A form and workflow node linkage arrangement method and system

Abstract

The application discloses a form and workflow node linkage arrangement method and system, through zero-rejection silent duration backtracking and reverse abnormal dense quantity statistics of field history rejection record, linkage deviation and linkage pseudo-stable interval are identified, combined with reverse sequence submission abnormal pre-determination and field activation decreasing amplitude determination of each process stage, a trigger link break node is located and one-way migration of the dependent relationship is completed; based on opinion similarity rate rising pre-scanning, overload hidden danger rating is generated, through redundant amount scheduling of historical active skip nodes, a cross-node dredging path is constructed, combined with opinion missing rate analysis of overdrive section, an execution constraint set is established; according to the solidification degree of the approval rhythm and the identity overlap abnormal amplitude, the health status of the flow is verified, the missing position and the supplement condition of the rejection dense period are back calculated, the linkage arrangement instruction containing the trigger field identifier, the trigger condition interval, the target node identifier and the execution time sequence is generated, and the adaptive repair of the approval link linkage structure is realized.

Description

Technical Field

[0001] This invention relates to the field of business process management technology, and in particular to a method and system for the linked arrangement of forms and workflow nodes. Background Technology

[0002] In enterprise-level approval systems, the trigger dependencies between form fields and workflow nodes are typically statically configured by the administrator during system deployment. The binding method between field activation order and node approval logic remains fixed after configuration. As business scales up and personnel structure changes, the deviation between actual submission behavior and the original configuration accumulates. The field trigger links of some nodes gradually fail during operation, and the dependencies between nodes become disconnected from the actual business flow path. However, the existing system lacks a proactive mechanism to detect the health status of the trigger links, and the deviation exists implicitly for a long time without being identified.

[0003] When multiple nodes in the same approval chain are subjected to continuous operational pressure or personnel changes, the approval opinions of each node tend to converge rather than check each other. Early signs of homogenization of opinions are not visible in throughput indicators, and the chain is already in a state of overall dysfunction by the time the backlog of processes becomes apparent. In addition, in terms of the dynamic repair of the linkage between form fields and nodes, existing solutions generally rely on manual review triggers and cannot automatically infer the conditions for filling missing nodes based on the historical evolution of the process status and generate executable orchestration adjustment instructions, resulting in low timeliness of the repair of the linkage structure. Summary of the Invention

[0004] This invention discloses a method and system for linking and arranging forms and workflow nodes. It achieves adaptive reorganization of node dependencies by tracing back field rejections and locating trigger link breaks. It establishes path adaptation and execution constraint mechanisms through early pre-scanning of opinion homogenization and redundancy scheduling. Based on the workflow health score, it infers the missing filling conditions and generates global linkage and arrangement instructions for form fields and workflow nodes, realizing adaptive repair of the approval link linkage structure.

[0005] The first aspect of this invention proposes a method for linking and arranging forms and workflow nodes, comprising the following steps:

[0006] Collect field configuration data and node dependency data, and perform a backtracking comparison and verification of the field configuration data and the node dependency data to generate node linkage deviation.

[0007] The field configuration data is subjected to reverse submission anomaly pre-judgment to generate a trigger centralized distribution. The field activation decrease rate of the three stages of entry, judgment and archiving is measured from the field configuration data to generate a stage activation difference coefficient. Based on the trigger centralized distribution and the stage activation difference coefficient, a field dependency unidirectional migration is carried out to build a node reorganization strategy.

[0008] Based on the node linkage deviation and the node reorganization strategy, a pre-scan is performed to increase the opinion similarity rate, generating an overload hazard rating. The overload hazard rating is then adapted to a path adaptation scheme, and a hierarchical collaborative drive is implemented to establish an execution constraint set based on the path adaptation scheme.

[0009] Based on the execution constraint set, the execution weight and trigger threshold of the node reorganization strategy are coordinated to generate a parameter change log. Based on the parameter change log, the overlap between the approver and the submitter is verified to generate an operational anomaly interference sequence. The first rejection node is identified through the operational anomaly interference sequence to generate a circulation health score.

[0010] Based on the flow health score, the missing positions and filling conditions of the rejection dense period are deduced to generate the arrangement structure parameters. According to the arrangement structure parameters, the path level allocation is executed to generate the form and the workflow node linkage arrangement instruction.

[0011] A second aspect of this invention provides a form and workflow node linkage orchestration system, comprising:

[0012] The data verification module is used to collect field configuration data and node dependency data, and to perform a backtracking comparison and verification of the field configuration data and the node dependency data to generate node linkage deviation.

[0013] The orchestration and evaluation module is used to perform reverse submission anomaly pre-judgment on the field configuration data to generate a trigger centralized distribution, measure the field activation decrease rate of the three stages of input, judgment and archiving from the field configuration data to generate a stage activation difference coefficient, and perform field dependency unidirectional migration to build a node reorganization strategy based on the trigger centralized distribution and the stage activation difference coefficient.

[0014] The path decision module is used to perform a pre-scan to generate an overload hazard rating based on the node linkage deviation and the node reorganization strategy, perform path adaptation to generate a path adaptation scheme based on the overload hazard rating, and implement hierarchical collaborative drive to establish an execution constraint set based on the path adaptation scheme.

[0015] The load control module is used to coordinate the execution weight and trigger threshold of the node reorganization strategy according to the execution constraint set to generate a parameter change log, verify the overlap of the approver and submitter identities based on the parameter change log to generate an operational anomaly interference sequence, and identify the first rejection node through the operational anomaly interference sequence to generate a circulation health score.

[0016] The topology generation module is used to generate orchestration structure parameters by inferring the missing positions and filling conditions of the rejection dense period based on the flow health score, and to execute path hierarchical allocation generation form and workflow node linkage orchestration instructions according to the orchestration structure parameters.

[0017] The beneficial effects of this invention are reflected in the following points: First, by retrospectively analyzing the zero-rejection silent duration and reverse anomaly density statistics of historical rejection records, erroneous fields that have been in a pseudo-stable state for a long time are identified. Combined with the pre-judgment of reverse submission anomalies and the measurement of the decrease in field activation at each process stage, the trigger link break node is located and the unidirectional migration and reorganization of field dependencies is completed. This allows the workflow to adaptively reconstruct dependent links and restore the effective continuity of field activation even when the node triggering order is continuously disordered. Second, based on the node linkage deviation and node reorganization strategy, a pre-scan of the increase in opinion similarity rate is carried out. The early signal of the tendency of review homogenization in the approval link is transformed into a gradeable overload risk rating. By constructing a cross-node guidance path through the redundancy distribution of historical proactive skipped nodes, and determining the compensation drive upper limit based on the approval opinion missing rate of overdriven sections, the load is redistributed in advance when the quality of link approval shows a downward trend, avoiding the problem of opinion convergence from evolving into full-link failure. Finally, by tracking the batch evolution of the approval time interval rhythm and verifying the abnormal amplitude of overlapping approver and submitter identities, the true stabilization window of operational anomalies is identified, and the reverse verification of the first rejection node and the flow uniformity assessment are completed. Then, based on the flow health score, the missing positions and filling conditions of the rejection intensive period are deduced, and global linkage orchestration instructions for form field triggering behavior and workflow node approval logic are generated, so that the approval link can actively repair the linkage structure according to the health status after the interference subsides. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for linking and arranging forms and workflow nodes according to the present invention.

[0019] Figure 2 This is a structural block diagram of a form and workflow node linkage arrangement system according to the present invention. Detailed Implementation

[0020] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0021] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0022] References to "one embodiment" or "some embodiments" as used in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] The technical solutions of the embodiments of this application will be described below.

[0024] like Figure 1 As shown, this embodiment of the invention provides a method for linking and arranging forms and workflow nodes, including the following steps S101-S105:

[0025] Step S101: Collect field configuration data and node dependency data, and perform a backtracking comparison and verification of the field configuration data and node dependency data to generate node linkage deviation.

[0026] Specifically, field configuration data and node dependency data are collected. Field configuration data is continuously recorded by the form management platform at the field level. Each field entry carries a field number, its associated node identifier, version batch number, and historical rejection records. Rejection records are structured around the rejection time and the associated node identifier. The associated node identifier is written into the configuration parameters based on the node topology hierarchy during the initialization phase. Rejection records for the same field generated at different approval nodes are archived independently. The field number and node identifier together constitute the location key for the rejection record. Some fields in the field configuration data have cross-version migration records. The migration operation keeps the field number unchanged but changes the associated node identifier. Rejection records before and after the migration are distinguished in the field configuration data by version boundary markings. Rejection records before the version boundary time are not included in the current version's backtracking comparison to prevent historical version rejection behavior from polluting the current version's field status evaluation. Node dependency data is output by the workflow engine according to the node topology structure. It includes the set of preceding dependent nodes, the set of subsequent triggering nodes, and dependency weights for each node. The dependency weights reflect the constraint strength of the preceding nodes on the triggering of the current node. The weights are manually assigned by the process administrator according to business priority during the process design phase. Nodes with high constraint strength usually have their dependency weights concentrated in the key verification links of multi-level approval processes, while nodes with low weights are mostly information registration or automatic transfer nodes. Node pairs with circular dependencies in the node dependency data undergo topology sorting verification during the collection phase. When a circular dependency is detected, the corresponding node pair is marked as a topology anomaly. Node pairs with topology anomalies do not participate in dependency path derivation, and their dependency relationships are written into the node dependency data as broken chain annotations.

[0027] In some embodiments, the step of performing a backtracking comparison and verification of rejection records between the field configuration data and the node dependency data to generate node linkage deviation includes: extracting the zero rejection duration of each field in the field configuration data to obtain a zero rejection silent sequence; statistically analyzing the reverse anomaly density of each time period in the zero rejection silent sequence to form a reverse silent distribution; defining the zero rejection false fields based on the reverse silent distribution to determine the linkage pseudo-stable interval; and performing a node dependency mapping comparison between the linkage pseudo-stable interval and the node dependency data to generate node linkage deviation.

[0028] Extract the zero rejection duration from each field in the field configuration data to obtain the zero rejection silent sequence. The zero rejection duration is calculated as the difference between two adjacent rejection times in the rejection records of each field. When there is only one rejection record, the silent period before the time of that record is taken as the zero rejection duration. The interval with no rejection events is determined as the zero rejection period. All zero rejection periods of each field are arranged in chronological order to form the zero rejection silent sequence of that field. Fields with sparse historical rejection records in the field configuration data tend to have long zero-rejection silent sequences, with a single zero-rejection duration potentially spanning several approval cycles. In multi-level approval processes, some fields require approval nodes to verify each item according to business rules, but approvers often process entire orders as a whole. These fields do not generate any rejection records in the field configuration data, resulting in a single extremely long silent sequence covering the entire observation window. This extreme case is treated as a single long silent field in the subsequent statistical stage and is not included in the ratio calculation of silent entries during normal periods. This prevents extremely long silent sequences from inflating global statistical indicators and causing a large number of normal fields to be misclassified in subsequent judgments. Fields in the field configuration data that carry version boundary markers due to version migration have their zero-rejection silent sequences forcibly cut off at the version boundary. The zero-rejection periods on both sides of the version boundary are separated into independent segments to prevent cross-version silent durations from being mistakenly merged into a single extremely long silent segment. The migration field for node identifier changes retains both the node identifier before and after the change in the zero-rejection silent sequence. The silent duration on both sides of the change boundary has been cut into independent entries during the collection phase. The current version node identifier is used as the primary key for density statistics and participates in aggregation. The historical identifier is only used as a reference for version tracing. Therefore, there is no false increase in cross-version silent duration caused by version migration in the zero-rejection silent sequence.

[0029] A reverse silence distribution is formed by statistically analyzing the density of reverse anomalies in each time period of the zero-rejection silent sequence. The density of reverse anomalies is defined as the ratio of the number of silent entries with abnormally long durations in the zero-rejection silent sequence to the number of normal silent entries within the same window, within a defined statistical window. A higher ratio indicates that the silent behavior of the field within that window is more concentrated in extremely long periods, and the stronger the suppression of the normal approval rhythm. The statistical window is based on the standard approval cycle, which is configured as parameters according to the average timeliness of the business process during the initialization phase. The threshold for judging extremely long silences is the 90th percentile of the zero-rejection duration of all fields in the same node. Silence entries below this percentile are considered normal, while those above are considered reverse anomalies. In a zero-rejection silent sequence, entries from different fields but belonging to the same node are jointly calculated within the statistical window. The density of reverse anomalies in fields of the same node reflects the overall degree of approval rejection suppression for that node. A single-field reverse anomaly is insufficient to trigger a node-level linkage judgment. When multiple fields of the same node simultaneously enter an ultra-long zero-rejection state during the same period, the density increases significantly. In actual processes, this phenomenon usually corresponds to approvers uniformly releasing all fields under a node in batch confirmation without conducting item-by-item verification of the content of each field. Some nodes generate a large number of normal silent clusters due to a surge in submissions during specific business periods. In the corresponding period of such nodes, the zero-rejection silent sequence has a high density of silent entries, but the duration is generally normal. The density of reverse anomalies is not increased during this period. Therefore, the business concentration period effect and the actual rejection suppression can be distinguished in the reverse silent distribution. The concentration period label is simultaneously written into each statistical window of the reverse silent distribution for comparison and reference.

[0030] Based on the inverse silent distribution, zero-rejection false fields are defined to determine the linkage pseudo-stable interval. The trigger condition is a statistical window where the density of each node in the inverse silent distribution exceeds a set high density threshold. This threshold is initialized by adding two standard deviations to the mean of the historical batch density ratio distribution and writing it into the configuration parameters. When the number of windows where the density of a single node continuously exceeds the threshold is not less than the set lower limit for continuous windows, all fields under that node that participate in the density increase are determined to be zero-rejection false fields. In the inverse silent distribution, if the density exceeds the threshold only in an isolated window while the windows before and after it are normal, this usually corresponds to an occasional operational oversight by the approver rather than continuous rejection suppression. Isolated windows exceeding the threshold do not trigger the zero-rejection false field determination; these windows are classified as occasional entries in the inverse silent distribution to distinguish them from continuous threshold-exceeding segments. After the zero-rejection false fields are determined, the continuous threshold-exceeding window periods of each false field are merged on the time axis to form candidate linkage pseudo-stable intervals. The start and end boundaries of the candidate intervals are determined by the first and last times of the continuous threshold-exceeding windows, with boundary precision consistent with the statistical window granularity. When the proportion of windows carrying the concentration period label within a candidate linkage pseudo-stable interval exceeds a set percentage, the corresponding interval is identified as a business concentration period interference interval rather than a true pseudo-stable interval, and is removed from the linkage pseudo-stable interval to prevent normal business concentration submission behavior from being mistakenly judged as linkage failure. The node identifier set of each interval in the linkage pseudo-stable interval is the direct operation object of dependency mapping comparison. When there are pre-dependencies or post-trigger relationships between nodes in the set, the linkage deviation intensity is significantly higher than when there are no dependencies between nodes. In a multi-level approval process, if the node identifier set of the linkage pseudo-stable interval contains dependent nodes, a multi-node linkage label is added to that interval, which means that the approval link is bypassed in its entirety during that period rather than each node being independently and occasionally silent.

[0031] Node linkage deviation is generated by comparing node dependency mappings between linkage pseudo-stable intervals and node dependency data. The set of node identifiers involved in each interval of the linkage pseudo-stable interval is compared pairwise with the node topology in the node dependency data. The core issue of this comparison is whether multiple nodes simultaneously identified as false within the linkage pseudo-stable interval have pre-dependencies or post-trigger relationships in the node dependency data. If so, it indicates that nodes in the dependency chain synchronously exhibit rejection suppression, and the credibility of the linkage deviation is higher than that of an isolated single node being identified as false. When node pairs with high dependency weights in the node dependency data experience synchronous loss, the linkage deviation intensity of the corresponding linkage pseudo-stable interval is greater than that of node pairs with low dependency weights. The deviation intensity is quantified by the product of the dependency weight and the number of false fields within the interval. The quantification result is normalized and used as the linkage deviation score for that interval. In pseudo-stable intervals with multi-node linkage annotations, the comparison scope is expanded to include the second-order neighbor nodes of the involved nodes in the node dependency data. If the initial and final review nodes of the main approval chain fail synchronously, the density of related review nodes is continuously increasing. These nodes are then included in the calculation with a 0.5x weighting based on attenuation experience to prevent weak linkage signals on strong dependency chains from being completely ignored. This second-order neighbor expansion is particularly necessary in complex approval processes with a large number of workflow nodes, because the transmission effect of rejection suppression often spreads to bypass nodes after the failure of the main link node. The increase in the density of bypass nodes lags behind that of the main link node. If the comparison is not expanded, this transmission effect will be underestimated in the node linkage deviation. Nodes with topological anomalies and broken chains in the node dependency data do not participate in the dependency path extension derivation during mapping comparison. The two ends of the broken chain are processed independently, and the score of the broken chain segment is incorporated into the node linkage deviation. The node linkage deviation is then output after merging and normalizing the scores of each interval.

[0032] Step S102: Perform reverse submission exception pre-judgment on field configuration data to generate trigger centralized distribution; measure the field activation decrease rate of the three stages of entry, judgment and archiving from the field configuration data to generate stage activation difference coefficient; and construct node reorganization strategy based on field dependency unidirectional migration of trigger centralized distribution and stage activation difference coefficient.

[0033] Specifically, a centralized distribution of pre-judgment triggers for reverse submission anomalies is generated based on the field configuration data. The reverse submission anomaly pre-judgment is based on the submission time sequence of each field in the field configuration data. It checks the consistency between the actual submission order and the logical triggering order of the workflow design, field by field. The logical triggering order is determined by the hierarchical position of the node to which the field belongs in the process topology. Fields under a later-level node should not be submitted earlier than fields under an earlier-level node in normal workflow. A reverse submission anomaly is triggered when the submission time difference between adjacent node fields is negative and its absolute value exceeds the set time tolerance. The tolerance is written into the configuration parameters according to the standard processing time of each node during the initialization phase. In workflows with multi-node parallel processing, some fields allow synchronous submission across nodes. These fields are identified as parallel fields during reverse submission judgment. The submission order verification of parallel fields skips cross-node comparisons and only performs reverse submission judgment within the same node. During workflow execution, submitters, in an effort to complete submissions before the deadline during the backlog period, often fill in the fields for subsequent nodes before the preceding nodes have been approved. The field configuration data records a large number of reverse-order submission events during this period. The corresponding peak in the backlog period trigger density quickly subsides after the peak. This type of periodic peak differs from the structurally high-density segment pattern in the concentrated trigger distribution; the former corresponds to rhythmic backlog disturbances, while the latter usually points to a persistent problem with the node triggering logic. High-value clusters of trigger density for each node in the concentrated trigger distribution correspond to periods where reverse-order submissions occur in concentrated fields. When high-value clusters are continuously distributed along the node dimension, it indicates that the triggering order between adjacent nodes is unstable throughout this period. Isolated high values ​​for a single node usually correspond to a long-term mismatch between the node's own triggering conditions and the actual submitted content.

[0034] The activation difference coefficients for the three stages of field activation reduction (entry, judgment, and archiving) were determined from the field configuration data. Based on the activation counts of each form field in the three stages of entry (user submission), judgment (node ​​determination), and archiving (completion filing), the reduction rate from entry to judgment is calculated as α = (N1-N2) / N1, and the reduction rate from judgment to archiving is calculated as β = (N2-N3) / N2, where N1, N2, and N3 are the activation counts for entry, judgment, and archiving, respectively. For example, when N1=100, N2=80, and N3=60 for the reimbursement field, α=20% and β=25%, reflecting the proportion of the field being progressively filtered. The number of activations for each field in the three stages was normalized according to the batch cycle and calculated segment by segment. Normalization eliminates the influence of differences in the basic activation volume between high-frequency and low-frequency fields. The stage activation difference coefficient κ = α / β, with κ = 0.8 in the previous example. A consistently large κ indicates that field activation loss is concentrated in the transition from data entry to judgment. Some workflows have strict pre-verification rules for judgment nodes. If the content submitted by the submitter frequently fails to meet the entry requirements of the judgment node, the field will be blocked by the system after data entry and will not be able to enter the judgment node. The field configuration data will show significantly fewer activation records in the judgment stage under such nodes, and the stage activation difference coefficient will show typical front-end loss characteristics in the corresponding batch. When the ratio is consistently small, the loss is concentrated in the judgment to archiving stage. Usually, there is a manual confirmation threshold at the archiving node, and a large number of fields are lost in the archiving stage even after passing the judgment. Items with higher business importance in the field configuration data have a higher weight in the calculation of the average stage reduction rate. When the stage activation difference coefficient deviates from the historical average, it usually points to a systemic triggering obstacle in a certain transition stage. The type of obstacle directly corresponds to the direction of deviation of the ratio.

[0035] In some embodiments, the step of constructing a node reorganization strategy based on field-dependent unidirectional migration using the trigger set distribution and the stage activation difference coefficient includes: extracting the activation amount change amplitude of each node in the trigger set distribution to obtain an activation decrease distribution; comparing the activation decrease distribution with the stage activation difference coefficient to locate the trigger interruption critical node; verifying the field trigger continuity for the trigger interruption critical node to confirm the trigger interruption failure segment and the adjacent high-efficiency trigger segment; and generating a node reorganization strategy by performing field-dependent unidirectional migration based on the trigger interruption failure segment and the adjacent high-efficiency trigger segment.

[0036] The activation decline distribution is obtained by extracting the change in activation levels of each node in the trigger-centralized distribution. Based on the trigger density difference between adjacent time windows of each node in the trigger-centralized distribution, the absolute value of the decline when the difference is negative is taken as the activation decline value of that node in that time window; a positive difference is recorded as zero decline. After the high-density period in the trigger-centralized distribution ends, the activation decline value often shows a significant jump. The causes of this jump can be divided into two categories: one is that the trigger link suddenly disconnects after the high-density period due to the removal of field dependencies, and the activation decline value remains high after the jump; the other is caused by the concentrated release of backlogged submissions during the high-density period, and the submission rhythm naturally declines after the backlog is cleared, with the corresponding activation decline value gradually returning to zero within several windows after the jump. These two cases can be directly distinguished in the activation decline distribution due to the different decay patterns of the decline value. The former forms a sustained high decline plateau in the activation decline distribution, while the latter forms a rapid decay curve after a single jump. After a process version upgrade, the triggering conditions for some nodes change. Fields that could be activated normally in the old version cannot be activated in the new version because the triggering conditions are not met. The activation decline distribution forms a version-switching decline peak in the version switch window. These peaks usually fade naturally within several observation windows as submitters adapt to the new rules. The decline rate can be used as a quantitative indicator of the submitter's adaptation speed. A slow decline indicates a large gap between the new version's triggering conditions and actual business filling habits. Nodes with a dense concentration of significant decline points in the activation decline distribution are usually preferred candidate source nodes during the unidirectional migration phase of field dependencies. Nodes with a high average decline and a large standard deviation of decline values ​​in each window indicate that the triggering mechanism is continuously unstable within the observation period. The higher the degree of instability, the more drastic the fluctuation of the node's activation status between observation windows, making it difficult to make a reliable judgment based on the observation results of a single period.

[0037] The activation decline distribution is compared with the stage activation difference coefficient to locate the critical node for triggering interruption. The average decline of each node in the activation decline distribution is jointly evaluated with the ratio of the corresponding stage's decrease in the stage activation difference coefficient. If both are high, it is considered a candidate for triggering interruption. For example, the average decline of the legal review node in contract approval reaches 40%, and the stage decrease ratio reaches 2.5 times, making it a candidate. Nodes with a high average decline in the activation decline distribution but a stage activation difference coefficient close to normal levels usually reflect that the fluctuation in activation volume originates from external submission rhythm disturbances. A short-term absence of the node approver causes a sharp drop in processing volume during the absence period, resulting in a high decline value recorded in the activation decline distribution. However, the total activation volume of all fields in the entire process does not decrease synchronously, and the stage activation difference coefficient remains normal during the same period. After identifying this difference through joint evaluation, the node is not included in the critical node for triggering interruption, preventing temporary declines caused by personnel absence from being mistakenly identified as triggering a break in the chain. Conversely, when the activation difference coefficient of a stage deviates significantly from the historical average, and the corresponding node's reduction value in the activation reduction distribution remains consistently large, the stronger the degree of both factors being higher in the same direction, the more it indicates that the activation loss of that node is both macroscopically reflected in the inter-stage reduction anomaly and microscopically confirmed in the window-by-window reduction. The credibility of triggering an interruption increases with the degree of both factors being higher in the same direction. Nodes with low joint evaluation scores but high individual scores in each factor indicate that the triggering obstacle only manifests in one dimension. Such nodes are listed as low credibility candidates. Low credibility candidates carry credibility score labels in the triggering interruption critical nodes. The credibility score affects the leniency of the upper limit of the trigger interval in the field triggering continuity determination stage. The lower the score, the wider the upper limit, in order to reduce the risk of misjudgment.

[0038] For trigger interruption critical nodes, the continuity of the verification field is confirmed to trigger interruption failure segments and adjacent high-efficiency trigger segments. Based on the field trigger interval sequence of each candidate node of the trigger interruption critical node, segments in the trigger interruption critical node that continuously exceed the scoring adjustment upper limit are judged as trigger interruption candidate segments. During the node approval backlog period, approvers concentrate on processing a large number of pending tasks. After the backlog is cleared, there is a significant blank interval in the trigger sequence. The trigger sequence before this blank interval is extremely dense, which is fundamentally different from the uniform sparse pattern before and after the trigger link break type interval. Candidate segments with dense front end are marked as digestion intervals and are not included in the trigger interruption failure segments. The boundary identification of the trigger interruption failure segment is based on the moment when the trigger interval sequence breaks through the upper limit from the normal rhythm as the starting boundary and the moment when the interval value falls back to within the upper limit as the ending boundary. The accuracy of the start and end boundaries is consistent with the granularity of the trigger sequence collection. In the boundary identification, there are cases where the interval value repeatedly crosses near the upper limit. For segments that cross more than twice, the midpoint of the crossing interval is used as the boundary anchor point to avoid the boundary moving back and forth frequently due to slight fluctuations. Once the trigger interruption failure segment is determined, segments with low average trigger intervals and consistently high trigger density in the immediate preceding and following time periods are identified as adjacent high-efficiency trigger segments. When the trigger density of a high-efficiency segment is higher than the empirical threshold of the average trigger density of all nodes, it is identified as a strong high-efficiency segment. The node corresponding to the strong high-efficiency segment is given priority as the migration target during the field dependency migration phase. The weak high-efficiency segment with a trigger density only slightly higher than the average is the next priority. When there are adjacent high-efficiency trigger segments that meet the conditions on both sides of the trigger interruption failure segment, the strong high-efficiency side is the primary migration target, and the weak high-efficiency side is the alternative.

[0039] A node reorganization strategy is implemented based on a unidirectional migration of field dependencies between the triggered interruption failure segment and the adjacent high-efficiency trigger segment. The field dependencies of nodes with broken trigger links within the triggered interruption failure segment are reorganized with reference to the dependency structure of nodes with complete trigger links in the adjacent high-efficiency trigger segment. The migration direction is unidirectional, from the failure segment to the high-efficiency segment, preventing bidirectional migration from introducing circular dependencies. Field dependencies with dependency weights below a set migration threshold are considered migrateable dependencies. Dependencies of fields with high business constraints are not included in the migration to prevent core workflow logic from being disrupted by reorganization. In workflows, it is common for an intermediate node to remain in a triggered interruption failure segment due to a long-term mismatch between its trigger conditions and the actual submitted content, while its upstream input node and downstream archive node are both operating normally. In this case, unidirectional field dependency migration transfers the migrateable dependencies of the failure node to the downstream archive node. The trigger results of the upstream input fields are directly transmitted to the archive node, the failure node exits the dependency link, and the activation continuity of the entire link is restored. If a node with a minor failure in a trigger-interruption failure zone fails to return to a normal activation rate after migration, it usually indicates a configuration deviation in the triggering conditions themselves, in addition to migration dependency issues. Such nodes are subject to secondary verification of triggering conditions in the node reorganization strategy. The verification criterion is whether the density value of the trigger concentration distribution during the corresponding time period decreases after migration. If the density value does not decrease, it indicates that the migration operation failed to eliminate the fundamental triggering obstacle. When multiple failed nodes in a trigger-interruption failure zone need to be migrated simultaneously, the migration operation is performed sequentially from most severe to least severe failure. After each migration, the set of migrateable dependencies for the remaining failed nodes is reassessed to prevent conflicts in target selection within adjacent efficient triggering zones after the previous migration changed the topology. Nodes that cannot be migrated are subject to manual review in the node reorganization strategy.

[0040] Step S103: Based on the node linkage deviation and node reorganization strategy, conduct a pre-scan to generate an overload hazard rating by increasing the opinion similarity rate. Then, perform path adaptation to generate a path adaptation scheme for the overload hazard rating and establish an execution constraint set based on the path adaptation scheme and hierarchical collaborative drive.

[0041] Specifically, an overload risk rating is generated by pre-scanning the increase in opinion similarity rate based on node linkage deviation and node reorganization strategies. The opinion similarity rate is calculated as the proportion of records where the approval opinions of each node are completely consistent with those of the preceding node within a set statistical window to the total number of approval records. The higher the proportion, the less independent judgment the node has. The node set corresponding to the high-level interval of node linkage deviation is prioritized in the scan. For nodes with higher linkage deviation scores, approvers are more likely to directly adopt the approval conclusion of the preceding node during the period when rejection is systematically suppressed. Nodes in the failed segment triggered by the node reorganization strategy are also marked as key attention objects. The trigger link break causes discontinuous field activation, and approvers tend to refer to the opinions of the preceding node due to incomplete reference information. After the node reorganization strategy migrates the dependencies of failed nodes to the high-efficiency segment, the scanning benchmark for opinion similarity rate is recalculated based on the migrated node dependency structure. The pre-scanning process calculates the slope of the similarity rate trend window by window. Nodes with a continuously positive slope and a cumulative increase exceeding a set threshold are identified as overload potential nodes. These nodes in the workflow often exhibit consistent approval opinions, a significant reduction in objection records, and no apparent backlog. The overload potential rating classifies each overload potential node into three levels: high, medium, and low, based on the rate of increase in similarity rate and the current absolute value of similarity rate (e.g., a cumulative increase exceeding 50% and a current increase exceeding 70% is classified as a high level). The level boundaries are written into the configuration parameters based on historical backlog warning thresholds. The node linkage deviation and node reorganization strategy jointly affect the rating level distribution. The dense appearance of high-level nodes usually reflects a systemic decline in the overall approval quality of the workflow.

[0042] In some embodiments, the process of generating a path adaptation scheme for the overload hazard rating includes: conducting a layer-by-layer investigation of the risk level of each node in the overload hazard rating to form an inter-layer risk comparison; extracting the redundancy of historical actively skipped nodes from the inter-layer risk comparison to form a scheduling redundancy distribution; statistically analyzing cross-node redundant migration paths based on the scheduling redundancy distribution to form a redundant migration path table; and executing node path reconfiguration to generate a path adaptation scheme based on the redundant migration path table.

[0043] The risk level of each node in the overload hazard assessment is investigated layer by layer to form an inter-layer risk comparison. Starting from the highest level node in the overload hazard assessment, the investigation proceeds layer by layer, checking the risk level difference between adjacent level nodes. The larger the difference, the more significant the risk stratification between adjacent levels. The boundary of significant risk stratification usually corresponds to a certain link in the workflow taking on an excessive approval load transferred from upstream nodes. If adjacent nodes at the same level in the overload hazard assessment are both in high hazard layers, it usually reflects that the entire approval link of these nodes is under pressure. The decline in approval quality of higher-level nodes will spread to subsequent nodes along the dependent link. The similarity rate of subsequent nodes increases synchronously with the increase in the similarity rate of higher-level nodes. This kind of transmission effect often manifests in the workflow as an initial shortening of approval time followed by a sudden delay. The initial shortening of time is a result of approvers compressing the review depth to cope with backlogs, while the sudden delay corresponds to the concentrated rejection of approvals by subsequent nodes due to the reduced approval quality. Inter-layer risk comparison unfolds the risk level distribution across different levels at the node dimension. The difference between the risk levels of higher and lower levels reveals the transmission direction of overload hazards. When the transmission direction is from upstream nodes to downstream nodes, the priority intervention point for path diversion is at the upstream node. Blocking the pressure of consensus at upstream nodes can effectively alleviate the passive consensus at downstream nodes. When the transmission direction is not obvious, the sources of hazards at each level are relatively independent, and diversion strategies need to be formulated separately for each level. The concentration of high-level nodes in the inter-layer risk comparison reflects the topological distribution of the overall risk in this batch. When nodes are concentrated on a single link, diversion resources can be focused. When nodes are dispersed across multiple links, priority should be given to the highest-level node risk level in each link in the inter-layer risk comparison. Prioritize the handling of the highest-risk links to prevent cross-link risks from overlapping and causing global consensus.

[0044] Redundancy reserves are extracted from historically skipped nodes using inter-layer risk comparison to form a scheduling redundancy distribution. Historically skipped nodes refer to nodes in which the workflow was previously allowed to bypass and directly proceed to subsequent nodes in previous batches. Skipping operations typically occur during periods of sudden increases in submissions, where administrators temporarily authorize bypassing some intermediate nodes to alleviate backlogs. If the process can still complete normally after bypassing, it indicates that the node has bypassable capacity in the business flow. The redundancy reserves are quantified by combining the historical skip count of each node in the inter-layer risk comparison with the load status of upstream and downstream nodes during the skipping period. Nodes with more historical skip counts and no downstream backlog during the skipping period have more sufficient redundancy reserves. For nodes with the same number of skip counts but downstream backlogs during the skipping period, their redundancy reserves are reduced according to the severity of the backlog; the more severe the backlog, the greater the reduction. The reduced reserves reflect the actual burden that the node placed on downstream nodes during past skipping operations. In the scheduling redundancy distribution, nodes with moderate redundancy margins participate in path construction under the constraint that the current batch submission volume exceeds a set multiple of the node's standard load. Nodes with moderate redundancy margins are not invoked during non-backlog periods to prevent premature exhaustion of redundancy space. Some nodes in the workflow, due to their highly overlapping approval content with preceding and following nodes, have historically been frequently skipped by administrators without causing any compliance issues. These nodes typically have the highest redundancy margin scores in the scheduling redundancy distribution, and their bypassability has been thoroughly verified in multiple historical analyses. Nodes with zero redundancy margins in the inter-layer risk comparison do not participate in cross-node migration path construction to prevent such nodes from being mistakenly included in the diversion path and compromising process integrity.

[0045] A redundant migration path table is formed based on the statistical cross-node redundant migration paths in the scheduling redundancy distribution. Starting with a node in the scheduling redundancy distribution whose redundancy margin is higher than a set lower limit, the path extends forward along the dependent link. When a node with zero redundancy margin is encountered during the extension, the path is truncated at that point, and the path segments on either side of the truncation point become independent paths. If multiple extendable paths exist between nodes with high redundancy margins in the scheduling redundancy distribution, the path that crosses the most high-risk nodes is prioritized for inclusion in the redundant migration path table. If the number of high-risk nodes crossed is the same, the path with the shorter total length is prioritized to reduce the consumption of intermediate node redundancy margins in a single migration. When the path extension passes through a node with medium redundancy and conditional constraints in the scheduling redundancy distribution, the conditional constraints of that node are simultaneously written into the path entry. During actual scheduling, the path is only included in the activation range if the condition is met; otherwise, the path is truncated before that node, and the shorter path segment participates in the scheduling. The backlog patterns in the workflow exhibit cyclical patterns, with submissions typically surging at the end of the month. Historically, administrators have repeatedly activated the same skip paths during this peak period. These paths receive the highest confidence scores in the redundant migration path table due to their high historical activation frequency. Prioritizing high-confidence paths in end-of-month batches can significantly reduce the trial-and-error costs of path scheduling. The confidence score for each path in the redundant migration path table is calculated by combining historical skip success rates with the sufficiency of redundancy. A higher score indicates a more substantial history of successful activation without triggering new backlogs in actual scheduling. Paths with confidence scores below a set lower limit are designated as backup paths. Backup paths can only be activated after all redundancy in high-confidence paths has been exhausted.

[0046] Based on the redundant migration path table, a path adaptation scheme is generated by reconfiguring node paths. Migration paths are extracted from the redundant migration path table by jointly sorting them by path confidence and coverage. Paths ranked higher are prioritized for allocation to high-level nodes in the overload hazard rating. The coverage of a path is measured by the number of high-hazard nodes between its starting and ending nodes; paths covering more high-hazard nodes have higher priority. When multiple paths in the redundant migration path table share intermediate nodes, the redundancy of these shared nodes is distributed proportionally among the paths. If the redundancy is insufficient to simultaneously handle the migration load of multiple paths, priority is given to the higher-ranked paths. Paths ranked lower are marked as backup paths, which can only be used after the redundancy of the primary path is exhausted. When a high-level node in the overload hazard rating has no corresponding coverage path in the redundant migration path table, it is usually because the surrounding nodes have zero redundancy. The overload hazard of a node without a path cannot be resolved through path migration and must be handled separately through a compensation-driven mechanism based on execution constraints. When a cluster of nodes without paths appears in a certain approval link, it indicates that the node redundancy of that link is generally low, and there have been almost no proactive skip operations historically, indicating weak overall schedulability of the link. The activation conditions for each migration path in the path adaptation scheme accurately reflect the business scenarios in which the path can be enabled. If the activation conditions are too lenient, the path will be frequently triggered and consume the reserve during unnecessary periods. If the conditions are too strict, the path will be unable to be activated when it is really needed because the conditions are not met. The path adaptation scheme calibrates the activation conditions with reference to the node load level corresponding to the historical backlog trigger time, ensuring that the triggering time of the conditions is highly consistent with the actual occurrence time of the backlog.

[0047] In some embodiments, the step of establishing an execution constraint set based on the path adaptation scheme and implementing hierarchical collaborative driving includes: summarizing the collaborative driving execution parameters of each node according to the path adaptation scheme to obtain driving execution records; screening the driving execution records for redundant approval segments that have been overdriven to determine overdriven segments and low-load nodes; conducting threshold trigger analysis of the missing approval rate for the overdriven segments to determine the driving upper limit; and using the driving upper limit to back-calculate the driving input amount of the low-load nodes to establish an execution constraint set.

[0048] The drive execution record is obtained by summarizing the collaborative drive execution parameters of each node based on the path adaptation scheme. Utilizing the activation records of each migration path in the path adaptation scheme, each path activation corresponds to one drive execution record, carrying the path number, activation time, set of involved nodes, and actual load transfer amount. The load transfer amount is calculated as the difference between the number of pending approval records for each involved node before and after path activation. When a backup path is enabled in the path adaptation scheme, a backup activation label is added to the corresponding entry in the drive execution record. A dense appearance of backup activation labels indicates that the main path's capacity allocation is consistently insufficient during that period, and there is a significant gap between the designed coverage capacity of the main path and the actual backlog size. This situation typically occurs during abnormal peak periods when the submission volume exceeds historical peak values. The capacity of the main path, as defined by historical peak values, is insufficient to support complete load balancing in ultra-peak scenarios. The time interval between adjacent activation records in the drive execution log reflects the path scheduling frequency. When the interval is consistently short, it indicates that the workflow frequently triggers path switching, and the node load oscillates back and forth between various migration paths. The oscillation is usually caused by the activation condition thresholds of multiple paths in the path adaptation scheme being set too close. After a certain path is activated, the load briefly shifts towards the target node, and the shift then triggers the activation condition of another path to pull back, forming a continuous oscillation. After identifying the oscillation pattern, the drive execution log is marked with an oscillation label in the corresponding time period. Dense batches carrying oscillation labels are given high priority and included in the comprehensive evaluation of drive efficiency during the drive segment screening stage.

[0049] By screening the drive execution records for redundant approval segments, over-driven segments and low-load nodes are identified. Over-driven refers to a situation where an approval segment is frequently scheduled in the drive execution records, but the actual load transfer volume remains low. Frequent scheduling but low transfer volume reflects that the collaborative drive initiation threshold of this segment is too low, and the backlog that is substantially relieved after path activation is far less than expected. The ratio of the actual load transfer volume to the number of activations for each node in the drive execution records is used as the drive efficiency index for that node. The segment containing a node whose efficiency index is lower than a set lower limit is identified as an over-driven segment. High-frequency and inefficient drive scheduling in the workflow often occurs at intermediate nodes located at the intersection of multiple migration paths. These nodes are repeatedly called by various paths, but due to their limited redundancy, the actual transfer volume each time is extremely small. This is particularly prominent in peak batches. During peak periods, the large number of backlogged nodes causes multiple paths to compete for the spare capacity of the same intermediate node. After the spare capacity is dispersed and consumed, the single transfer volume is generally low. Therefore, over-driven segments appear in the drive execution records of peak batches. Low-load nodes are identified as nodes whose activation frequency in the drive execution record is lower than the global average and whose number of pending records is consistently low. The approval capacity of low-load nodes has not been fully utilized under the current path allocation for a long time. Usually, the activation condition threshold for this node is set too high in the corresponding path adaptation scheme, so that it is not triggered in most batches. Directing some of the scheduling pressure of the drive segment to low-load nodes can improve the overall collaborative drive efficiency. The premise of directing is that the approval quality of low-load nodes remains stable in the historical record and the rejection rate does not show a trend of zeroing.

[0050] For over-driven approval segments, a threshold trigger analysis of the missing approval opinion rate is conducted to determine the driving upper limit. The missing approval opinion rate is calculated based on the proportion of blank approval opinion records generated by each node during frequent driving scheduling within the over-driven segment to the total number of approval records. Blank opinions reflect that approvers failed to complete substantive review before submission under high-frequency driving pressure; a higher missing opinion rate indicates more severe damage to approval quality caused by over-driving. When the missing approval opinion rate in the over-driven segment exceeds a set threshold, a driving upper limit determination is triggered. The driving upper limit is derived by inversely calculating the maximum driving activation frequency corresponding to just not exceeding the missing opinion rate threshold. The missing opinions of approvers in the over-driven segment exhibit a stepped characteristic. For example, at the end of the month, when the peak driving frequency for reimbursement approvals is no more than 20 times per day, the missing opinion rate stabilizes at 5%. After exceeding 30 times per day, the missing opinion rate surges to 25%. This jump point corresponds to the critical state where approvers go from barely completing the review to abandoning substantive review. The driving upper limit is determined based on the highest activation frequency before this jump point. In over-driven segments, segments with weak correlation between missing rate and activation frequency may have other interfering factors affecting the missing rate. The upper limit of the driving force for such segments is conservatively estimated based on the activation frequency corresponding to the 95th quantile of the historical distribution of missing rate. At the same time, a weak correlation label is added to indicate that the confidence of the upper limit of the driving force for such segments is lower than that for strongly correlated segments. Additional safety margin should be reserved during the constraint set calibration stage.

[0051] For example, the step of establishing an execution constraint set by back-calculating the priority compensation drive input of low-load nodes based on the drive upper limit includes: calculating the margin of low-load nodes based on the drive upper limit to form a margin compensation distribution; identifying nodes with historical rejection rates of zero based on the margin compensation distribution to obtain a margin compensation sequence; verifying the collaborative drive fluctuation suppression margin for the margin compensation sequence to obtain a fluctuation suppression verification result; and determining the allowable margin compensation drive input for each node based on the fluctuation suppression verification result to establish an execution constraint set.

[0052] Based on the driving limit, margin calculations are performed on low-load nodes to form a margin compensation distribution. Using the maximum activation frequency limited by the driving limit as a constraint, the difference between the current number of pending records and the historical peak load for a low-load node is calculated. This difference reflects the additional approval input that the node can handle without exceeding the driving limit. Low-load nodes with low driving limits have strictly limited additional input capacity and small margin compensation space; low-load nodes with high driving limits and current pending records significantly lower than the historical peak have larger margin compensation space and are priority targets for traffic redirection. Among low-load nodes, those with current pending records close to the historical peak have small margin values ​​or even negative margins. A negative margin indicates that the current load is close to saturation. The margin compensation distribution marks these nodes as saturated nodes, and saturated nodes are not targeted for traffic redirection to compensated nodes to prevent forcibly redirecting traffic to near-saturated nodes and exacerbating local backlog. Some low-load nodes in the workflow remain underutilized for extended periods because their approval processes have relatively lenient requirements on the format of submitted materials. Historically, these nodes have demonstrated significantly higher processing efficiency than mandatory nodes after receiving traffic diversion. These nodes typically have ample margin in the margin compensation distribution and historically high peak loads, indicating that they have handled far higher volumes during peak periods. The current low load reflects a structural bias in path allocation rather than an inherent deficiency in the node's processing capacity. In the margin compensation distribution, the margin value of each node is dynamically updated in conjunction with the driving upper limit. As the driving upper limit tightens within consecutive batches, the margin compensation space of the corresponding node is simultaneously compressed, ensuring that the compensation driving input for each batch never exceeds the actual processing upper limit of the current batch.

[0053] Based on the residual compensation distribution, nodes with historical rejection rates reaching zero are identified to obtain a residual compensation sequence. A node with a historical rejection rate reaching zero refers to a node where the number of rejection records has been consistently zero across several historical batches. For example, a leave application approval node might have zero rejection records for six consecutive months, indicating that approvers have consistently approved all submissions unconditionally. A zero rejection rate signifies that this node has lost its substantive review function. If a low-load node with a positive residual value in the residual compensation distribution also belongs to a node with a historical rejection rate reaching zero, the reliability of the residual compensation space is low. This is because even after accepting more approval inputs, this node might still unconditionally approve all content. The effect of compensation-driven processing is equivalent to a formal diversion rather than a substantial improvement in approval quality. The remaining capacity compensation distribution is screened based on the current rejection rate of nodes. Nodes with a recent upward trend in rejection rate are prioritized for inclusion in the remaining capacity compensation sequence, indicating that the approval process for that node is substantially recovering. The causes of the rejection rate increase can be further differentiated: nodes with an increase due to a change in approver have higher credibility than nodes with an increase due to a passive rejection triggered by a decline in the quality of submitted materials. The former reflects a proactive recovery in review capabilities, while the latter will return to zero as the materials improve. The remaining capacity compensation sequence is arranged in descending order of the product of the normalized remaining capacity value and the magnitude of the rejection rate increase. A higher comprehensive score indicates that the node has both sufficient capacity to handle the remaining tasks and high review credibility. Nodes ranked higher are given priority in the execution constraint set for allocating compensation-driven inputs, while nodes ranked lower can only take on the remaining inputs after all the remaining capacity of the preceding nodes has been allocated.

[0054] The fluctuation suppression verification results are obtained by verifying the collaborative drive fluctuation suppression margin in the margin compensation sequence. The collaborative drive fluctuation suppression margin is quantified as the inverse of the normalized fluctuation amplitude within the historical batches of each node in the margin compensation sequence. For example, when the processing volume of node A in the last 10 batches is 100±5 (fluctuation amplitude 0.05) and that of node B is 100±30 (fluctuation amplitude 0.30), the fluctuation suppression margin of A is 6 times that of B. The more sufficient the fluctuation suppression margin, the less likely it is to cause load oscillation due to batch differences in input volume after receiving compensation-driven input. Even if the margin value is sufficient, nodes with large load fluctuation amplitude in the margin compensation sequence are still prone to new backlogs due to unstable processing capacity when the input volume is large. The fluctuation suppression verification results are used to adjust the actual input volume that such nodes can handle downward, and the adjustment range is positively correlated with the load fluctuation amplitude. Nodes with extremely large fluctuation amplitude usually correspond to links with unstable approver configurations. Multiple part-time approvers taking turns lead to significant differences in the processing efficiency of each batch. Although such nodes may have high margin values ​​in the margin compensation sequence, the low fluctuation suppression margin significantly adjusts their actual input volume that they can handle. When multiple nodes simultaneously receive compensation-driven input during peak workflow periods, the fluctuation suppression margin of each node is jointly evaluated in the fluctuation suppression verification result. When the total fluctuation suppression margin is lower than the globally set lower limit, an overload warning is triggered. When the overload warning is activated, the node with the lowest margin at the end of the margin compensation sequence is removed from the current round of compensation drive, ensuring that the limited fluctuation suppression margin is concentrated on highly reliable nodes with sufficient margin. The removed node is marked as removed from the current round in the fluctuation suppression verification result.

[0055] Based on the fluctuation suppression verification results, the allowable margin compensation driving input for each node is determined, and an execution constraint set is established. The allowable margin compensation driving input for each node has an upper limit of the corrected acceptable value in the fluctuation suppression verification results and a lower limit of the set ratio of the margin calculated by the margin compensation distribution. The range between the upper and lower limits is the allowable input range for that node. Nodes with a narrower range have higher requirements for precise control of the input, while nodes with a wider range have greater flexibility in adjusting the input between batches. Nodes that exit due to overload warnings in the fluctuation suppression verification results have their allowable input set to zero in the execution constraint set, and their participation qualification is restored after the fluctuation suppression margin recovers in the next batch. If some nodes repeatedly exit due to overload warnings, such as a node whose load fluctuation range has increased from 0.1 to 0.4 in the last 6 batches due to the rotation of part-time approvers, the execution constraint set adds a continuous exit warning to such nodes, indicating that the unstable processing capacity of the node has been accumulating and intervention is needed at the approver configuration level. The execution constraint set dynamically updates the input volume on a batch-by-batch basis by compensating for the allowable margin of each node. Nodes whose upper limit of allowable input volume continues to decrease usually correspond to a gradual degradation of processing capacity. The upper limit adjustment of the execution constraint set for such nodes tightens rapidly as the slope of the degradation trend increases, preventing degraded nodes from continuously accepting input volume exceeding their actual processing capacity due to constraint lag. At the same time, the rapidly tightening constraints send a warning signal to the workflow management layer that the node will soon be unable to accept any compensating input.

[0056] Step S104: Based on the execution constraint set, coordinate the execution weight and trigger threshold of the node reorganization strategy to generate a parameter change log. Based on the parameter change log, verify the overlap between the approver and the submitter's identities to generate an operational anomaly interference sequence. Through the operational anomaly interference sequence, identify the first rejection node and generate a circulation health score.

[0057] Specifically, based on the execution constraint set, the execution weights and trigger thresholds of the node reorganization strategy are coordinated and adjusted to generate parameter change logs. Input volume is compensated based on the allowable margin of each node in the execution constraint set. For nodes with high input volume upper limits, the corresponding field dependency weights in the node reorganization strategy are increased by multiplying the relative deviation by a preset step size. Nodes with zero input volume are forced to have their weights reduced to the lowest level. The lowest level retains non-zero weights to maintain the node's existence in the topology structure and prevent topology breakage caused by zero weights. Trigger threshold adjustment is based on the lower limit of the allowable input volume range for each node in the execution constraint set. Nodes with low lower limits have their trigger thresholds increased to require higher load accumulation to activate the corresponding migration path. Nodes with continuous exit warnings in the execution constraint set have their trigger thresholds additionally configured with bias, after which the node is downgraded from an active scheduling role to an emergency standby role. In the workflow, parameter adjustments are executed centrally at batch boundaries, without triggering mid-batch adjustments. This avoids abrupt changes in node dependencies caused by parameter changes during batch execution. During peak seasons with extremely short batch intervals and dense batch boundaries, the entry density of parameter change logs is significantly higher than during off-seasons. This sudden increase in density can serve as an auxiliary signal for judging abnormal process load. In the node reorganization strategy, the field dependencies of non-migratable nodes do not participate in weight allocation. The corresponding weights are marked as frozen in the parameter change log. The frozen weights remain unchanged until the node's eligibility is reassessed by executing the constraint set. Nodes whose weights continuously decrease within consecutive batches form a monotonically decreasing sequence in the parameter change log, indicating that the node's credibility is continuously deteriorating within consecutive batches.

[0058] In some embodiments, the step of generating an operational anomaly interference sequence based on verifying the overlap between the approver and submitter identities in the parameter change log includes: extracting approval operation records for each node from the parameter change log to obtain a node operation sequence; identifying the approval time interval rhythm distribution in the node operation sequence to form a rhythm solidification distribution; locking the rhythm solidification rise segment based on the rhythm solidification distribution to obtain a solidification expansion record; and determining the abnormal amplitude of the overlap between the approver and submitter identities based on the solidification expansion record to generate an operational anomaly interference sequence.

[0059] The node operation sequence is obtained by extracting approval operation records for each node from the parameter change log. Using the allocation operation time of each node in the parameter change log as a time anchor, the corresponding node's approval operation entries are extracted from the approval system operation logs before and after the anchor. The extraction range covers the complete batch interval from the last allocation to the current allocation. Each approval operation record carries the operation time, operator identification, operation type, and operation result. Nodes whose weights are reduced in the parameter change log typically show a decrease in the frequency of approval operations within the corresponding batch interval. The decrease is positively correlated with the weight allocation magnitude. When the frequency decrease meets expectations, it indicates that the weight allocation effect is taking effect. If no significant decrease is observed, it reflects that although the node's weight is lowered, it is still frequently activated by other paths. The node operation sequence is marked with "allocation not effective" in the corresponding interval of such nodes. A dense appearance of "allocation not effective" marks indicates excessive overlap in the node reorganization strategy path design. Nodes with this mark are prioritized for verification of their solidification degree during the rhythm solidification distribution identification stage. The node operation sequence is organized along two dimensions: node identifier and batch time sequence. The approval operation records of each node are arranged in chronological order within a batch, and the sequence across batches supports the detection of long-term evolution trends in approval behavior. Nodes whose approval operation frequency monotonically decreases within consecutive batches are usually in the process of gradually withdrawing from active approval. In the workflow, such nodes often correspond to approvers gradually reducing their substantive involvement in the node due to work adjustments. The difference between this and the frequency decrease caused by weight allocation is that the former is not accompanied by weight change records in the parameter change log. The two causes can be distinguished in the node operation sequence by comparing the consistency between the weight change records and the frequency change time sequence. The frequency decrease when the two time sequences match is attributed to the allocation effect, while the frequency decrease when the time sequences do not match indicates the existence of spontaneous degradation of approval behavior independent of allocation operations.

[0060] The rhythmic distribution of approval time intervals in the node operation sequence is identified to form a rhythmic solidification distribution. Referring to the time interval between two adjacent approval operations at each node in the node operation sequence, the distribution shape of the time interval reflects the approval rhythm of that node. A wide and dispersed distribution indicates significant differences in the processing time of each submitted material by the approver, usually corresponding to substantive review. A narrow and concentrated distribution indicates a highly regularized approval time interval, with the processing time of each material approaching a fixed value. This pattern does not naturally occur in normal approvals and often corresponds to approvers processing materials in batches in a procedural manner without conducting individual content review. The degree of solidification of the approval time interval distribution in the node operation sequence is quantified by the ratio of peak height to peak width. The narrower the peak width and the more prominent the peak height, the higher the degree of solidification. When the degree of solidification exceeds a set threshold, the corresponding node is determined to be a rhythmic solidification node. The rhythmic solidification distribution unfolds over time the trend of the solidification degree of each node evolving with each batch. Nodes where the solidification degree increases within consecutive batches indicate that the approver's operational behavior is becoming more mechanized. This may be due to the continuous increase in submission volume forcing approvers to compress the review time per session, or it may be due to a tacit cooperation between submitters and approvers—submitters ensuring the content meets the minimum formal requirements, and approvers defaulting to approval—resulting in a stable approval interval at a very short fixed value. The two causes exhibit similar patterns in the rhythmic solidification distribution, but the former has a corresponding growth curve in the submission volume data as a distinguishing criterion. In the node operation sequence, the approval interval of some nodes shows a bimodal distribution, with one peak concentrated in the extremely short interval range and the other peak distributed in the normal interval range. For example, in the review node rotation, senior employees review for an average of 8 minutes, while new employees approve in batches in 30 seconds, forming a typical bimodal distribution. The rhythmic solidification distribution records the degree of solidification of the bimodal distribution for such nodes. The proportion of the area of ​​the short interval peak in the bimodal structure to the total distribution area reflects the degree of penetration of procedural approval behavior at that node.

[0061] Based on the rhythmic solidification distribution, the rising segment of rhythmic solidification is identified to obtain the solidification expansion record. The rising segment of rhythmic solidification is defined as the batch interval in the rhythmic solidification distribution where the solidification degree continuously exceeds a set threshold and increases batch by batch. The increasing trend reflects that the rhythmic solidification behavior is expanding rather than maintaining a stable level. Situations where only a single batch exceeds the threshold while the preceding and following batches are normal are not included in the rising segment of solidification to avoid mistaking occasional concentrated processing behavior for systemic solidification expansion. When the rising segment of solidification in the rhythmic solidification distribution continuously covers multiple nodes at the node dimension, it indicates that the solidification behavior has spread from a single node to multiple links in the approval chain. In the workflow, such multi-node linked solidification usually corresponds to approvers throughout the entire chain experiencing the same pressure at the same time, with a sudden increase in submissions or a simultaneous decrease in approvers causing a general compression of the approval time across the entire chain. Isolated solidification at a single node usually points to individual behavioral changes of the approver at that node, unrelated to the overall pressure of the chain. The solidification expansion record of an isolated solidified node involves only a single node, while the set of linked solidification segments spans multiple adjacent nodes. In the rhythmic solidification distribution, segments with steeper slopes of solidification are marked as rapidly expanding segments in the solidification expansion record. Rapid expansion typically corresponds to a sudden, mechanized change in the behavior of approvers shortly after a triggering event. This triggering event could be a surge in submission volume or an approver actively avoiding substantive review. The subsequent evolution directions of these two scenarios are drastically different. Rapidly expanding segments are included in the trigger cause verification scope in the solidification expansion record to highlight the need for careful identification during subsequent identity overlap verification. Segments with higher peak solidification levels in the solidification expansion record undergo stricter overlap verification during the approver and submitter identity overlap verification stage. This correlation stems from the typical symbiotic relationship between highly solidified approval time intervals and identity overlap behavior. Identity overlap operations do not require a substantive review process, thus the approval time interval is highly concentrated around a fixed, extremely short value.

[0062] An operational anomaly interference sequence is generated based on the abnormal amplitude of overlap between the approver and submitter identities in the solidified expansion records. Overlapping approver and submitter identities are verified using the identity identifier field in the operation logs within each solidified elevation segment of the solidified expansion records. When the submitter identifier and approver identifier of the same approval operation record are identical, it is considered an overlapping operation. The proportion of overlapping records to the total number of approval operation records within that segment is used as the overlap rate for that segment. Segments with a high solidification level peak in the solidified expansion records typically also have a high overlap rate, because when the approver and submitter are the same person, no substantive review process is required, and the approval time naturally tends to a fixed, extremely short value. Time solidification and identity overlap mutually corroborate each other in these segments. Segments with a high solidification level but a low overlap rate indicate that the mechanized passage behavior at this node stems from the approver voluntarily abandoning review rather than identity overlap. These two scenarios correspond to different sources of interference in the operational anomaly interference sequence: the former points to issues of access control, while the latter points to issues of approval attitude. In some workflow nodes, the submitter is allowed to also act as the approver for that node under specific conditions. Such authorized compliant overlaps are marked with a concurrent authorization tag in the system, and overlap records with authorization tags are not included in the abnormal amplitude calculation. When authorized and unauthorized overlap records coexist in the same node, a mixed overlap tag is added to the corresponding segment in the operational anomaly interference sequence, indicating that the overlap behavior of that node has a complex source, and that compliant concurrent operation may provide cover for unauthorized overlap. The time distribution of the two types of records usually differs, with compliant concurrent operation occurring at a dispersed time, while unauthorized overlap occurs in a concentrated period. Segments with an overlap rate exceeding a set threshold are identified as identity overlap abnormal segments. The product of the overlap rate and the peak value of the segment's solidification level is taken as the abnormal amplitude of that segment. The operational anomaly interference sequence is arranged in batch chronological order by the abnormal amplitude of each solidified and elevated segment. Batch segments with continuously high amplitudes usually reflect that the approval process is subjected to continuous operational anomalies during that period.

[0063] In some embodiments, the step of identifying the first-barrel node and generating a circulation health score through the abnormal operation interference sequence includes: identifying the oscillation segment of the abnormal operation interference sequence to determine a pseudo-health window; reducing the pseudo-health window and screening the baseline rise segment to obtain a true stabilization window; performing reverse verification of the first-barrel node on the true stabilization window to obtain a stability verification result; and conducting circulation uniformity assessment based on the stability verification result to generate a circulation health score.

[0064] Identifying stabilizing oscillation segments in operational anomaly sequences to determine pseudo-health windows. These segments are identified as batches within the operational anomaly sequence where the amplitude decreases from a high value range to a low value range, but then continues to oscillate slightly within the low value range. While the decrease in amplitude superficially indicates that the operational anomaly is easing, the continued oscillation within the low value range reveals that the source of the disturbance has not yet been eliminated. Batch segments in the operational anomaly sequence where the amplitude remains consistently low and the oscillation amplitude is extremely small correspond to genuine disturbance resolution. The difference between these and stabilizing oscillation segments is that the latter's oscillation amplitude within the low value range exceeds the set fluctuation tolerance, which is written into the configuration parameters during the initialization phase based on the standard deviation of historical normal amplitudes. Stabilizing oscillation segments are common in actual workflows when a certain type of abnormal operation is discovered, briefly avoided, and then quietly reappears. After noticing overlapping operations being recorded, approvers deliberately increase the approval interval in the short term to avoid fixed feature detection, and then revert to their original operating habits after several batches. In such cases, the operational anomaly sequence typically exhibits a pattern of amplitude first decreasing and then slightly oscillating. If the mean amplitude of the oscillation center in the stabilizing oscillation segment of an abnormal interference sequence decreases compared to the higher value range, it indicates that the avoidance operation has indeed partially suppressed the abnormal behavior. The greater the decrease in the center, the more obvious the avoidance effect. However, if the center is still higher than the historical normal baseline, the abnormal behavior has not been completely eliminated. If the oscillation center does not decrease or even increases, it suggests that the avoidance operation is only a superficial action, and the actual intensity of the abnormal operation has not been affected. The pseudo-health window accurately covers the entire range from the decrease in amplitude to the end of the oscillation. When the amplitude increases outside the pseudo-health window, it indicates that the abnormal operation has recovered to the interference intensity before it was avoided.

[0065] To identify the true stable window, the pseudo-health window is reduced and the baseline-raising segment is screened. The time boundary of the pseudo-health window is contracted inward from both ends, with the contraction amount determined by the oscillation amplitude; the larger the oscillation amplitude, the greater the contraction. This contraction removes the most unstable batch segments at both ends of the pseudo-health window, retaining the relatively stable core interval in the middle. The unstable segments at both ends of the pseudo-health window typically correspond to the following scenarios in the workflow: the front end of the window represents a transitional batch where abnormal behavior suddenly switches from high to low intensity, and the approver's operation has not yet fully adjusted, resulting in large amplitude fluctuations; the back end of the window represents the initial batch where abnormal behavior begins to slowly recover, with the amplitude showing a weak but continuous upward trend. The existence of these two types of boundary segments lowers the overall credibility of the pseudo-health window. After reducing the operation to remove the two ends, the amplitude stability within the true stable window is higher. Baseline rise screening detects whether there is an overall rise in the amplitude baseline of abnormal interference sequences within the reduced core interval. The baseline is estimated using the lower quartile of the amplitude sequence within the core interval. Baseline rise is defined as a rise exceeding a set proportion compared to the historical baseline before pseudo-health window identification, indicating that even in low amplitude intervals, the overall interference level is higher than the historical normal level. The residual interval of the pseudo-health window after reduction and baseline rise screening is identified as the true stabilization window. A narrow window width indicates that the overall operational quality of the workflow remains low. In extreme cases, the residual interval of the pseudo-health window is empty after double screening, and the true stabilization window is missing in that batch. The first-stop node reverse verification uses the verification results of the historical window as the estimation benchmark in the missing batch. The missing batch is classified as a downgrade estimation item in the workflow health score.

[0066] For example, the step of performing reverse verification of the first-barrel node on the real stabilization window to obtain a stability verification result includes: extracting the flow micro-amplitude rise features before the first-barrel in the real stabilization window to obtain a preceding micro-rise sequence; identifying the preceding micro-rise attenuation segment based on the preceding micro-rise sequence to determine the signal attenuation segment; determining the first-barrel disappearance boundary for the signal attenuation segment by measuring the time when the preceding micro-rise returns to zero; and evaluating the flow credibility based on the first-barrel disappearance boundary to obtain a stability verification result.

[0067] The subtle increase characteristics of the flow before the first rejection are extracted from the actual stabilization window to obtain the preceding subtle increase sequence. The extraction range is defined as several frames of approval operation records before the first rejection operation time for each batch within the actual stabilization window. Within this range, the presence of a subtle decrease in the approval pass rate is detected. A subtle decrease in the pass rate indicates that a small number of approval opinions have changed from approval to retention before the first rejection, serving as a precursor signal of a stricter pre-review attitude from the approvers before the first rejection event. The preceding subtle increase amplitudes for each batch within the actual stabilization window are arranged chronologically to form the preceding subtle increase sequence. A persistently large amplitude indicates that the approval process has already shown significant pre-warming of oversight before the first rejection, typically corresponding to the approvers having completed a sufficiently in-depth pre-review of the submitted content before the first rejection. The existence of this pre-review process is important evidence of the credibility of the first rejection operation. A small amplitude in the initial micro-rise sequence suggests that the initial rejection was rather sudden, indicating insufficient awareness of oversight. In batches with high amplitudes of identity overlap anomalies within the true stabilization window, the amplitude of the initial micro-rise sequence is usually extremely low or even zero. This is because there is no genuine pre-review process under abnormal operating scenarios. Initial rejections in such batches typically stem from the independent judgment of individual approvers who were not involved in the abnormal operation, and these rejections have high credibility in stable verification results. Nodes in the initial micro-rise sequence where the amplitude shows a regular rise and fall across batches usually correspond to approvers' work status being affected by external cyclical factors. Month-end performance evaluation pressure leads approvers to appropriately increase review rigor before the end-of-month batches, and the pre-review signal declines after the pressure eases at the beginning of the month. This cyclical pattern forms a fixed rhythm in the initial micro-rise sequence, which can be directly distinguished from irregular random fluctuations.

[0068] The signal attenuation segment is determined by identifying the pre-aperture attenuation segment based on the pre-aperture micro-rise sequence. Attenuation is identified when the amplitude value in the pre-aperture micro-rise sequence decreases continuously across multiple batches, with the decrease exceeding a certain percentage of the average. This attenuation reflects a weakening of the pre-aperture signal, indicating that the pre-aperture review process by the approver is being compressed or omitted. The attenuation pattern of the amplitude value in the pre-aperture micro-rise sequence can be divided into gradual attenuation and sudden attenuation. Gradual attenuation indicates that the pre-aperture signal gradually fades with each batch, usually corresponding to a gradual weakening of the review process under external pressure. The submission volume usually shows a synchronous upward trend during this period, and the consistency of the timing of these two events can serve as evidence for the cause of gradual attenuation. Sudden attenuation indicates that the amplitude value drops sharply to a low level in a certain batch, usually corresponding to a concentrated outbreak of operational anomalies in that batch. The operational anomaly interference sequence usually shows a synchronous amplitude jump in that batch, and the consistency of the timing of these two events serves as the basis for confirming sudden attenuation in the identification of signal attenuation segments. Signal attenuation segments are identified by the start and end numbers of consecutive attenuation batches. Gradual attenuation begins with the first batch where all three batches decrease in value, while sudden attenuation begins with a single batch whose decrease exceeds 50% of the global average of the preceding micro-up sequence. The two types of attenuation are identified using different starting methods to ensure that the boundary positioning of the signal attenuation segment matches the actual attenuation trigger time. When multiple attenuation segments exist simultaneously in the preceding micro-up sequence, each attenuation segment is independently identified as a signal attenuation segment. When multiple signal attenuation segments appear consecutively in the batch sequence, it indicates that the first-pass precursor signal has been continuously interfered with for a long period of time, and the stability of the workflow's control signal is generally low during this period. If the attenuation type of each signal attenuation segment is sudden, it usually corresponds to the approval link being subjected to multiple rounds of intermittent abnormal impacts; if they are all gradual, it corresponds to a round of continuously accumulating pressure erosion.

[0069] The first rejection boundary is obtained by determining the zeroing time of the pre-amplitude rise in the signal attenuation section. The zeroing time of the pre-amplitude rise is defined as the batch in which the amplitude value of the pre-amplitude rise sequence in the signal attenuation section first falls below the noise baseline and remains below the baseline. The noise baseline is estimated by the lower 5 quantile of the pre-amplitude rise amplitude distribution of normal batches within the true stabilization window. The 5 quantile is written into the configuration parameters during the initialization stage based on the background noise level of the pre-amplitude rise sequence amplitude in historical batches. Historical data shows that samples with amplitude values ​​lower than this quantile in normal batches basically correspond to background noise generated by no operation by the approver or system idling. Taking this quantile as the baseline can effectively distinguish between the actual control signal attenuation and background noise fluctuations. The rebound after the amplitude value drops below the baseline within the signal attenuation section indicates a brief interruption in the precursor signal of the first refueling. The rebound after the interruption suggests that the gatekeeping behavior of the approval link has resumed after a brief period of pressure. The brief interruption does not trigger the determination of the first refueling disappearance boundary. It is only determined as the first refueling disappearance boundary after the amplitude value has been continuously maintained below the baseline for more than a set number of batches. The set number of batches is written into the configuration parameters during the initialization stage based on the shortest duration of the historical gatekeeping signal decline. Too short a duration may cause occasional low amplitude values ​​to be mistakenly identified as the first refueling disappearance boundary, while too long a duration may cause the true decline to be delayed in recognition. The disappearance boundary of the first barge in the workflow usually coincides with the amplitude jump in the abnormal operation interference sequence. When the timing consistency between the two is high, the disappearance boundary of the first barge is marked as a high confidence boundary in the stability verification result; when the consistency is low, it is marked as a low confidence boundary. The appearance of a low confidence boundary suggests that the reason for the zeroing of the preceding micro-increase may not be entirely due to abnormal operation interference. The approver’s active adjustment of the operation strategy or the change in the structure of the node submission volume can cause the amplitude of the preceding micro-increase to naturally return to zero. If the attenuation type of the signal attenuation section is gradual and there is no obvious amplitude change in the abnormal operation interference sequence at the same time, the disappearance boundary of the first barge tends to be marked as a low confidence boundary.

[0070] The reliability of the workflow was assessed based on the first-ship disappearance boundary to obtain stable verification results. The reliability was quantified according to the positional relationship between the first-ship disappearance boundary and the actual stabilization window. When the first-ship disappearance boundary falls within the actual stabilization window, the zeroing of the first-ship precursor signal occurs during a period of relatively low interference. For example, if the first-ship disappearance occurred during normal operation in March without any corresponding external anomalies, the reliability score would be low. When the first-ship disappearance boundary falls outside the actual stabilization window during a period of high interference, the fading of the first-ship precursor signal highly coincides with the peak period of operational anomalies, resulting in a higher reliability score in the stability verification results. The farther the boundary point is from the outer boundary of the actual stabilization window, the more prominent the dominant role of external interference, and the reliability score increases linearly with distance up to the upper limit. In some batches within the workflow, multiple first-ship disappearance boundaries exist, corresponding to multiple nodes in the link losing their first-ship capability successively. In the case of multiple boundaries, the reliability scores of each boundary are calculated independently and then weighted and merged according to the dependency weight of the corresponding node in the node reorganization strategy. Nodes with higher dependency weights have a greater impact on the overall reliability of the workflow when their first-ship disappearance occurs. When the initial rejection boundary is marked as a low-confidence boundary, the circulation confidence score is replaced with the lower quartile of the circulation confidence distribution of normal batches within the true stabilization window, to prevent the uncertainty of the low-confidence boundary from inflating the score. The final confidence score of the stability verification result is a weighted combination of the circulation confidence score and the score of the high-confidence boundary in the true stabilization window. When both scores are low, the stability verification result is marked with double anomalies. These double anomalies trigger an additional downgrade of the health score during the circulation uniformity assessment stage, and the magnitude of the downgrade is positively correlated with the degree of joint lower-than-normal levels of the two scores.

[0071] Based on the stability verification results, a workflow uniformity assessment is conducted to generate a workflow health score. The distribution of credibility scores for each node in the stability verification results is used to assess the uniformity of review substantive content across workflow nodes in the current batch. High uniformity indicates that the review substantive content of all nodes is at a similar level, while low uniformity indicates that some nodes have higher review substantive content while others have lower content, resulting in uneven distribution of review responsibility within the link. The standard deviation D of the credibility scores for each node in the stability verification results is used as the original uniformity indicator. A large D usually corresponds to a node in the link whose first rejection boundary disappears significantly earlier than other nodes. This means that the review substantive content of that node enters the degradation stage before the entire link, and other nodes bear higher gatekeeping pressure after that node becomes ineffective, leading to a significant divergence in the credibility scores of each node. A common scenario in workflows is that the first node in the approval chain, facing the highest submission volume for an extended period, exhibits stagnant characteristics first. The pressure to control then shifts to intermediate nodes, leading to a short-term increase in rejection rates. Conversely, the tail nodes, with reduced actual arrival volumes, maintain higher credibility. This divergence in node scores results in a typical distribution of low scores at the beginning and high scores at the end in stable verification results, causing a significant increase in D and a significant decrease in uniformity. The workflow health score H = λ·U + (1-λ)·V, where U = 1-D / D_max is the normalized value of the uniformity index (D_max is the upper limit of the historical batch standard deviation), V is taken from the final credibility score mentioned at the end of the stable verification results, and λ is a weighting coefficient (0.4 ≤ λ ≤ 0.6, default 0.5). When uniformity issues are prominent, λ shifts towards 0.6, increasing the weight of U; when node credibility fluctuates drastically, λ shifts towards 0.4, increasing the weight of V. A low U leads to a downward correction of H, with the correction magnitude positively correlated with the degree to which U deviates from the normal level. A low U or V results in H falling below the baseline level. When H remains below twice the historical average by two standard deviations, a health warning is triggered, indicating that the approval process has entered an unhealthy operating state.

[0072] Step S105: Based on the flow health score, reverse the missing positions and filling conditions of the rejection dense period to generate the arrangement structure parameters, and execute the path level allocation to generate the form and workflow node linkage arrangement instructions according to the arrangement structure parameters.

[0073] Specifically, the scheduling structure parameters are generated by reverse-engineering the missing locations and replenishment conditions during periods of concentrated rejections based on the circulation health score. Batch segments with circulation health scores below the baseline level correspond to substantive deficiencies in the core review of key points in the approval process during that period. The reverse-engineering operation uses the historical circulation health batch rejection distribution as a reference benchmark, identifying the nodes and time periods where rejection events occur in the reference benchmark as theoretical rejection-intensive areas. These are then compared with the actual rejection distribution of the current batch; the nodes in the difference set are the missing nodes, and the time periods corresponding to the difference set are the missing time periods. Missing nodes in batches with double anomaly markings in the circulation health score typically cover multiple key links in the approval process. The range of missing distributions identified by the reverse-engineering operation in these batches is significantly wider than in batches with single anomalies. These batches correspond to stronger replenishment constraints in the scheduling structure parameters. The supplementary conditions are extracted based on the field content characteristics corresponding to the rejection events triggered by the missing nodes in the historical healthy batches. These field content characteristics include specific rules such as reimbursement amount > 50,000 yuan, contract terms containing "exclusive agency," or application submission 3 days before approval. The finer the granularity of the supplementary conditions, the more accurately the orchestration structure parameters characterize the content boundaries that trigger rejections. In the workflow, some missing nodes have a relatively low rejection rate in the historical healthy batches, and their theoretical rejection density is weaker than that of nodes with high rejection rates. The trigger threshold for supplementary conditions is correspondingly relaxed for these nodes to prevent overly strict supplementary conditions from being frequently triggered on low-rejection-rate nodes, which could lead to new overdrive problems. The orchestration structure parameters are centered on the set of supplementary conditions for all missing nodes. The supplementary conditions are arranged hierarchically according to the severity of the missing status. The batch with the longest missing period and the most missing nodes corresponds to the mandatory rules in the orchestration structure parameters, while the supplementary constraints for other batches correspond to auxiliary rules. These two types of rules are executed layer by layer in the path-level allocation.

[0074] Based on the orchestration structure parameters, execution path hierarchy allocation generates form and workflow node linkage orchestration instructions. The priority of each node in the orchestration structure parameters is used as the hierarchical basis. Nodes corresponding to mandatory rules are assigned to the top-level path, while nodes corresponding to auxiliary rules are assigned to the middle layer. After the hierarchy is defined, the trigger signals for each field are rerouted according to the field's hierarchy and the priority rule for addition. After the trigger relationship is reconstructed, each instruction is transformed into a form and workflow node linkage orchestration instruction. Each instruction consists of five elements: trigger field identifier, trigger condition range, target node identifier, activation priority, and execution sequence. When the workflow health score identifies a long-term absence of initial rejection at a certain intermediate review node, the orchestration structure parameters generate an addition rule. The corresponding instruction uses the application type field as the trigger entry, the historical health batch rejection trigger value range as the condition constraint, the review node as the target, the activation priority as the top level, and the execution sequence as being triggered after the initial review node completes. The review node thus re-intervenes in the substantive judgment of the submitted content that has passed the initial review. When multiple fields jointly trigger the same node, the form and workflow node linkage orchestration instructions combine the condition ranges of each field into a joint trigger condition using logical AND relationships. The target node is only activated when all participating fields simultaneously meet their respective conditions. For fields with redundant activation before allocation, only the trigger relationship of the highest-level node is retained according to the hierarchical matching rules. Low-level redundant trigger entries are marked as disabled in the form and workflow node linkage orchestration instructions. Disabled entries are skipped after the workflow engine loads. When the peak batch concurrent activation volume exceeds the processing limit, it is truncated according to the activation priority. The instructions of the node corresponding to the enforcement rule are responded to first under any load conditions.

[0075] To implement the form and workflow node linkage orchestration method corresponding to the above method embodiment, in order to achieve the corresponding functions and technical effects. See also Figure 2 , Figure 2 This diagram illustrates a structural block diagram of a form and workflow node linkage orchestration system 200 provided in an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The form and workflow node linkage orchestration system 200 provided in this embodiment includes:

[0076] Data verification module 201 is used to collect field configuration data and node dependency data, and to perform rejection record backtracking comparison and verification between the field configuration data and the node dependency data to generate node linkage deviation.

[0077] The orchestration evaluation module 202 is used to perform reverse submission anomaly pre-judgment on the field configuration data to generate a trigger centralized distribution, measure the field activation decrease rate of the three stages of input, judgment and archiving from the field configuration data to generate a stage activation difference coefficient, and perform field dependency unidirectional migration to build a node reorganization strategy based on the trigger centralized distribution and the stage activation difference coefficient.

[0078] The path decision module 203 is used to perform a pre-scan to generate an overload hazard rating based on the node linkage deviation degree and the node reorganization strategy, perform path adaptation to generate a path adaptation scheme based on the overload hazard rating, and implement hierarchical collaborative drive to establish an execution constraint set based on the path adaptation scheme.

[0079] The load control module 204 is used to coordinate the execution weight and trigger threshold of the node reorganization strategy according to the execution constraint set to generate a parameter change log, verify the overlap of the identities of the approver and the submitter based on the parameter change log to generate an operational anomaly interference sequence, and identify the first rejection node through the operational anomaly interference sequence to generate a circulation health score.

[0080] The topology generation module 205 is used to generate arrangement structure parameters based on the flow health score to reverse the missing positions and filling conditions of the rejection dense period, and to execute path hierarchical allocation generation form and workflow node linkage arrangement instructions according to the arrangement structure parameters.

[0081] The form and workflow node linkage orchestration system 200 described above can implement one of the form and workflow node linkage orchestration methods in the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining content of this application embodiment can be referred to the content of the above method embodiments, and will not be repeated in this embodiment.

[0082] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A method for linking and arranging forms and workflow nodes, characterized in that, include: Collect field configuration data and node dependency data, and perform a backtracking comparison and verification of the field configuration data and the node dependency data to generate node linkage deviation. The field configuration data is subjected to reverse submission anomaly pre-judgment to generate a trigger centralized distribution. The field activation decrease rate of the three stages of entry, judgment and archiving is measured from the field configuration data to generate a stage activation difference coefficient. Based on the trigger centralized distribution and the stage activation difference coefficient, a field dependency unidirectional migration is carried out to build a node reorganization strategy. Based on the node linkage deviation and the node reorganization strategy, a pre-scan is performed to increase the opinion similarity rate, generating an overload hazard rating. The overload hazard rating is then adapted to a path adaptation scheme, and a hierarchical collaborative drive is implemented to establish an execution constraint set based on the path adaptation scheme. Based on the execution constraint set, the execution weight and trigger threshold of the node reorganization strategy are coordinated to generate a parameter change log. Based on the parameter change log, the overlap between the approver and the submitter is verified to generate an operational anomaly interference sequence. The first rejection node is identified through the operational anomaly interference sequence to generate a circulation health score. Based on the flow health score, the missing positions and filling conditions of the rejection dense period are deduced to generate the arrangement structure parameters. According to the arrangement structure parameters, the path level allocation is executed to generate the form and the workflow node linkage arrangement instruction.

2. The method according to claim 1, characterized in that, The step of performing a backtracking comparison and verification of the field configuration data and the node dependency data to generate node linkage deviation includes: Extract the zero-rejection duration from each field in the field configuration data to obtain the zero-rejection silent sequence; The reverse anomaly density in each time period is statistically analyzed for the zero-rejection silent sequence to form a reverse silent distribution; Based on the aforementioned inverse silent distribution, the zero rejection false field is defined to determine the linkage pseudo-stable interval; Based on the linkage pseudo-stable interval and the node dependency data, a node dependency mapping comparison is performed to generate the node linkage deviation.

3. The method according to claim 1, characterized in that, The node reorganization strategy based on the field-dependent unidirectional migration constructed according to the trigger centralized distribution and the stage activation difference coefficient includes: The activation decrease distribution is obtained by extracting the activation change amplitude of each node in the trigger distribution. The activation reduction distribution is compared with the stage activation difference coefficient to locate the critical node that triggers the interruption. For the aforementioned trigger interrupt critical node verification field, the trigger continuity confirmation triggers the interrupt failure section and the adjacent high-efficiency trigger section; A node reorganization strategy is generated by performing field-dependent unidirectional migration based on the triggered interruption failure segment and the adjacent high-efficiency trigger segment.

4. The method according to claim 1, characterized in that, The process of generating a path adaptation scheme for the overload hazard rating includes: The risk level of each node in the overload hazard rating is investigated layer by layer to form an inter-layer risk comparison; The redundancy of historical actively skipped nodes is extracted from the inter-layer risk comparison to form a scheduling redundancy distribution; Based on the aforementioned scheduling redundancy distribution, a redundant migration path table is formed by statistically analyzing cross-node redundant migration paths. Based on the redundant migration path table, the node path reconfiguration is performed to generate a path adaptation scheme.

5. The method according to claim 1, characterized in that, The step of establishing an execution constraint set based on the path adaptation scheme and implementing hierarchical collaborative driving includes: Based on the path adaptation scheme, the collaborative driver execution parameters of each node are summarized to obtain the driver execution record; The driver execution records are screened to identify redundant approval segments that have been over-driven and low-load nodes. For the over-driven section, conduct an approval opinion missing rate threshold trigger analysis to determine the driving upper limit; Based on the upper limit of the drive, the low-load node priority compensation drive input is used to establish an execution constraint set.

6. The method according to claim 1, characterized in that, The step of generating an operational anomaly interference sequence based on the overlap between the approver's and submitter's identities in the parameter change log includes: Extract the approval operation records of each node from the parameter change log to obtain the node operation sequence; The node operation sequence is used to identify the approval time interval rhythm distribution and form a fixed rhythm distribution; Based on the rhythmic solidification distribution, the rhythmic solidification lifting segment is locked to obtain a solidification expansion record; Based on the aforementioned solidified expansion record, the abnormal amplitude of the overlap between the identity of the approver and the submitter is used to generate an operational anomaly interference sequence.

7. The method according to claim 1, characterized in that, The step of identifying the first-barrel node and generating a circulation health score through the abnormal operation interference sequence includes: For the abnormal operation interference sequence, identify the disturbance stabilization oscillation segment and determine the pseudo-health window; The pseudo-health window is reduced and the baseline rise segment is filtered to obtain the true stabilizing window; The stability verification result is obtained by performing reverse verification at the first barge node on the actual stabilization window; Based on the stability verification results, a circulation uniformity assessment is conducted to generate a circulation health score.

8. The method according to claim 5, characterized in that, The step of establishing an execution constraint set by inversely calculating the low-load node priority compensation drive input based on the drive upper limit includes: Based on the aforementioned driving upper limit, a margin calculation is performed on the low-load nodes to form a margin compensation distribution; Based on the aforementioned margin compensation distribution, nodes with historical rejection rates that have reached zero are identified to obtain the margin compensation sequence; The fluctuation suppression verification result is obtained by verifying the margin compensation sequence and co-driving fluctuation suppression margin. Based on the fluctuation suppression verification results, the allowable margin compensation driving input of each node is determined to establish an execution constraint set.

9. The method according to claim 7, characterized in that, The process of performing reverse verification of the first barge node on the real stabilization window to obtain the stability verification result includes: Extracting the flow micro-lift features before the first barge in the real stabilization window yields the preceding micro-lift sequence; Based on the aforementioned pre-amplifier sequence, the signal attenuation segment is determined by identifying the pre-amplifier attenuation segment; For the signal attenuation section, the first barge disappearance boundary is obtained by measuring the pre-rise micro-rise zeroing time; Based on the disappearance boundary of the first barge, the reliability of the transfer is assessed to obtain a stable verification result.

10. A form and workflow node linkage orchestration system, characterized in that, include: The data verification module is used to collect field configuration data and node dependency data, and to perform a backtracking comparison and verification of the field configuration data and the node dependency data to generate node linkage deviation. The orchestration and evaluation module is used to perform reverse submission anomaly pre-judgment on the field configuration data to generate a trigger centralized distribution, measure the field activation decrease rate of the three stages of input, judgment and archiving from the field configuration data to generate a stage activation difference coefficient, and perform field dependency unidirectional migration to build a node reorganization strategy based on the trigger centralized distribution and the stage activation difference coefficient. The path decision module is used to perform a pre-scan to generate an overload hazard rating based on the node linkage deviation and the node reorganization strategy, perform path adaptation to generate a path adaptation scheme based on the overload hazard rating, and implement hierarchical collaborative drive to establish an execution constraint set based on the path adaptation scheme. The load control module is used to coordinate the execution weight and trigger threshold of the node reorganization strategy according to the execution constraint set to generate a parameter change log, verify the overlap of the approver and submitter identities based on the parameter change log to generate an operational anomaly interference sequence, and identify the first rejection node through the operational anomaly interference sequence to generate a circulation health score. The topology generation module is used to generate orchestration structure parameters by inferring the missing positions and filling conditions of the rejection dense period based on the flow health score, and to execute path hierarchical allocation generation form and workflow node linkage orchestration instructions according to the orchestration structure parameters.

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