the identity residual is called. With residual convergence Perform a review and remove residual convergence. Above the residual convergence threshold The cloud-based semantic branch outputs column adjacency scores, while the local discrete fingerprint branch outputs the length of the numeric string, the sign bit, and the null field marker. The two branches are merged at the matrix level using the same column index as the key, and column semantics cannot be generated independently of the field offset table.

[0057] After receiving the frozen sample batch, the verification sandbox server first restores the original byte boundaries of each column according to the field offset table, and then performs a fast screening on each column using a strong regular expression engine. The preferred business serial number rule is a 24-bit fixed-length structure, where the first 2 bits are the platform domain code, the middle 8 bits are the date segment, and the last 14 bits are the sequence segment; the preferred UTC timestamp rule is a second-level time format with a zero-offset time zone marker. The verification sandbox server does not directly treat the hit result as the absolute anchor point for the two-dimensional business, but continues to verify the adjacency stability of the two under the same message line, adjacent message lines, and the same mutation channel identifier, avoiding mistaking pseudo-numeric strings in certain remarks columns as real index keys.

[0058] Furthermore, the text object that looks like a business order number is further compressed into a business index object that can be closed together with the field offset table and ERP records.

[0059] Where: Anchor point closure degree : Indicates the degree of closure between the business serial number and the UTC timestamp in the frozen sample batch, field offset table, and ERP returned records. The value range is a real number greater than or equal to 0 and not exceeding 1; Business serial number hit rate. : Indicates the hit rate of the 24-digit business serial number rule in the specified column of the frozen sample batch, with a value range of real numbers greater than or equal to 0 and not exceeding 1; timestamp hit rate : Indicates the hit rate of UTC timestamp rules in the specified column of the frozen sample batch, and the value range is a real number greater than or equal to 0 and not exceeding 1; Field drift distance : Represents the cumulative column offset of the same candidate column between adjacent message lines, a real number greater than or equal to 0; field drift distance This is the cumulative sum of the absolute values ​​of the column index differences of the same candidate column between adjacent message lines. If the difference lies between two lines, the column index of the candidate column is... and Then the drift distance increment is .

[0060] Drift penalty coefficient : Indicates the field drift distance versus anchor point closure The compressive strength, taking values ​​from real numbers greater than 0; natural base. : Represents the mathematical base of the exponential decay term, with a fixed value; used to represent the field drift distance. Convert to a monotonically decreasing closed-loop penalty; When anchor point closure Crossing Anchor Threshold At that time, the verification sandbox server encapsulates the corresponding business serial number and UTC timestamp into a two-dimensional business absolute calibration anchor point, and initiates a read-only query to the ERP accounting server; among which, the anchor point threshold : Indicates the minimum threshold that the absolute calibration anchor point of the two-dimensional business is allowed to enter the ERP query chain. The value range is a real number greater than 0 and not exceeding 1; When the ERP accounting server returns the results, in addition to the baseline total, it also returns the key identifiers of the settlement currency, debit item set, and credit item set. However, step two does not immediately use these key identifiers to rewrite the frozen sample batch, but only uses them as reference boundaries for subsequent residual trial calculations.

[0061] For example, in a frozen sample batch from a certain platform, the original commission column was split into platform service fee and activity rebate, the transaction number remained in the fourth column, and the UTC timestamp was moved to the ninth column. The verification sandbox server first locked the two columns based on the field offset table, and then retrieved the baseline total amount of the transaction in the settlement base from the ERP accounting server. Therefore, it is no longer necessary to rely on manual judgment to determine which column name is more like revenue; instead, the baseline total amount is given first, and the candidate columns are then verified around that baseline total amount.

[0062] Among these features, the field offset table restores the original column boundaries, the strong regular expression engine eliminates false anchors, and the anchor closure is adjusted. To compress the scope of cross-domain queries and ensure that ERP queries are derived from stable indexes in the frozen sample batch rather than inferences from drifting text, the candidate mapping solver only begins processing column semantics and amount shapes after obtaining the baseline total amount. This is because within the same frozen sample batch, multiple amount columns are only similar in shape to the total amount in a local sense. Only after accommodating the baseline total amount do algebraic constraints emerge regarding which columns should be included in the candidate columns for predicting positive income and which should be included in the candidate columns for predicting negative deductions.

[0063] Preferably, the candidate mapping solver generates a column name adjacency list from a cloud-based semantic branch and a monetary column shape table from a local discrete fingerprint branch, then merges them to form a draft candidate mapping dictionary matrix. The cloud-based semantic branch obtains complete sentences, which are column name fragments segmented by a field offset table; the local discrete fingerprint branch obtains not abstract labels, but rather the length of the number string, the position of the plus or minus sign, the position of the decimal point, the position of the thousands place, and the number of empty field placeholders. The two branches are summarized at the matrix level, and each candidate column simultaneously possesses two sets of evidence: column name adjacency relationships and monetary shape relationships. Specifically: Where: identity residual : Represents the difference between the benchmark total and the income deduction combination corresponding to the candidate mapping dictionary draft matrix, with a value in the range of real numbers; Benchmark total : Represents the accounts receivable settlement benchmark value returned by the ERP accounting server based on the absolute calibration anchor point of the two-dimensional business, with a value range of real numbers; Predicts the set of candidate columns for positive revenue. : Represents the set of column indices that are assigned to the positive income direction under the current candidate mapping dictionary draft matrix, and the range of values ​​is a finite discrete set; Predict the set of candidate columns for reverse deduction : Represents the set of column indices that are assigned to the reverse deduction direction under the current candidate mapping dictionary draft matrix, with values ​​ranging from a finite discrete set; candidate column amount Candidate column amount : Represent the set of positive income candidate columns respectively With the set of reverse deduction candidates The corresponding column represents the amount value in the frozen sample batch, and the value range is real numbers; positive column index. : Represents the set of candidate columns for predicting positive income. The column position number in the index is a positive integer; the inverse column index is a column number. : Represents the set of candidate columns for predicting reverse deductions The position number of a single column in the array, with values ​​ranging from positive integers; Furthermore, after obtaining the identity residual Then, the candidate mapping solver continues to filter out divergent branches according to the convergence criterion: Where: residual convergence : indicates identity residual relative to the benchmark total The normalized deviation, taking values ​​of real numbers greater than or equal to 0; used for the residual convergence threshold. Comparison; Identical Residual : Same meaning as before; used to reflect the deviation of the current candidate mapping dictionary draft matrix; benchmark total amount : Same meaning as above; used to provide a normalization benchmark; normalization buffer size : Indicates avoiding the benchmark total amount The protection amount for denominator instability when approaching zero, with a value range greater than [value missing]. Real numbers; used to maintain residual convergence. Computability; Residual convergence threshold : Indicates the upper bound on which the candidate mapping dictionary draft matrix is ​​allowed to be considered as a convergent candidate mapping draft, with values ​​greater than 0 and strictly less than 10. －6 real numbers; When the residual convergence Not higher than the residual convergence threshold When the current candidate mapping dictionary draft matrix is ​​retained as a convergent candidate mapping draft, the residual convergence is... Above the residual convergence threshold At that time, the candidate mapping solver only retains its column attribution relation and identity residual. And write it into the non-convergent residual matrix.

[0064] Specifically, the candidate mapping solver preferably employs depth-first enumeration combined with Monte Carlo tree search pruning, or it can use Hungarian matching to determine the initial column affiliation before performing local swaps; both tools use identity residuals and residual convergence Instead of solely determining the output based on column name text similarity, this serves as a stopping condition.

[0065] For example, when the frozen sample batch contains four columns: platform service fee, activity rebate, cross-store subsidy, and net settlement amount, the cloud-based semantic branch might treat the latter three as revenue-related columns, while the local discrete fingerprint branch would find that one of the columns consistently carries a negative sign. The candidate mapping solver would then merge the two and substitute them into the identity residual. The trial calculations ultimately only retained the columns with negative signs in the predicted reverse deduction candidate column set. The draft.

[0066] Furthermore, the cloud-based semantic branch is responsible for proposing the column name adjacency hypothesis, the local discrete fingerprint branch is responsible for proposing the amount direction hypothesis, and the double-entry loan accounting identity formula is responsible for determining whether the hypothesis is valid. The combination of the three can transform what kind of column to whether it is closed after substitution.

[0067] Furthermore, if none of the candidate mapping dictionary draft matrices pass the residual convergence threshold... Step two does not directly send the frozen sample batch to manual processing; instead, it first organizes all divergent branches into a non-convergent residual matrix. This matrix records at least the candidate column attribution and the identity residual. Residual convergence The two-dimensional business absolute calibration anchor point, mutation channel identifier, and field offset table index position are used in step four to further extract the higher-order algebraic residual matrix and derived time series vector.

[0068] Preferably, the non-convergent residual matrix is ​​stored in row-major sparse order, with the matrix rows corresponding to the numbers of the candidate mapping dictionary draft matrix and the matrix columns corresponding to the column affiliations of the tried columns. The purpose of this encapsulation is not to repeatedly recalculate in step two, but to pass the failed forms to subsequent links as is, avoiding the smoothing out of the long-tail structure in step two. For scenarios with limited network access or cloud inaccessibility, the candidate mapping solver preferably disables the cloud semantic branch and retains only the local discrete fingerprint branch. For scenarios with sufficient computing power, the candidate mapping solver preferably enables two branches simultaneously and adds a column adjacency penalty term in addition to the initial matrix.

[0069] In one implementation, the verification sandbox server runs in a Linux kernel user-space isolated container. The strong regularization engine uses PCRE2, and the candidate mapping solver employs a root-finding process combining a sparse matrix library and Newton's iteration. Equivalent tools are also suitable for replacement. The ERP accounting server returns the baseline total amount via a read-only connection, without writing any business status back to the frozen sample batch. During project acceptance, the following evaluation chain is checked: first, check whether the absolute calibration anchor point of the two-dimensional business can close between the frozen sample batch and the ERP return; then check the residual convergence. Can the candidate mapping dictionary draft matrix be separated into a convergent candidate mapping draft and a non-convergent residual matrix? Finally, check whether the non-convergent residual matrix still retains the field offset table index position and the mutation channel identifier.

[0070] Therefore, the boundaries of the output objects in step two are clear: the convergent candidate mapping draft is used for dimensionality reduction and hot reloading in step three, and the non-convergent residual matrix is ​​used for suspension and separation in step four. The two output chains have been separated at the end of this step. The following actions are performed by the verification sandbox server, candidate mapping solver, static compiler, data access gateway server, and circuit breaker controller.

[0071] Step 3: After stripping the dynamic attributes from the converged candidate mapping draft obtained in Step 2, compile it into a static parsing rule image, then write it back to the main parsing configuration tree in a controlled hot overwrite manner, and release the circuit breaker interception after confirming that the discharge boundary is stable, so that the backlog of flow corresponding to the mutated channel identifier can be restored to parsing according to the new rules.

[0072] Step two has proven that a certain set of column affiliations can make the benchmark total amount The amounts in the frozen sample batch are closed under the double-entry bookkeeping identity, but this closure remains within the validation sandbox and still relies on the candidate mapping solver, column adjacency relations, and multiple trial branches working together. If this high-dimensional deduction state is maintained throughout the full pipeline, the entry resolution of the data access gateway server will continue to be constrained by the candidate mapping solver. Although the mutation channel identifier has obtained a converged answer, it still has to bear the computational overhead of batch-by-batch trial calculations.

[0073] Therefore, step three separates the proven mapping relationship from the solution process introduced to find that mapping relationship, keeping only the former in the main parsing configuration tree and leaving only the latter in the verification sandbox as a one-time solution trace. The principle is not relearning, but rather abstracting the convergent candidate mapping draft into field addressing edges, amount direction edges, and default backoff edges, then folding it into a static parsing rule image with fixed key values, fixed order, and fixed decision gates, so that subsequent entry parsing returns to a state of single-step addressing, single-decision, and single-path output.

[0074] In this implementation, the static compiler receives the convergence candidate mapping draft, field offset table, mutation channel identifier, and baseline total. Identity residual Residual convergence From this, select the field mapping skeleton that can exist on the main parsing configuration tree for a long time; delete the column name semantic labels, discrete fingerprint cache, and branch search traces generated by the candidate mapping solver, and retain the column position, amount direction, symbol attribution, rollback order, and abnormal jump gate; the data access gateway server receives the static parsing rule image through the double-buffered publishing area and hot overwrites it with atomic pointers; the circuit breaker controller restores the backlog flow corresponding to the mutation channel identifier according to the grayscale release order, and at the same time commands the feature probe daemon to exit the preemption state.

[0075] In this process, after reading the convergent candidate mapping draft, the static compiler does not copy the entire draft into the main parsing configuration tree as is. Instead, it first strips away all transient objects related to trial calculations. In this implementation, transient objects include column name semantic vectors, discrete fingerprint caches, search tree node numbers, residual trial trajectories, and intermediate labels returned by cloud semantic branches. The static compiler retains only three types of objects: first, field addressing edges, which indicate the key placement of a given input column in the main parsing configuration tree; second, amount direction edges, which indicate whether the column belongs to the revenue or deduction direction when entering funds; and third, default fallback edges, which indicate alternative forwarding paths when input columns are missing, out of order, or have reversed signs.

[0076] Furthermore, the convergent candidate mapping draft is transformed from a multi-branch derivation result into a finite-state image.

[0077] Where: static image fidelity : Represents the degree of preservation of the convergent candidate mapping draft after stripping transient objects and compressing it into a static analytic rule map; its value is a real number greater than or equal to 0 and not exceeding 1; residual convergence. : Indicates the current convergent candidate mapping draft in step two relative to the baseline total amount The normalization deviation, taking values ​​of real numbers greater than or equal to 0; residual convergence threshold. : This indicates that the candidate mapping dictionary draft matrix allowed in step two is considered an upper bound of the convergent candidate mapping draft, with values ​​greater than 0 and strictly less than 10. －6 real numbers; Folding depth : Represents the number of mapping levels in the convergent candidate mapping draft that are compressed into the static parsing rule map, with a value ranging from an integer greater than or equal to 1; folding depth The difference between the number of mapping levels in the convergent candidate mapping draft before dimensionality reduction compilation and the number of static key-value levels after compilation. Depth penalty coefficient. : Indicates fold depth For still image fidelity The attenuation intensity, taking values ​​of real numbers greater than 0; natural base. : Represents the mathematical base of the exponential decay term, taken as a fixed constant, used to calculate the folding depth. Transformed into continuous decay; When static image fidelity Not lower than the compilation threshold At this time, the static compiler writes the field addressing edge, the amount direction edge, and the default backoff edge into the abstract syntax tree object, and then serializes the abstract syntax tree object into flat key-value pairs to form a static parsing rule map; when the static map fidelity is Below the compilation threshold At this point, the convergent candidate mapping draft is returned to the validation sandbox and does not enter the main parse configuration tree.

[0078] Among them, compilation threshold : Indicates the minimum static image fidelity allowed before hot overwrite in the convergent candidate mapping draft. The value of is a real number greater than 0 and less than 1, used to limit distortion after folding.

[0079] For example, in step two, the net settlement amount column of a frozen sample batch should be mapped to the main ledger amount key, the platform service fee column should be mapped to the deduction amount key, and the refund replenishment column should be mapped to the default rollback edge. The static compiler first deletes the column name semantic vector and search tree trajectory, transforms the above three columns into serialized key-value pairs with fixed keys and fixed directions, and the static parsing rule image after compilation only contains the addressing order and jump gate required by the entry point.

[0080] Then, the data of transient objects is stripped so that the trial calculation state in the verification sandbox no longer enters the main parsing configuration tree; field addressing edges, amount direction edges, and default fallback edges are retained so that the gateway entry can still perform column misalignment and sign flipping; static image fidelity By using compilation thresholds to constrain folding boundaries, the results of dimensionality reduction compilation are still executable and do not lose convergence relationships.

[0081] After the static resolution rule image is formed, the data access gateway server does not directly rewrite each item on the current main resolution configuration tree. Instead, it first writes the new image to the standby publication area. Preferably, the main publication area and the standby publication area each occupy an independent page group and use the same version description header to record the mutation channel identifier, rule version number, field offset table summary, and generation time. After the standby publication area is loaded, the data access gateway server calls an atomic pointer toggle instruction, causing the entry resolution thread to jump from the old publication area to the new publication area within a pointer switching cycle.

[0082] If item-by-item rewriting is used, the entry parsing thread may read unfinished rules during the rewriting process, causing some packets to be parsed using the old keys and others using the new keys. Specifically: In the formula; the hot rewrite gate value : Represents the gating result of the combined effects of static parsing rule mapping closure deviation and hot rewrite jitter on the grayscale batch, with values ​​ranging from real numbers greater than or equal to 0; grayscale residual : Represents the identity residual obtained by recalculating the static analytical rule map on the grayscale batch, with a value range of real numbers; Gray scale base total amount : Represents the accounts receivable settlement benchmark value returned by the ERP accounting server corresponding to the grayscale release batch, with a value range of real numbers; normalized buffer size. This represents the protective amount used in step two to prevent the denominator from becoming unstable when the base total approaches zero; its value range is greater than... A real number; used to maintain the hot rewrite gating value. Computability; Hot rewrite jitter coefficient : Represents the instantaneous jitter caused by double-buffered handover, page group switching, and pointer toggling to the entry thread; the value is a real number greater than or equal to 0; used to explicitly account for the impact of memory handover on gating results; hot-write jitter coefficient. It is obtained by the ratio of the number of parsing failures that occurred before and after the grayscale release batch switched to the total number of packets in the grayscale release batch.

[0083] Hot rewrite gate value Not exceeding the release threshold When the circuit breaker controller increases the grayscale discharge ratio to full discharge, the hot-write gate value... Above the release threshold At that time, the data access gateway server maintains the circuit breaker half-open state and rolls back to the previous version of the stable memory image; Release threshold : Indicates the highest thermal rewrite gate value that allows the transition from grayscale discharge to full discharge after thermal rewrite is completed. , whose value range is a real number greater than 0; used to constrain the release rhythm; In this implementation, the grayscale release batch is no longer reconstructed from the validation sandbox, but instead, front-end messages are obtained from the backlog of messages corresponding to the mutation channel identifier in arrival order. The data access gateway server obtains the new release area to query the front-end messages. The candidate mapping solver does not perform closure verification or addressing. If the verification passes, the circuit breaker controller increases the release ratio again in this order. If the verification fails, it switches back to the old release area, leaving a spare release area for the next compilation reload. The double-buffered release area isolates the memory writes of the old and new rules; atomic pointer flipping eliminates intermediate state reads that modify each item sequentially; and hot-write gating values ​​are used. With release threshold The release time is jointly limited, and the inlet release is based on closed-loop verification.

[0084] Once the static parsing rule image completes hot overwrite and grayscale verification, it no longer preempts the entry parsing thread. The data access gateway server sends a suspend command to the operating system, freezing the feature probe daemon while retaining the heartbeat and version monitoring threads. Because the solution process for converging the candidate mapping draft in step two has ended, if the feature probe daemon continues to preempt frequently, data that should have been directly accessed by the static parsing rule image will undergo useless branch probing again. Simultaneously, the circuit breaker controller performs tiered de-interception: first, it limits the read-side flow of the mutated channel, then it unlocks the write-side queuing lock, and finally, it releases the session keep-alive proxy associated with that channel.

[0085] Preferably, the above three actions are performed in a fixed sequence, and at each timing node, the availability of the previous version of the stable memory image is checked. If page segment verification or pointer out of bounds occurs, the process is immediately reversed.

[0086] Where: discharge coefficient : Indicates the fidelity of a static image The discharge intensity after interacting with the backlog queuing window, taking values ​​greater than or equal to 0 and not exceeding 1; static image fidelity. : Same meaning as before; used to reflect the degree to which the statically analyzed rule map after dimensionality reduction and compilation preserves the convergent candidate mapping draft; backlog queuing window : Indicates the duration for which the backlog of traffic corresponding to the variant channel identifier waits to be resolved after the circuit breaker; the value is a real number greater than 0; Stability holding window : Indicates the duration for which the newly released area remains unchanged after grayscale verification, and the value range is a real number greater than 0; When the discharge coefficient Not lower than the release order threshold When the circuit breaker controller releases the blockade in the following order: read-side current limiting - write-side queuing lock - session keep-alive agent; when the discharge coefficient... Below the release order threshold At that time, the circuit breaker controller only retains grayscale discharge; discharge order threshold : Indicates the minimum discharge coefficient at which the circuit breaker controller switches from gray-scale discharge to staged release. The value range is a real number greater than 0 and not exceeding 1; In one implementation, the static compiler is deployed on the release node of the same rack as the verification sandbox server. The release node uses the Linux operating system and adopts a memory release mechanism that supports memory-mapped page groups and atomic reference switching. After the static resolution rule image is loaded in the standby release area, the entry resolution thread is switched to the new release area as a whole through an indivisible reference update action.

[0087] The primary release area and the backup release area each have their own independent version description header and rollback pointer; the stable memory image of the previous version is stored in an independent image area and does not share write handles with the verification sandbox.

[0088] When a backlog of pipelines is already waiting in the queue, after the release node completes the writing of the new image, the gateway thread first sees the pointer flip, then sees the feature probe daemon enter a frozen state. Subsequently, the circuit breaker controller sequentially releases the interception on the read side, write side, and session side. Finally, the backlog of pipelines passes through the main parsing configuration tree continuously according to the new key, and the output is a normal comparison drop data stream. During project acceptance, four objects can be checked in sequence to see if they are all true: whether the new release area has taken over the static parsing rule image, whether the old release area still retains the rollback path, whether the feature probe daemon only leaves the heartbeat thread and the version monitoring thread, and whether the backlog of pipelines corresponding to the mutation channel identifier is no longer sent back to the verification sandbox.

[0089] Furthermore, suspending the feature probe daemon process causes the entry point resolution to return to a fixed path; disabling the circuit breaker interception at each stage restores the read, write, and session boundaries sequentially; the discharge coefficient... Release order threshold Together, we can determine when to fully open the system and set an executable threshold, thereby ensuring a stable and normal comparison data flow for step four.

[0090] The following actions are executed collaboratively by the reconciliation control server, business terminal, cache server, database server, event bus server, and on-chain fixed nodes.

[0091] Step 4: Convert the normal comparison discrepancy data stream into a embodied work order that can be callbacked, has confirmed weights, and can be concurrently isolated. Then, encapsulate the non-convergent residual matrix into a cluster of unresolved, unknown, and suspended anomalies. This allows the atomic accounting of known discrepancies and the suspension separation of unknown discrepancies to be completed simultaneously in the same stage.

[0092] Step three has compressed the convergent candidate mapping draft into a static parsing rule image. Therefore, the normal comparison difference data stream subsequently output by the data access gateway server no longer contains entry-level noise such as field misalignment, and can be directly sent to the difference object for business confirmation. However, these difference objects are not suitable for direct writing to the database because manual review, system callback, duplicate submissions, and network jitter will occur simultaneously at the business terminal. If coarse-grained exclusive locks or two-phase commit long transactions are still used on the database server, the connection pool will be slowed down during peak periods. At the same time, the non-convergent residual matrix generated in step two cannot be manually consumed along with normal differences, because it retains deep residual information such as rule dimension loss, new cost items, and temporal resonance. If it is covered by ordinary work orders here, step five will lose the high-level material for reverse-engineering new rules.

[0093] Therefore, step four, on the one hand, compresses normal differences into embodied work orders and completes application layer authorization through ghost soft tokens; on the other hand, it cuts off unknown differences from the regular flow and maintains their original residual structure, time structure and topological attribution structure.

[0094] In this implementation, the reconciliation control server first reads the order key, store key, amount difference, direction identifier, and callback address from the normal reconciliation gap data stream, and encapsulates them into an embodied work order. The cache server then issues a phantom soft token for each embodied work order and binds the phantom soft token to the callback address, version stamp, and expiration time. After the business terminal displays the embodied work order, the operator submits a confirmation callback. The reconciliation control server first verifies the phantom soft token, and then calls the cascading atomic script to complete the database table overwrite and phantom soft token cancellation. The reconciliation control server receives the non-convergent residual matrix, extracts the higher-order algebraic residual matrix, derived time-series vector, and store domain key from it, writes it into the pending unknown anomaly suspended data cluster, and then forwards it asynchronously by the event bus server. The action chain maintains unidirectional connection: the normal reconciliation gap data stream only enters the embodied work order branch, and the non-convergent residual matrix only enters the pending unknown anomaly suspended data cluster branch. The two branches do not intersect at the field layer, storage layer, or message layer.

[0095] A phantom soft token must include at least a work order key, token number, version stamp, issuance time, expiration time, and holding status. Version drift coefficient. It is calculated from the callback version stamp and the current version stamp of the embodied work order.

[0096] The cascading atomic script executes in the following order: First, it reads the work order key, work order status, version stamp, and phantom soft token; only when the work order status is "pending confirmation," the phantom soft token is valid, and the version stamp matches the version stamp carried in the callback, does it enter the accounting field overwrite stage; then, it writes the confirmed accounting fields to the database server and synchronously updates the work order status to "writing to database"; after the database server returns success, it updates the work order status from "writing to database" to "confirmed" in the same script context and cancels the phantom soft token; if any precondition is not met, or the accounting field overwrite fails, the script only returns a rejection result and does not perform any database table updates. The status transition of the embodied work order is limited to: pending confirmation → writing to database → confirmed, pending confirmation → frozen, or pending confirmation → invalid; the confirmed status cannot be transitioned back to the pending confirmation status.

[0097] After receiving the normal reconciliation discrepancy data stream, the reconciliation control server does not immediately write it to the database server. Instead, it converts each discrepancy record into a embodied work order. An embodied work order is a type of digital entity object with a clear business carrier. Its object header includes the order key, store key, amount difference, direction identifier, callback address, version stamp, generation time, and work order status; its object body includes the parsing source, field offset summary, and discrepancy description; and the object tail records the phantom soft token index bit.

[0098] Therefore, the business terminal does not encounter an abstract exception number, but rather a physical work order that directly points back to the source of the bill, the callback path, and the current version. The cache server immediately issues a phantom soft token after the physical work order is successfully created. This phantom soft token does not rely on the database server's native table locking mechanism, but resides in the cache server's high-speed storage area and is bound to the physical work order.

[0099] In the formula: the validity of the phantom soft token : Indicates the stability of the application-layer authorization qualification maintained by the current phantom soft token in a concurrent callback environment, with a value ranging from a real number greater than or equal to 0 and not exceeding 1; Competition callback number : Represents the number of repeated callbacks received within a unit time window for the same work order; the value range is an integer greater than or equal to 0; contention decay coefficient. : Indicates the number of competition callbacks Validity of phantom soft tokens The compressive strength, taking values ​​that are real numbers greater than 0; Lease Length : Indicates the duration from issuance to natural expiration of the phantom soft token, with a value ranging from a real number greater than 0; callback round-trip time. : Represents the actual time from when the business terminal displays the embodied work order to when the reconciliation control server receives the confirmation callback; the value is a real number greater than 0; natural base. : Represents the mathematical base of the exponential decay term, taken as a fixed constant; used to calculate the number of competing callbacks. Transformed into continuous decay; When the ghost soft token is valid Not lower than the token threshold At that time, the reconciliation control server retains the current phantom soft token and allows the business terminal to submit a confirmation callback; when the phantom soft token is valid... Below the token threshold At that time, the reconciliation control server freezes the specific work order and waits for subsequent lock level difference determination.

[0100] Token Threshold : Indicates the validity of the phantom soft token The minimum threshold allowed to enter the rights confirmation callback chain, with a value range of real numbers greater than 0 and not exceeding 1; used to define when application-layer rights confirmation is still reliable.

[0101] For example, if the data sent by the discrepancy object comes from a store, and the business terminal displays it as a shortfall in the net settlement amount for an activity refund, when the operator clicks confirm, the reconciliation control server does not directly write the data to the books. Instead, it first checks whether the phantom soft token corresponding to this embodied work order in the cache server is within the lease term. It is not occupied by other terminals.

[0102] Furthermore, embodied work orders solidify the discrepancy object into a referential entity; phantom soft tokens bring concurrent contention to the cache server for processing; phantom soft token validity... With token threshold Together, they define when entry into the ownership confirmation callback chain is allowed, thus suppressing the risk of dirty writes caused by repeated commits.

[0103] After the business terminal submits the confirmation callback, the reconciliation control server does not split it into multiple separate transactions for gradual execution. Instead, it calls a cascading atomic script to complete four actions within the same execution window: First, it verifies whether the status of the embodied work order is still pending confirmation; second, it verifies whether the phantom soft token matches the version stamp; third, it writes the confirmed accounting results to the database server and updates the work order status; and fourth, it cancels the phantom soft token from the cache server.

[0104] Preferably, the cascaded atomic script is a single-instance composite LuaEval script, which is issued once by the reconciliation control server and executed collaboratively by the connection proxy between the cache server and the database server.

[0105] In this process, writing to the database server first and then deleting the phantom soft token, or deleting the phantom soft token first and then writing to the database server, will expose an intermediate state window. To accommodate the different impacts of large and small tail differences, step four introduces lock level drift control: for embodied work orders with large amount differences, in addition to the phantom soft token, a row-level exclusive lock will be placed on the database server; for embodied work orders with small amount differences, optimistic spinning will be maintained on the in-memory version snapshot. Specifically: Where: Lock level difference : Indicates the decision value for which concurrency control level should be used for the current specific work order; the value is a real number greater than or equal to 0; used to select the execution path among application-layer spectral soft tokens, row-level exclusive locks, and version snapshot spin; normal drop value : Indicates the amount difference corresponding to this specific work order in the current normal comparison difference data stream, and the value range is real number; Benchmark Total : Represents the accounts receivable settlement benchmark value returned by the ERP accounting server in step two, with a value range of real numbers; used as a normal difference value. Provide a normalized baseline; normalized buffer size : This indicates the step two used to avoid the benchmark total amount The protection factor against denominator instability when the value approaches zero, taking values ​​that are real numbers greater than 0; version drift coefficient. : Indicates the degree of change in the version stamp from the creation of the specific work order to the arrival of the confirmation callback; the value range is a real number greater than or equal to 0. When lock level difference Not lower than the exclusivity threshold At that time, the reconciliation control server requests a row-level exclusive lock from the database server before executing the cascading atomic script; when the lock level difference... Below the exclusivity threshold At that time, the reconciliation control server maintains the application-layer phantom soft token and uses version snapshot spin to complete the write; exclusive threshold : Indicates the lock level difference The minimum threshold for triggering a row-level exclusive lock, with values ​​ranging from real numbers greater than 0; In the enhanced implementation, after the cascading atomic script is executed, the reconciliation control server pushes the hash signature digest of this rights confirmation process to the on-chain solidified node, forming an irreversible solidified copy. In the degraded implementation, if a brief network interruption occurs between the database server and the cache server, the embodied work order status opportunity will switch to the local message retry queue, and the cascading atomic script will be replayed after the connection is restored, while the spectral soft token remains frozen during this period. Furthermore, the cascading atomic script compresses the database table overwriting and spectral soft token cancellation into the same execution window; lock level difference. Large differences and small tail differences are allowed to enter different concurrent paths; the enhancement and degradation implementation methods respectively cover on-chain solidification and network interruption scenarios.

[0106] For objects that failed to converge in step two, step four will no longer attempt to package them into embodied work orders. This is because the core issue with these objects is not whether the business terminal confirms them, but rather that the existing identity constraints themselves lack dimension. Therefore, the reconciliation control server extracts three layers of retained information from the non-convergent residual matrix: the first layer is a higher-order algebraic residual matrix, used to retain the deviation relationships between candidate columns; the second layer is a derived time-series vector, used to retain the continuous occurrence of similar anomalies over time; and the third layer is the store domain affiliation key, used to retain the clustering boundaries of anomalies in the store and merchant domains.

[0107] Subsequently, the reconciliation control server encapsulates the high-order algebraic residual matrix, derived time-series vector, and store domain affiliation key into the pending unknown anomaly suspended data cluster. The pending unknown anomaly suspended data cluster includes header fields, body fields, and tail fields. The header fields must at least contain a time period identifier, rule mismatch vector, and store hash identifier; the body fields must at least contain the original column affiliation relationship, residual source column, and version description information; and the tail fields must at least contain an event bus topic identifier and a suspended status identifier. Specifically: Where: suspension separation : Represents the fit strength of the current non-convergent residual matrix into the unresolved, unknown, anomalous, suspended data cluster; its value is a real number greater than or equal to 0; identity residual : This indicates that the current non-convergent residual matrix in step two is relative to the baseline total amount. The algebraic difference, taking values ​​in the range of real numbers; the benchmark total amount : Represents the accounts receivable settlement benchmark value returned by the ERP accounting server in step two, with a value range of real numbers; used for the identity residual. Provide a normalized benchmark; Normalized buffer size : This indicates the step two used to avoid the benchmark total amount The protection factor against denominator instability when the denominator approaches zero, taking values ​​of real numbers greater than 0; missing dimension counting. : Represents the number of regular dimensions in the current non-convergent residual matrix that cannot be mapped to the existing identity constraint constant pool; the value is an integer greater than or equal to 0; missing dimension count. This refers to the number of fee columns in the current non-converging residual matrix that cannot be mapped to the constant pool of the existing compound debit and credit accounting identity formula. Match count : Indicates the number of rule dimensions that the current non-convergent residual matrix can map to the existing identity constraint constant pool; an integer with a value equal to 0; Match count. This refers to the number of toll columns in the existing constant pool that the current non-convergent residual matrix can map. The mapping is based on the target business semantic slots that have been solidified in the convergent candidate mapping draft of step two.

[0108] When suspension separation Not lower than the suspension threshold At that time, the reconciliation control server encapsulates the object into a pending unknown exception suspended data cluster and sends it to the event bus server; when the suspension separation degree Below the suspension threshold At that time, the reconciliation control server leaves the object in the manual entry queue to await further entry.

[0109] Hang up the threshold : Indicates suspension separation The minimum threshold allowed to enter the pending unknown anomaly hanging data cluster, with a value range of real numbers greater than 0; used to distinguish between ordinary anomalies and unknown rule anomalies.

[0110] In one implementation, the reconciliation control server, cache server, and event bus server are deployed in the same availability zone, while the database server is deployed independently. Embedded work orders are stored using both relational tables and in-memory indexes, while pending, unknown, and suspended anomaly data clusters are stored using both JSON objects and sparse vectors. In real-world operations, when a platform adds cross-store / cross-industry cost sharing during a promotional period, the business terminal will only see the embodied work orders that can already be interpreted by static parsing rules; those still displaying high-dimensionality missing counts will be ignored. and Gao Heng et al. residuals The objects are directly transcribed into pending unknown exception hanging data clusters within the reconciliation control server, and then the event bus server sends them to subsequent topics. The business terminal does not intervene in this branch.

[0111] Furthermore, suspension separation Unknown rule exceptions are separated from ordinary work order chains; the unresolved unknown exception suspended data clusters retain three types of information: algebraic layer, time layer, and store layer, providing purity input for step five; business terminals do not need to passively process difference objects that cannot be explained by existing rules, and the manual verification pool will not be encroached upon by long-tail new rules.

[0112] After the patch takes effect, the revision residuals of other similar samples to be verified in the verification sandbox For the original identity residual The increase rate is less than the increase value, and the proportion of branches entering the convergent candidate mapping draft is increased. The reconciliation control server separates the pending unknown anomaly hanging data clusters and embodied work orders at the entry point, reducing the number of discrepancy objects that business terminals encounter that cannot be explained by the existing constant pool.

[0113] The following actions are executed collaboratively by the event bus server, topology evolution engine, graph storage server, constant pool manager, verification sandbox server, and cloud master node.

[0114] Step 5: Using only unresolved, unknown, and suspended abnormal data clusters as input, construct a spatiotemporal connectivity topology graph and obtain the macroscopic tolerance benchmark coefficient. Then, encapsulate the macroscopic tolerance benchmark coefficient as a dynamic rule constraint constant patch and write it in reverse into the constant pool of the compound lending and borrowing accounting identity formula, so that subsequent similar long-tail dead accounts can obtain a new convergence boundary in the sandbox.

[0115] Step four has already separated known and unknown differences. The embodied work order branch is responsible for accounting, while the pending unknown anomaly data cluster branch is responsible for preserving the failure status. If, at this point, the pending unknown anomaly data cluster is still handed over to manual classification, the system remains in a passive mode of manually patching problems after they are discovered.

[0116] Especially when cross-store and cross-industry cost sharing, hidden gambling deductions, and activity rebate penetration deductions occur simultaneously, a single abnormal amount difference cannot directly tell the system which rule constant is missing, because the stable signal is not within a single record, but within a cluster relationship where the same time slice, the same store domain, and the same deduction shape repeatedly co-occur. Therefore, step five uses high-order algebraic residual matrices, derived time-series vectors, and store domain affiliation keys as research objects, and reassembles the same type of failure into a topological form that is aggregateable, propagable, and reversible in the graph storage server.

[0117] In this implementation, the event bus server listens for pending unknown anomaly suspended data cluster topics. Upon receiving an object, the topology evolution engine first generates nodes and edges according to the time period identifier, rule mismatch vector, and store hash identifier. Nodes represent a single pending unknown anomaly suspended data cluster, and edges represent associations that simultaneously satisfy connectivity conditions in three dimensions: temporal adjacency, store domain adjacency, and residual vector adjacency. Subsequently, the topology evolution engine selects the execution engine based on the local aggregation results: deployment environments with GPU computing arrays call the graph neural network branch, while low-computing-power environments call the DBSCAN branch. Both output the macroscopic tolerance benchmark coefficient of the homogeneous anomaly cluster. The constant pool manager then writes the candidate value as a dynamic rule constraint constant patch, which, after patch gating verification, is written in reverse to the constant pool of the duplex debit and credit accounting identity formula in the verification sandbox server; if the gating fails, the system remains suspended; if the host machine load continues to reach its peak, it switches to the short-window fitting and CFO physical key verification branch.

[0118] After the event bus server sends the pending, unknown, and suspended anomaly data clusters into the topology evolution engine, the engine first performs three normalization assemblies. The first normalization assembly sorts objects by time period identifier and cuts them into sliding time slices; the second normalization assembly maps objects to the store domain index table by store hash identifier; the third normalization assembly expands the rule mismatch vector into column vectors of a higher-order algebraic residual matrix. After assembly, the graph storage server generates a spatiotemporally connected topology graph, where nodes store source object identifiers, time slice numbers, store domain numbers, and residual vector indices, and edge objects store temporal adjacency values, store domain affinity values, and residual direction consistency values.

[0119] Preferably, the time slice length is a discrete window from the most recent half hour to the most recent six hours, sliding in 5-step increments; the store domain number is generated by combining the store hash identifier with the platform domain code; the residual vector adjacency is determined by cosine projection and co-directional sign bit. For data centers with GPU computing arrays, the graph neural network branch reads the adjacency matrix and propagates it multi-hop; for edge nodes without GPU computing arrays, the DBSCAN branch scans the node density in the sliding time slice using the neighborhood radius and core point threshold. Wherein: Where: Topology trigger value : Represents the aggregation intensity of a local community, taking a real number greater than or equal to 0; number of residual components : Represents the total number of residual components participating in the aggregation decision, with a value greater than or equal to 1; component weight. : indicates the first The contribution intensity of each residual component, taking the value of a real number greater than or equal to 0; residual components : indicates the first The deviation value corresponding to each residual component is a real number; used to reflect the amount offset of unknown rules; the base total amount. : This represents the accounts receivable settlement benchmark value returned by the ERP accounting server in step two, and the value is a real number; Normalized buffer size : Represents the protection quantity in step two, with a value greater than 0; used to prevent denominator instability; reference time window : Represents the length of the historical reference time, with a value greater than 0; used to provide a reference for trigger speed; current aggregation window. : Represents the duration of a local community, and takes the value of a real number greater than 0; used to reflect whether an anomaly is short-lived; When topology trigger value Not lower than the trigger threshold When the topology evolution engine marks the local community as a homogeneous anomalous community and enters the macroscopic tolerance baseline coefficient solution chain; when the topology trigger value... Below the trigger threshold At that time, the local community remains in the graph storage server. The trigger threshold... : Indicates the minimum topology trigger value for switching from the observer state to the solver state. , which takes the value of a real number greater than 0; used to prevent reverse write-back caused by occasional noise.

[0120] For example, different stores might simultaneously generate pending, unknown, or suspended data clusters with continuously decreasing net settlement amounts, and the direction of these decreasing amounts aligns with the activity return column. After the topology evolution engine attaches these objects to the same store domain index table, it first triggers the data through topology trigger values. To determine if the region is stable enough, three types of fields are used to prevent one-dimensional clustering errors; graph neural network branches and DBSCAN branches are used for different computing power scenarios; topology trigger value. and trigger threshold Used to limit the solution time.

[0121] Once a homogeneous anomalous cluster is identified, the topology evolution engine first reverse-engineers the macro-level tolerance benchmark coefficient. This macro-level tolerance benchmark coefficient is not a local error of a single anomalous event, but rather the stable deduction or recovery ratio exhibited by the homogeneous anomalous cluster under repeated exposure conditions. To obtain this coefficient, the topology evolution engine regroups the nodes in the cluster according to candidate charging factors. These candidate charging factors include at least cross-store cost-sharing factors, activity return factors, and hidden deduction factors.

[0122] Within each group, the identity residuals retained in step two are then... The column vectors of the higher-order algebraic residual matrix retained in step four, along with the edge connectivity in the current community, are fed into the solver.

[0123] The core of this implementation does not lie in a specific solver name, but rather in the fact that candidate mapping results or homogeneous anomalous community results must be constrained by the aforementioned identity residual decision chain, and use the same set of business objects and version objects as input. The aforementioned solver tool is merely an engineering path for implementing this decision chain under different computing power conditions, and does not change the core concept of this invention: constraining candidate mappings with prior business financial benchmarks and revising the constant pool in reverse with subsequent community results.

[0124] For example, the solver can employ Newton iteration, quasi-Newton methods, or coordinate descent with boundary constraints, and its stopping condition is to output a single value or a small set of constants that can be written into the constant pool of the recursive borrowing and lending accounting identity formula.

[0125] Where: Macroscopic tolerance benchmark coefficient : Represents the stability tolerance constant of the candidate charging factor, taking the value of a real number greater than or equal to 0; used as the core value for patching; number of community samples. : Indicates the number of unresolved, unknown, or suspended anomaly data clusters participating in the solution; the value is an integer greater than or equal to 1; used to limit the summation range; sample weight. : indicates the first The contribution weight of each community sample is a real number greater than or equal to 0; the sample residuals : indicates the first The identity residuals corresponding to each community sample are real numbers, used to reflect the explanatory gaps in the existing constant pool. Exposure : indicates the first The exposure base of a community sample under the candidate charging factor, taking a real number greater than or equal to 0; used to convert residuals into tolerance constants; normalized buffer. The meaning is the same as before; it is used to avoid instability in the denominator. Furthermore, the macroscopic tolerance benchmark coefficient is obtained. Then, the constant pool manager constructs a new compound debit and credit accounting identity: Where: revised residual : Indicates the injection macroscopic tolerance benchmark coefficient The resulting new residuals, which are real numbers, are used to determine whether the patch has narrowed the original explanation gap; the baseline total amount. : Same meaning as above; used to provide a baseline; a set of candidate columns for predicting positive income. : Represents the set of positive income column indexes in step two, with values ​​being a finite discrete set used to preserve the positive accumulation structure; Predict the set of candidate columns for reverse deduction : Represents the set of indexes for the reverse deduction column in step two, with values ​​being a finite discrete set used to preserve the reverse accumulation structure; candidate column amount Candidate column amount : Represents the monetary values ​​of the positive income column and the negative deduction column, respectively, and the values ​​are real numbers; Macroeconomic tolerance benchmark coefficient : Same meaning as before; used to write the stable charging factor back into the identity formula; implied charging exposure. : Represents the exposure level of the current sample to be verified under the new charging factor, and takes the value of a real number greater than or equal to 0; used to convert constants Coupled with specific samples; Exposure For the first The sum of the absolute values ​​of the target fee columns corresponding to the current candidate fee factor in each cluster sample; the set of target fee columns is derived from the intersection of the common columns of homogeneous abnormal clusters and the co-directed columns of the rule mismatch vectors. If the candidate fee factor points to only a single column, then Take the absolute value of the amount in that single column; if the candidate charging factors point to multiple columns, then... Sum of the absolute values ​​of the amounts in these columns. Implied fee exposure. This represents the signed sum of the amounts belonging to the target fee column set in the current sample to be verified. The constant pool manager establishes a unique patch slot for each group of platform domain code, store domain, and fee factor name; dynamic rules constrain constant patches to only be written to patch slots that simultaneously match the platform domain code, store domain, and fee factor name. When writing a patch, it is first written to the mirror area and a new version number is generated, then the version pointer is flipped, and the original slot retains a rollback pointer for patch retraction.

[0126] At the patch object level, the constant pool manager sets the macroscopic tolerance baseline coefficient. The charging factor name, applicable platform domain, applicable store domain, version number, and rollback pointer are packaged into a dynamic rule constraint constant patch. The patch header contains the patch identifier, source community identifier, and generation time period identifier; the patch body contains the replacement path, old constant value, and new constant value; and the patch tail contains the rollback slot and applicable scope.

[0127] Furthermore, the macro tolerance benchmark coefficient Obtained from the community layer rather than a single sample layer; revised residuals. Connect the identity equation in step two with the write-back action in step five into the same parameter chain; the dynamic rule constraint constant patch provides rollback pointers and ranges, so that cross-layer write-back has a clear landing point.

[0128] After receiving the dynamic rule constraint constant patch, the constant pool manager does not directly overwrite the constant pool in the verification sandbox server. Instead, it performs patch gating. The goal of patch gating is to confirm the revised residuals on a limited verification sample. There was indeed a contraction, and it was confirmed that the host machine load still allowed for writeback.

[0129] Preferably, the verification samples come from the most recent batch of suspended objects that are in the same platform domain, store domain, and residual direction as the homogeneous anomaly cluster. After verification, the constant pool manager writes the patch to the constant pool mirror area of ​​the verification sandbox server via a cross-layer call interface, and then replaces the old constant pool with the mirror area through atomic flipping. For cross-tenant SaaS deployment scenarios, single-tenant nodes also irreversibly hash the cluster summary and upload it to the cloud master node. The cloud master node re-aggregates the summary according to the platform domain and charging factor. If multiple tenants provide patch conclusions in the same direction, the cloud master node will distribute the aggregated dynamic rule constraint constant patch back to the local constant pool manager of each tenant. Among them: Where: Patch acceptance rate : Indicates the degree to which the patch reduces the original residuals on the review sample without significantly slowing down the host machine's synthesis; the value is a real number greater than or equal to 0; identity residual : Represents the residual of the original double-entry accounting identity in step two, with a real value; - is used as the original reference for the patch effect; revising the residual : Same meaning as before; used to indicate the new residual after the patch takes effect; Normalized buffer size : Same meaning as above; used to avoid denominator instability; patch holding window : Indicates the stable time length that the host machine can retain for constant pool writes in the current period, and is a real number greater than 0; used to measure whether the current environment is suitable for executing patches; core load window : Indicates the duration of high load for the host machine's core transaction process within the most recent observation period, with a value greater than Real numbers; used to introduce transaction pressure into patch gating; When patch acceptance Not lower than the acceptance threshold At that time, the constant pool manager performs a cross-layer reverse writeback; when the patch acceptance... Below the acceptance threshold At this time, the patch remains in the execution queue and does not enter the constant pool; Acceptance threshold : Indicates the minimum patch acceptance level allowed for a patch to enter the verification sandbox server constant pool. It takes the value of a real number greater than 0; it is used to prevent dangerous write-backs during invalid patches or high-load periods. If the host CPU core transaction process is detected to have reached the load threshold within ten consecutive seconds, the topology evolution engine immediately switches to degradation blocking: on the one hand, the original cross-day sliding window is compressed into a short window of the most recent half hour, retaining only the nodes within the short window for lightweight fitting; on the other hand, the automatic write-back channel is locked, and the dynamic rule constraint constant patch is moved to the approval queue awaiting CFO physical key signature. For example, when a promotional activity in a multi-tenant environment simultaneously triggers a cross-store subsidy plus a hidden deduction, each tenant's local node first forms a homogeneous abnormal cluster, and then uploads the digest to the cloud master node; the cloud master node aggregates and generates a unified patch, which is then distributed back to each tenant. The local constant pool manager controls the patch acceptance rate. After the write operation is completed, when subsequent batches of similar frozen samples enter step two, the residuals that were originally non-convergent begin to shrink and are transferred to the convergent candidate mapping draft branch.

[0130] Furthermore, patch acceptance Residual shrinkage and load tolerance are simultaneously incorporated into gating; federated convergence makes new rules that are difficult for single tenants to identify more readily apparent in multi-tenant environments; degradation blocking avoids dangerous write-backs during high-load periods through dual constraints of short-window fitting and CFO physical keys.

[0131] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0132] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0134] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0135] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included 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. An adaptive parsing and reconciliation method for multi-platform e-commerce bills, executed by a reconciliation server, characterized in that: include: Calculate the structural mismatch amount for the access billing message, and cut off the corresponding reading channel when the structural mismatch amount reaches the preset cutoff condition, extract abnormal source samples to form a frozen sample batch and put it into the isolation verification sandbox, and output the billing data according to the current parsing configuration when the structural mismatch amount does not reach the preset cutoff condition. Based on the frozen sample batch extraction of business serial number and UTC timestamp as calibration anchor, the benchmark total amount is obtained by calling ERP accounting data, generating a candidate mapping draft matrix composed of field offset index, column position adjacency index and amount direction index, calculating the double debit and credit identity residual for each candidate mapping draft, retaining the target mapping draft that meets the convergence condition, and outputting the mapping draft that does not meet the convergence condition as a residual matrix. The target mapping draft is compiled into a static parsing rule image, written to the backup publication area, and the current parsing configuration is replaced by atomic pointer switching, so that the truncated channel can restore the output of normal comparison drop data stream; The normal comparison difference data stream is encapsulated into an embodied work order and bound to a ghost soft token. When the confirmation callback is received and the ghost soft token and version stamp are verified, the accounting field is atomically updated and the ghost soft token is cancelled. When the verification fails, the embodied work order is frozen. Based solely on the residual matrix, unresolved unknown anomaly suspended data clusters are formed. Topological clustering is performed according to time association, store domain association, and residual vector association to generate macro tolerance benchmark coefficients. These macro tolerance benchmark coefficients are then written as constant patches into the compound debit-credit identity residual constant pool for subsequent frozen sample batch verification.

2. The adaptive parsing reconciliation method according to claim 1, characterized in that: Calculate the structural mismatch for the access billing message, including: normalizing the field name fragments, delimiter types, field length boundaries, and empty field placeholder patterns according to the receiving order to generate a structural fingerprint string; and performing a fixed hash operation on the structural fingerprint string to obtain the current structural hash signature. The current structure hash signature is compared bit by bit with the archived structure hash signature, and the structure mismatch is obtained by combining the field out-of-bounds count. When the structure mismatch reaches the preset cutoff condition, the mutation channel identifier is written and a frozen sample batch is formed. When the preset cutoff condition is not reached, the current parsing configuration is maintained.

3. The adaptive parsing reconciliation method according to claim 2, characterized in that: The extraction of frozen sample batches and the generation of calibration anchors include: selecting consecutive abnormal rows, inserted abnormal rows and backtracking retransmission rows in a sliding window based on the same mutation channel identifier, and encapsulating the original byte sequence, delimiter, empty field placeholder and field offset table into a read-only shadow copy. In the read-only shadow copy, candidate columns are filtered according to the business serial number rule and the UTC timestamp rule, and a calibration anchor point is formed when the columns in the same message line are simultaneously matched and pass the stability check of adjacent message lines.

4. The adaptive parsing reconciliation method according to claim 3, characterized in that: The target mapping draft is compiled into a static parsing rule map, including: converting the column position attribution, amount direction and default fallback relationship in the target mapping draft into a set of field addressing edges, a set of amount direction edges and a set of exception jump gates; Write the set of field addressing edges, the set of amount direction edges, and the set of exception jump gates, along with the platform domain code, bill type, rule version number, and rollback pointer, into the version description header of the standby release area; When the grayscale batch review passes, the atomic pointer is switched; when the review fails, the current parsing configuration is maintained and the rollback pointer is returned.

5. The adaptive parsing reconciliation method according to claim 4, characterized in that: The formation of unresolved unknown anomaly suspended data clusters and the writing of constant patches include: extracting high-order algebraic residual matrices, deriving time-series vectors, and store domain attribution keys from the residual matrix to form unresolved unknown anomaly suspended data clusters; The unresolved unknown anomaly data clusters are associated by time, store domain, and residual vector to generate homogeneous anomaly clusters; based on the homogeneous anomaly clusters, the unique patch slots corresponding to the platform domain code, store domain, and charging factor name are located, and constant patches are written to the mirror area before the version pointer is flipped.

6. The adaptive parsing reconciliation method according to claim 1, characterized in that: The preset interception conditions are given by the header drift determination chain: first, perform case unification, whitespace folding and delimiter merging on the text header of the access billing message, and then calculate the cosine similarity between the current text header and the historical stable header. When the cosine similarity is lower than the warning threshold, the corresponding reading channel is truncated and a frozen sample batch is formed. When the cosine similarity is not lower than the warning threshold, the billing data continues to be output according to the current parsing configuration.

7. The adaptive parsing reconciliation method according to claim 3, characterized in that: The generation of the candidate mapping draft matrix adopts a two-track branch: when the semantic branch in the cloud is available, the column adjacency score between the field name fragment and the target business semantic slot is extracted and written into the candidate mapping draft matrix; When the semantic branch in the cloud is unavailable, extract the column stability scores corresponding to the length of the numeric string, the sign bit, and the empty field marker, and write them into the candidate mapping draft matrix; both branches are merged with the field offset table using the same column index as the key.

8. The adaptive parsing reconciliation method according to claim 1, characterized in that: Atomic update of accounting fields and cancellation of ghost soft tokens include: first generating lock level difference based on the amount difference and version stamp in the normal comparison gap data stream; When the lock level difference reaches the exclusive threshold, after verifying the phantom soft token, request a database row-level exclusive lock and perform accounting field updates; when the lock level difference does not reach the exclusive threshold, retain the phantom soft token and complete the accounting field updates by spinning according to the version snapshot.

9. The adaptive parsing reconciliation method according to claim 5, characterized in that: The topology clustering and constant patch writing of unresolved unknown anomaly suspended data clusters include: when the core load of the topology evolution engine reaches a preset load threshold, compressing the time window into a short window of the most recent half hour, and transferring the constant patch to the queue to be signed while keeping the current constant pool unchanged; When the core load does not reach the preset load threshold, maintain the current sliding time window and continue to perform constant patch mirror area writing and version pointer flipping.

10. The adaptive parsing reconciliation method according to claim 1, characterized in that: An embodied work order must include at least an order key, a store key, an amount difference, a direction indicator, a callback address, a version stamp, and a work order status; the work order status is limited to pending confirmation, writing to the database, confirmed, frozen, and void; When the reconciliation server generates a embodied work order, receives a confirmation callback, and completes the update of accounting fields, it writes the corresponding work order status into the status field of the embodied work order.

11. The adaptive parsing reconciliation method according to claim 10, characterized in that: The confirmation callback should at least include the work order key, token number, version stamp, callback time, and confirmation code; When the work order status is pending confirmation and the phantom soft token and version stamp verification pass, the reconciliation server returns a receiving status and triggers an update of the accounting fields; when the verification fails, it returns a reject status and switches the embodied work order to frozen.

12. The adaptive parsing reconciliation method according to claim 4, characterized in that: The version description header includes at least the platform domain code, billing type, rule version number, generation time, target mapping draft identifier, and rollback pointer; when the reconciliation server writes to the standby release area, performs atomic pointer switching, and rolls back the current parsing configuration, it generates release log records containing version description header fields and release status fields, respectively.

13. The adaptive parsing reconciliation method according to claim 5, characterized in that: When unresolved unknown anomaly suspended data clusters are written to the event bus, their message fields must include at least the time period identifier, rule mismatch vector, store hash identifier, cluster sequence number, and topic identifier; The fields of a constant patch should include at least the patch identifier, the charging factor name, the applicable platform domain, the applicable store domain, the old constant value, the new constant value, and the rollback slot; and the corresponding patch status records should be output before and after writing to the mirror area.

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