Enterprise finance and tax integration method and system

Abstract

This invention discloses an integrated method and system for enterprise finance and taxation, relating to the technical field of integrated finance and taxation systems. It involves averaging baseline response surface data over time to obtain the ideal steady-state baseline performance value. By separating low-frequency trend terms to extract parameter disturbance sets and routing anomaly sets, it identifies the dominant influence dimensions at different stages. Scene segmentation is performed according to the scale and type of business units to form a multi-dimensional dominant graph. Using the Taylor deconvolution algorithm, the dominant parameter disturbances in the multi-dimensional dominant graph are decomposed into physical drivers, and their respective contribution ratios are quantified. Root cause localization of routing anomalies is performed, and coordinated optimization is implemented based on the drivers and root causes. This integrated method can adaptively identify key issues, implement precise measures, and continuously optimize, ultimately achieving a significant improvement in cross-system collaborative efficiency and systematic control of operational risks.

Description

Technical Field

[0001] This invention relates to the field of financial and tax integration system technology, specifically to an integrated method and system for enterprise financial and taxation. Background Technology

[0002] Against the backdrop of increasingly integrated corporate finance and taxation operations and continuously increasing complexity of cross-system collaboration, the financial and taxation process is often affected by a variety of business-driven factors (such as policy updates, frequent audits, and system upgrades) and internal and external interference sources (such as resource fluctuations, routing mismatches, and rule conflicts). The end-to-end performance of different business units varies significantly under parameter configurations and routing rule combinations and is easily affected by short-term disturbances.

[0003] Traditional methods cannot effectively separate low-frequency trends from high-frequency disturbances to identify the dominant influencing dimensions. They lack mechanistic analysis of the physical drivers behind parameter disturbances and accurate root cause localization of routing anomalies. This leads to problems such as extended processing time, increased anomaly rollback rates, and a surge in dwell time during critical periods such as policy changes or system upgrades. Therefore, there is an urgent need for an integrated method that can combine multi-source data modeling, disturbance separation, driver decomposition, and coordinated optimization to achieve efficient and stable operation and continuous optimization of the integrated financial and tax system in a changing environment. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated method and system for enterprise finance and taxation to address the shortcomings in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an integrated method for enterprise finance and taxation, the method comprising the following steps: S1: Combining different business drive types and internal and external interference sources, extract key parameters from multi-source data to form a combined dataset. Relying on the financial and tax integration efficiency diagnosis model, calculate the end-to-end indicators of each business unit under different combinations of parameters and rules, and establish a baseline response surface. S2: Average the baseline response surface data over time to obtain the ideal steady-state baseline performance value. Extract the parameter disturbance set and routing anomaly set by separating the low-frequency trend term, identify the dominant influence dimension at different stages, and segment the scene according to the scale and type of business unit to form a multi-dimensional dominant map. S3: Using the Taylor deconvolution algorithm, the perturbation of the dominant parameter in the multidimensional dominant graph is decomposed into physical causes, and the contribution ratio of each is quantified. The root cause of routing anomalies is located, and the linkage optimization is implemented based on the causes and root causes.

[0006] Preferably, the baseline response surface data is averaged over time to obtain the ideal steady-state baseline performance value, including the following steps: Based on the characteristics of the business cycle, an averaging window is set. For a certain set of fixed parameter values ​​and routing rules, the performance index observations of all business units within the averaging window are collected. Robust statistical methods are used within the window to identify outliers that deviate from the majority value. The arithmetic mean of the remaining valid samples is calculated for each indicator, forming the steady-state performance vector of the averaged window. The steady-state efficiency vectors for all time periods are averaged again to obtain the ideal steady-state baseline efficiency value.

[0007] Preferably, by separating the low-frequency trend term to extract the parameter disturbance set and the routing anomaly set, the dominant influencing dimensions at different stages are identified, including the following steps: The measured values ​​of the performance indicators of each business unit within a continuous observation period are arranged into a time series at a fixed sampling frequency, and the corresponding ideal steady-state baseline value is subtracted to obtain the deviation series; The deviation sequence is filtered to remove low-frequency components below the cutoff frequency, and the high-frequency part is retained as the disturbance signal. The source of the filtered disturbance signal is traced and matched with the time point of parameter change or routing rule trigger to obtain the parameter disturbance set. After obtaining the parameter perturbation set, determine the relative impact weight of the parameter perturbation on performance fluctuations; Statistical analysis of routing rule triggering rates and their impact on performance; identification of dominant influencing dimensions at different stages. Data is segmented according to business stages, the number of times routing rules are triggered and the total number of requests are counted, and the trigger rate is calculated. The performance indicators before and after each rule trigger are differentially calculated, and the average impact is calculated by grouping according to the rule. By combining the trigger rate with the average direction / magnitude of influence, the dominant dimension at a certain stage can be determined.

[0008] Preferably, scenarios are segmented according to the scale and type of business units to form a multi-dimensional dominant map, including the following steps: Business units are divided into two dimensions based on scale and type, forming multiple sub-scenarios. The aforementioned contribution and dominant dimension analysis is repeated in each sub-scenarios, and the differences are compared to obtain the perturbation weight distribution and dominant dimension of the sub-scenarios. By summarizing the patterns of disturbance sensitivity and dominant dimensions of different scale-type combinations, we can provide a basis for differentiated operation and maintenance and form a multi-dimensional dominant map.

[0009] Preferably, after obtaining the parameter perturbation set, the relative impact weight of the parameter perturbation on performance fluctuation is determined, including the following steps: Construct a perturbation impact matrix, where rows represent observed perturbation events and columns represent the perturbation intensity of parameters, and include the performance fluctuation amplitude as the target variable; Stepwise regression is used to find the contribution of each item. A separate regression model is built for each type of parameter to predict the performance fluctuation and record the coefficient of determination. The parameters are then included in the regression model and the overall coefficient of determination is recorded. The explained variance of each parameter model is proportionally allocated to the total explained variance to obtain its relative weight in the total fluctuation. The parameters are then sorted from high to low weight to identify the dominant parameter categories. If the weight of a rule class is higher than that of other classes in a certain stage, it indicates that the instability of the routing logic in that stage is the main cause of the performance bottleneck.

[0010] Preferably, the filtered disturbance signal is traced back to its source, matched with the time point of parameter change or routing rule triggering, to obtain the parameter disturbance set, including the following steps: If a disturbance occurs synchronously with a change in a key parameter, it is included in the parameter disturbance set. If the disturbances are concentrated in the activation of routing rules and accompanied by abnormal performance offsets, they are classified into the routing anomaly set. The disturbance event record is formed by comprehensively measuring the amplitude and duration of the disturbance signal.

[0011] Preferably, the multidimensional dominant graph structure dimensions include a stage axis, a parameter perturbation axis, a scene axis, and a routing perturbation axis.

[0012] Preferably, the Taylor deconvolution algorithm is used to decompose the perturbations of the dominant parameters in the multidimensional dominant spectrum into physical drivers and quantify their respective contribution ratios, including the following steps: The contribution ratio of parameter perturbation to performance fluctuation is obtained by relative weight analysis. The composite perturbation effect is regarded as the superposition of responses of several basic driving forces. The independent action of a single driving force is separated by inverse analysis and its contribution ratio is quantified. For each driver, a corresponding performance indicator change characteristic template is established. The parameter disturbance set is mapped to the basis function library according to the time section. The pattern matching algorithm is used to determine the main driver of each disturbance event. The composite disturbance curve is restored by weighted superposition, and the disturbance component of the single driver is separated. For the separated dynamic components, calculate their proportion in the total disturbance energy to obtain a quantitative contribution ratio.

[0013] Preferably, the root cause of routing anomalies is located, and coordinated optimization is implemented based on the cause and root cause, including the following steps: Check the matching logic of the routing mapping table to determine if there are any missing timestamp verifications; By analyzing the sequence of task ID and node reception time through log backtracking, we can verify whether the same ID enters the same node multiple times. For different physical drivers and routing root causes, generate coordinated optimization measures, including optimization of rule adaptation deviations, optimization of insufficient resource supply, optimization of process connection problems, and optimization of routing layer.

[0014] An integrated enterprise financial and tax system includes: Surface creation module: Combining different business drive types and internal and external interference sources, key parameters are extracted from multi-source data to form a combined dataset. Based on the financial and tax integration efficiency diagnosis model, end-to-end indicators of each business unit under different combinations of parameters and rules are calculated, and a baseline response surface is established. Image generation module: Averages the baseline response surface data over time to obtain the ideal steady-state baseline performance value. Extracts parameter disturbance set and routing anomaly set by separating low-frequency trend terms, identifies the dominant influence dimension at different stages, and segments the scene according to the scale and type of business unit to form a multi-dimensional dominant map. Tuning Module: Using the Taylor deconvolution algorithm, the perturbation of the dominant parameters in the multidimensional dominant graph is decomposed into physical causes, and the contribution ratio of each is quantified. The root cause of routing anomalies is located, and linkage tuning is implemented based on the causes and root causes.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This application forms a multi-dimensional dominant map by performing time-domain steady-state processing and disturbance separation, combined with stage identification and scene segmentation, realizing a panoramic view from macro-operational status to micro-disturbance structure, enabling managers to intuitively grasp the performance bottlenecks and dominant influence dimensions of business units of different periods, scales and types. This application introduces Taylor deconvolution-based motivation decomposition and routing anomaly root cause localization, transforming statistical disturbances into three physical drivers: operable process connections, rule adaptation, and resource supply. It then implements coordinated optimizations such as rule hot updates, IT expansion, interface reconstruction, and route matching enhancement for specific root causes, thereby eliminating performance fluctuations and routing risk sources at the mechanistic level. This significantly improves end-to-end processing time, reduces anomaly rollback rate and latency, while also improving route matching accuracy and system anti-interference capability. This application constructs a baseline response surface that covers multiple business-driven and interference scenarios, which can accurately characterize the end-to-end performance under different combinations of parameters and routing rules in complex financial and tax business environments, providing a reliable reference benchmark for subsequent analysis. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0017] Figure 1 This is a flowchart of the integration method of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: This example provides an integrated method for enterprise finance and taxation. Please refer to [link / reference]. Figure 1 As shown, the integration method includes the following steps: S1: Combining different business drivers such as high-frequency trading, periodic reports, and sudden audits, as well as internal and external interference sources such as ERP iteration, tax policy updates, and system upgrades, we divide the data into stages such as the normal stable period, policy switching period, system upgrade period, and high-incidence period of audits. From multi-source data such as business system logs, financial ledgers, tax declaration records, and bank-enterprise transaction records, we extract key parameters of process, rule, and resource categories. These parameters are then combined with four layers of routing rules, including default full-range distribution, targeted broadcasting, handling of unknown attribution, and loop suppression, to form a combined dataset. Based on the financial and tax integration efficiency diagnosis model, we calculate the end-to-end processing time, data consistency accuracy, anomaly rollback rate, and retention time of each business unit under different combinations of parameters and rules, and establish a baseline response surface.

[0020] S2: The parameter and rule data of the baseline response surface are averaged over time to obtain the ideal steady-state baseline performance value. Then, a high-pass filter is used to separate low-frequency trend terms, extracting the parameter disturbance set and routing anomaly set. Relative weight analysis is used to quantify the contribution of process, rule, and resource parameter disturbances to performance fluctuations, and the trigger rate of each routing rule at different stages and its impact on performance are statistically analyzed to identify the dominant influence dimensions at different stages. Scenarios are segmented according to the scale and type of business units, and the differences in dominant components under different sub-scenarios are analyzed to form a multi-dimensional dominant spectrum covering stages, scenarios, parameters, and routing disturbances.

[0021] S3: Using the Taylor deconvolution algorithm, the dominant parameter disturbances are decomposed into three main physical drivers: poor process integration, rule adaptation deviation, and insufficient resource supply, and their respective contribution ratios are quantified. Root cause analysis is performed on routing anomalies; for example, loop suppression failure may stem from missing timestamp verification in the routing mapping table matching logic, and overload in handling unknown attributions may be due to insufficient accuracy of the pre-classification model. Based on the drivers and root causes, coordinated optimization is implemented. To address rule adaptation deviations, hot updates and canary-scale verifications of the rule engine are triggered. For resource shortages, IT capacity expansion or task scheduling optimization is implemented. For process inconsistencies, interface protocols are reconstructed or buffer nodes are added. At the routing level, matching conditions are strengthened, filtering accuracy is improved, or priorities are adjusted to eliminate anomalies and improve processing efficiency. After optimization, end-to-end timeliness, anomaly rollback rate, latency, and routing quality indicators are recalculated. Performance deviation extremes and routing risk extremes are calculated and compared with preset thresholds. If the risk control and efficiency improvement requirements are met, the solution is deemed satisfactory; otherwise, it returns to S2 for re-analysis of the dominant components and iterative optimization.

[0022] This embodiment provides an integrated enterprise financial and tax system, including: Surface creation module: Combining different business drive types and internal and external interference sources, key parameters are extracted from multi-source data to form a combined dataset. Based on the financial and tax integration efficiency diagnosis model, end-to-end indicators of each business unit under different combinations of parameters and rules are calculated, and a baseline response surface is established. Image generation module: Averages the baseline response surface data over time to obtain the ideal steady-state baseline performance value. Extracts parameter disturbance set and routing anomaly set by separating low-frequency trend terms, identifies the dominant influence dimension at different stages, and segments the scene according to the scale and type of business unit to form a multi-dimensional dominant map. Tuning Module: Using the Taylor deconvolution algorithm, the perturbation of the dominant parameters in the multidimensional dominant graph is decomposed into physical causes, and the contribution ratio of each is quantified. The root cause of routing anomalies is located, and linkage tuning is implemented based on the causes and root causes.

[0023] This embodiment provides a detailed description of each step of the integration method of this application, as follows: To achieve baseline modeling of financial and tax integration efficiency for scenarios driven by multiple businesses and subject to multiple sources of interference, S1 needs to follow a systematic and hierarchical analysis process. This process organically links business cycle division, multi-source heterogeneous data fusion, key parameter extraction, routing rule mapping, efficiency indicator calculation, and baseline surface construction, forming a reproducible and traceable professional process.

[0024] Specifically, based on the actual scenarios of corporate financial and tax operations, the core factors driving business processes are systematically classified, and the characteristics of three types of business drivers are clarified: high-frequency transactions (such as daily issuance of massive amounts of invoices, payment and receipt verification, etc., which are highly repetitive and frequent operations), periodic reports (such as monthly closing reports, quarterly tax return preparation, etc., which are tasks triggered by fixed time periods), and sudden audits (such as temporary accounting audits and data submissions caused by regulatory spot checks and risk warnings). High-frequency transactions focus on real-time performance and concurrent processing capabilities, periodic reports emphasize the timeliness of cross-system data integration, and sudden audits focus on the ability to respond quickly and trace abnormal events.

[0025] The internal and external sources of interference were identified. Internal sources included ERP system function iterations (such as interface changes and data mapping rule adjustments due to module reconstruction) and internal system upgrades (such as updates to the financial accounting engine). External sources included tax policy updates (such as tax rate adjustments, changes in the structure of tax returns, and the activation / deactivation of preferential policies) and changes in the interface specifications of third-party platforms (such as adjustments to the format of bank statement push notifications).

[0026] By using event correlation analysis, the above-mentioned driving types are cross-mapped with the frequency, scope, and duration of interference sources, thus dividing the business operation into four stages: The period is divided into several phases: a stable period (no major tax policy updates, no core system upgrades, no sudden audits, and high-frequency transactions and periodic reports proceed according to the established schedule), a policy transition period (1-2 reporting cycles before and after the implementation of tax policy updates, involving adjustments to reporting rules and tax calculation logic), a system upgrade period (the window period for ERP iteration or internal system upgrades, usually accompanied by interface interruptions, data migration, and other operations), and a period of high incidence of audits (from the start of a special regulatory inspection to the completion of the rectification process, during which the proportion of sudden audit tasks increases significantly).

[0027] The process of dividing the system into stages requires combining historical data to determine the trigger thresholds for each stage. For example, the policy transition period is defined as the period from the issuance of the tax policy document to the deadline for filing the first application of the policy, and the system upgrade period is defined as the period from the announcement of system shutdown and maintenance to the date when the core functions are restored and verified.

[0028] For the defined business phases, key parameters are extracted from four types of data sources: business system logs (such as ERP operation logs, invoice management system interaction logs, and bank-enterprise direct connection transaction logs), financial ledgers (general ledger details, accounts receivable / payable reconciliation records, and expense collection ledgers), tax declaration records (submission logs of various tax declaration forms, tax payment vouchers, and records of rejected and resubmitted declarations), and bank-enterprise transaction records (bank account income and expenditure details, payment instruction status, and reconciliation discrepancy records). This ensures coverage of influencing factors throughout the entire business execution process, including process parameters, rule parameters, and resource parameters. Process parameters focus on the execution path and timing characteristics of business processes, including the number of end-to-end processing nodes for a single high-frequency transaction (such as the number of nodes involved in the process of invoice entry, review, upload, and selection from issuance to certification), the length of the task dependency chain for periodic report preparation (such as the number of dependent steps required for monthly reports to complete data collection, verification, merging, review, and reporting in sequence), and the time consumed for cross-departmental collaboration in sudden audit tasks (such as the average turnaround time from the issuance of the audit notice to the completion of document preparation by the finance department).

[0029] Rule-related parameters revolve around the constraints and judgment logic of business execution, including the accuracy of tax policy rule matching (such as the degree of conformity of a field value in the declaration form with the latest tax policy clauses, which can be measured by the proportion of successful matching times by the rule engine to the total number of verification times), the trigger threshold of built-in system verification rules (such as the rule threshold for forcibly triggering level 3 review when the invoice amount exceeds X million yuan), and the priority configuration of routing rules (such as the execution order of targeted broadcast rules before unknown attribution handling rules).

[0030] Resource parameters focus on the status of hardware and software resources that support business operations, including the CPU / memory utilization of core servers during peak business periods (such as the average CPU load of ERP application servers during high-frequency trading periods), network bandwidth utilization (such as the peak bandwidth usage ratio during bank-enterprise transaction synchronization), and the average number of staff on duty at manual processing nodes (such as the average daily number of staff on duty at the financial review position during the periodic report preparation period).

[0031] The extracted key parameters are structurally combined with the preset four-layer routing rules to form a parameter-rule combination dataset covering different business scenarios: When a business request lacks a clear attribution identifier (e.g., a new type of invoice cannot be matched with an existing accounting entity), the data will be synchronously pushed to all relevant business units (e.g., all accounting accountants and tax specialists) for processing. For batch transactions with a known ownership entity (such as monthly expense reimbursement for a regional branch), data is only pushed to the responsible positions corresponding to that entity (such as the branch's finance manager or the headquarters' expense accountant). For transactions whose attribution cannot be determined after initial matching (such as invoices with unclear headers in cross-entity transactions), a pre-set fallback process is triggered (such as first pushing the invoice to the shared service center for initial review, and then redistributing it based on the results of the initial review). To prevent processing loops caused by duplicate data pushes (e.g., after position A returns a task to position B, position B automatically sends it back to position A), the task flow path is marked and the number of times the same node is repeatedly received is limited.

[0032] Based on a pre-built integrated financial and tax efficiency diagnostic model (which is trained on historical business data and expert experience and has multivariate coupling analysis capabilities), the efficiency performance of each business unit under different parameter-rule combinations is quantitatively measured. The measurement logic is as follows: Extract the initiation timestamp and completion timestamp of the request from the business system logs and calculate the difference between them. If there are parallel nodes in the process (such as invoice review and invoice verification being carried out simultaneously), the time of the last node completed among all parallel nodes shall be used as the standard. Group the timeliness data under different parameter-rule combinations by business unit, remove extreme values ​​(such as timeout records caused by system crashes), and take the average value as the timeliness benchmark for that combination.

[0033] Define consistency judgment rules (allowing a difference of ±0.01 yuan for source data, and requiring cross-system logical consistency to satisfy that the revenue in the declaration form = the revenue in the financial statement ± the adjustment items); extract corresponding data items from the financial ledger, bank-enterprise transaction records, and tax declaration records, compare them one by one and execute the judgment rules, and calculate the proportion of consistent records to the total number of compared records, which is the accuracy of the combination.

[0034] In the business system logs, filter records with operation type = rollback, extract their associated original business request ID and rollback reason (such as rule validation failure, insufficient resources); count the total number of business requests initiated under this parameter-rule combination, and calculate the percentage of rollback records; it is necessary to distinguish between active rollback (such as actively canceling after manually discovering an error) and passive rollback (such as the system automatically detecting a conflict and triggering a rollback), and assign different weights to them in the model (such as passive rollback having a higher weight because it reflects the inherent defects of the rules or resources).

[0035] Extract the arrival timestamp and start processing timestamp of each node from the business system logs, and calculate the difference as the dwell time of that node; sum the dwell times of all nodes for the same request to obtain the total dwell time of a single request; group by parameter-rule combination and business unit, calculate the average dwell time, and focus on high dwell scenarios caused by unreasonable routing rules (such as default full-range distribution leading to multiple positions receiving tasks simultaneously and causing task backlog) or insufficient resources (such as manpower shortage leading to the dwell of review nodes).

[0036] Specifically, based on various performance indicator data, with key parameters as independent variables (such as the number of processing nodes for process types, the accuracy of tax policy rule matching for rule types, and the server CPU utilization rate for resource types), business stages and routing rules as moderating variables, and end-to-end processing time, data consistency accuracy, anomaly rollback rate, and dwell time as dependent variables, a baseline response surface is constructed.

[0037] Continuous parameters (such as CPU utilization) are discretized into intervals (such as 0-30%, 30%-60%, 60%-100%), and discrete parameters (such as routing rule type) are used as categorical variables. Then, for each business stage (such as the normal stable period), scatter plots are drawn with the parameter intervals as the coordinate axes under each routing rule (such as the horizontal axis representing the tax policy rule matching accuracy interval and the vertical axis representing the data consistency accuracy). The blank areas between the scatter points are filled by interpolation algorithms to generate a continuous response surface, which intuitively shows the fluctuation trend of performance indicators when the parameter values ​​change.

[0038] During the establishment process, the prediction accuracy of the surface is evaluated by cross-validation (e.g., reserving 20% ​​of historical data as a test set) to ensure that it has a reliable estimation ability for the performance of unobserved parameter combinations. The resulting baseline response surface can be used as a reference benchmark for subsequent business optimization.

[0039] In this specific implementation, the business phase is taken as a normal stable period, and the routing rule is rule A. The key parameters selected are the tax policy rule matching accuracy and the server CPU utilization rate as independent variables. The tax policy rule matching accuracy is divided into three intervals according to the continuous parameter: the low accuracy interval corresponds to a matching accuracy of 70% to 80%, the medium accuracy interval is 80% to 90%, and the high accuracy interval is 90% to 100%. The server CPU utilization rate is divided into three discrete intervals: 0% to 30%, 30% to 60%, and 60% to 100%.

[0040] Under this business phase and routing rules, the measured end-to-end processing time and data consistency accuracy are as follows: When the matching accuracy of tax rules is between 70% and 80% and the CPU utilization is between 0% and 30%, the data consistency accuracy is 92%, and the end-to-end processing time is 4.2 seconds. When the matching accuracy is between 80% and 90% and the CPU utilization is between 30% and 60%, the accuracy is 96%, and the processing time is 3.5 seconds. When the matching accuracy is between 90% and 100% and the CPU utilization is between 60% and 100%, the accuracy is 98%, and the processing time is 2.9 seconds. The measured values ​​of the remaining combination points are plotted on a two-dimensional coordinate scatter plot with the matching accuracy range on the horizontal axis and the CPU utilization rate on the vertical axis. Linear interpolation algorithms are used to fill in the numerical values ​​of the interval combinations that are not directly measured, forming a continuously changing response surface. This surface shows a smooth trend: as the matching accuracy and CPU utilization increase, the data consistency accuracy gradually increases from 92% to 98%, and the end-to-end processing time decreases from 4.2 seconds to 2.9 seconds. By reserving 20% ​​of historical data as a test set, the predicted values ​​of the surface were compared with the measured values. The mean absolute error was less than 1.2%, indicating that the surface has reliable predictive capabilities. Under normal stationary conditions and Rule A, this baseline response surface can clearly demonstrate the quantitative impact of different parameter configurations on performance indicators, providing a directly referable benchmark for reducing end-to-end processing time and improving data consistency.

[0041] S2 extracts an ideal steady-state performance reference from parameter + rule operation data containing time-varying noise and occasional disturbances. Based on this, it separates the parameter disturbances and routing anomalies that truly drive performance fluctuations, further quantifies the contribution weight of different types of parameter disturbances, identifies the dominant influence dimensions at each stage, and combines business unit attributes to make scenario-based subdivisions, ultimately forming a multi-dimensional dominant map.

[0042] In S1, the baseline response surface is a set of performance indicators measured by combining multidimensional parameters and routing rules. These indicators may fluctuate in the short term (such as intraday peaks and valleys, instantaneous performance degradation caused by concentrated weekly reporting) or be subject to occasional interference (such as temporary system lag, sudden delays in manual review) during actual collection.

[0043] Specifically, to obtain the ideal steady-state baseline performance value unaffected by short-term disturbances, the surface data must first be averaged over time: Based on the characteristics of the business cycle, an averaging window is set. For example, the normal stable period can be divided into calendar days, the policy transition period can be shortened to a half-day window due to concentrated applications, and the system upgrade period can be captured by hourly windows to capture short-term fluctuations. The window length should be longer than the execution cycle of a single business process to avoid distortion caused by cross-process mixing. For a fixed set of parameter values ​​and routing rules, collect the performance index observations (end-to-end processing time, data consistency accuracy, anomaly rollback rate, and latency) of all business units within the window. Within the window, use robust statistical methods (such as the median absolute deviation method) to identify outliers that significantly deviate from the majority values. These outliers are often caused by non-steady-state events (such as hardware transient failures or emergency manual intervention) and are not included in steady-state calculations. Calculate the arithmetic mean of the remaining valid samples according to the indicators to form the steady-state performance vector of the window.

[0044] The steady-state efficiency vectors for all time periods are averaged again (weighted by time period length, with longer windows assigned higher weights) to obtain the ideal steady-state baseline efficiency value under this parameter-rule combination. This value represents the theoretically achievable efficiency level after excluding short-term disturbances and serves as a reference benchmark for subsequent disturbance analysis.

[0045] Actual operating data, relative to the ideal steady-state baseline, will be superimposed with low-frequency trends (such as gradual performance changes due to long-term resource aging) and high-frequency disturbances (such as sudden parameter offsets or routing mismatches). To separate these two types of components, a high-pass filtering technique must be applied to retain only components above a set cutoff frequency as disturbance signals. The measured performance indicators of each business unit within a continuous observation period are arranged into a time series at a fixed sampling frequency (e.g., once per hour), and the corresponding ideal steady-state baseline value is subtracted to obtain the deviation series. The boundary between low and high frequencies is determined based on the characteristics of business fluctuations; for example, changes with a period longer than 3 days are considered low-frequency trends, and fluctuations shorter than 3 days are considered high-frequency disturbances. The cutoff frequency corresponds to the reciprocal of the period. IIR or FIR filters can be used to filter the deviation series, removing low-frequency components below the cutoff frequency, retaining the high-frequency portion, which is the disturbance signal. The filtered disturbance signal is then traced back to its source, matching it with the time points triggered by parameter changes or routing rules. If a disturbance occurs in sync with a change in a critical parameter (such as a surge in CPU utilization or a sharp drop in the accuracy of tax policy matching), it is classified into the parameter disturbance set; if the disturbance is concentrated on the activation of a specific routing rule and is accompanied by abnormal performance offset (such as task flooding caused by default full-range distribution), it is classified into the routing anomaly set.

[0046] By combining the amplitude of the disturbance signal (i.e. the absolute value of the performance index deviating from the steady state) and the duration, a weighted record of disturbance events is formed, which facilitates subsequent contribution analysis.

[0047] In a specific implementation, for the normal stable period, the selected parameter combination is: tax policy rule matching accuracy range of 90% to 100%, server CPU utilization range of 60% to 100%, and routing rule A. The average window is based on natural days, with a window length of 24 hours, which is greater than the execution cycle of a single business process by 10 minutes, to avoid cross-process mixing distortion. Performance observations of 100 business units within this window on a certain day are collected. The average end-to-end processing time is 3.0 seconds, the average data consistency accuracy is 97.8%, the average anomaly rollback rate is 0.6%, and the average dwell time is 12 seconds. After removing 3 outliers using the median absolute deviation method, the arithmetic mean of the remaining 97 samples is recalculated, resulting in a steady-state performance vector of 3.02 seconds, 97.9% accuracy, 0.58% rollback rate, and 11.9 seconds dwell time.

[0048] The steady-state vectors of the same window type over 7 consecutive days are weighted by window length (daily weight 1) and then averaged to obtain the ideal steady-state baseline performance value of 3.01 seconds, accuracy 97.85%, rollback rate 0.59%, and dwell time 12.0 seconds. The end-to-end processing time measured values ​​sampled hourly over 7 consecutive days are used to construct a time series. 3.01 seconds are subtracted hourly to obtain the deviation series. The low-frequency boundary is set as the cutoff frequency corresponding to a period of 3 days, f_c = 1 / (3×24) = 0.01389Hz. An FIR high-pass filter is used to filter out components below f_c, retaining high-frequency disturbance signals. A disturbance signal amplitude of |3.45 - 3.01| = 0.44 seconds was measured at a certain time, lasting for 2 hours. If the CPU utilization rate increased from 60% to 95% during the same period and the change time coincided with this, it was included in the parameter disturbance set. The disturbance event weight W_p = amplitude × duration = 0.44 × 2 = 0.88 seconds·hour, and was recorded as a parameter disturbance event. This process achieves the extraction of the steady-state baseline and the separation and quantification of the disturbance signal, providing a calculable numerical basis for subsequent disturbance contribution analysis.

[0049] Specifically, after obtaining the parameter perturbation set, it is necessary to clarify the relative impact weights of the three types of parameter perturbations—process-related, rule-related, and resource-related—on performance fluctuations. Relative weighting analysis is used here, its advantage being that it can reasonably allocate the explained variance even when multicollinearity exists. Construct a disturbance impact matrix, where rows represent observed disturbance events and columns represent the disturbance intensity of three types of parameters (which can be quantified using standardized deviation amplitude), and add the efficiency fluctuation amplitude as the target variable.

[0050] Stepwise regression is used to determine the individual contribution of each parameter. A separate regression model is built for each type of parameter to predict performance fluctuations and record the coefficient of determination (the proportion of explained variance). All three types of parameters are then included in the regression model, and the overall coefficient of determination is recorded.

[0051] The explained variance of each parameter model is proportionally allocated to the overall explained variance to obtain its relative weight in the total fluctuation. The parameters are then sorted from highest to lowest weight to identify the dominant parameter categories. If the weight of the rule-based category is significantly higher than other categories in a certain stage, it indicates that the instability of the routing logic in that stage is the main cause of the performance bottleneck.

[0052] In a specific implementation, after obtaining the parameter disturbance set, for routing rule A during the normal stable period, observation data of 10 disturbance events are collected to construct a disturbance impact matrix. The rows correspond to event numbers 1 to 10, and the columns are the disturbance intensity P_f of process parameters, the disturbance intensity P_r of rule parameters, and the disturbance intensity P_s of resource parameters. All of these are expressed as standardized deviation magnitudes, and the target variable is the performance fluctuation magnitude ΔE (the absolute value of the end-to-end processing time deviation from the ideal steady-state baseline).

[0053] Stepwise regression is used to calculate the contribution of each component. First, P_f is used to model and predict ΔE separately, and then the coefficient of determination R_f is obtained. 2 =0.42; R_r is obtained by modeling P_r alone. 2 =0.51; R_s is obtained by modeling P_s alone. 2 =0.27; then, P_f, P_r, and P_s are all included in the multiple linear regression model to obtain the overall coefficient of determination R_all. 2 =0.73.

[0054] The formula for calculating relative weights is w_i=(R_i) 2 / ∑R_j 2 )×R_all 2 Where j∈{f,r,s}. Substituting the values, ∑R_j 2 =0.42+0.51+0.27=1.20, resulting in the following weights: process class weight w_f=(0.42 / 1.20)×0.73=0.2555, rule class weight w_r=(0.51 / 1.20)×0.73=0.3103, and resource class weight w_s=(0.27 / 1.20)×0.73=0.1643. Sorted by weight, the rule class has the highest weight, indicating that the instability of tax policy or routing logic at this stage is the dominant factor in performance fluctuations, thus identifying the main direction for subsequent optimization.

[0055] Specifically, we need to statistically analyze the trigger rate of routing rules and their impact on performance, identify the dominant influencing dimensions at different stages, and, based on the known Layer 4 routing rules in S1, analyze their trigger rate (the proportion of the number of times a rule is executed to the total number of business requests in that stage) and the average impact on performance metrics after triggering (compared to the performance benchmark when the rule is not triggered).

[0056] Data is segmented by business phase, and the number of triggers and total requests for four types of routing rules are statistically analyzed during the normal stable period, policy transition period, system upgrade period, and high-incidence period of audits, and the trigger rate is calculated. The performance indicators before and after each rule trigger are differentially calculated (the observed value after trigger minus the value before trigger or the baseline value), and the average impact is calculated by grouping by rule, paying attention to distinguishing between positive impacts (improved accuracy, reduced latency) and negative impacts (increased rollback rate, extended timeliness).

[0057] By combining the trigger rate and the average direction / magnitude of the impact, the dominant dimension of a certain stage can be determined. If the trigger rate of handling unknown attribution is high and the dwell time is significantly increased in a certain stage, then the dominant dimension of that stage is the adaptability of routing rules. If the contribution of resource-related parameter disturbances is high, then the dominant dimension is the stability of resource supply.

[0058] Specifically, business units can be divided into two dimensions: size (e.g., large group subsidiaries, medium-sized regional companies, small offices) and type (e.g., production-oriented, sales-oriented, R&D-oriented), forming multiple sub-scenarios. Within each sub-scenarios, the aforementioned contribution and dominant dimension analysis is repeated, and the differences are compared. Scale can be categorized by revenue, number of employees, or transaction volume, and type is classified according to the main business function. Steps three and four of the analysis process are executed independently for each sub-scenario to obtain the distribution of process / rule / resource disturbance weights and dominant dimensions for that scenario. The patterns of disturbance sensitivity and dominant dimensions in different scale-type combinations are summarized to provide a basis for differentiated operation and maintenance. A multi-dimensional dominant graph covering stage, scenario, parameter, and routing disturbances is formed.

[0059] The above analysis results are integrated into a multidimensional dominance map, whose structural dimensions include: Phase Axis: Regular stable period, policy transition period, system upgrade period, and period of high incidence of audits; Scene axis: Sub-scenes that combine scale and type; Parameter perturbation axis: weight distribution of process, rule, and resource categories; Routing disturbance axis: the direction of the trigger rate and performance impact of each routing rule.

[0060] In a specific implementation, based on the known Layer 4 routing rules of S1, the trigger rate and performance impact of each service stage are statistically analyzed. During a typical stable period, the total number of service requests is 5000, and the trigger counts for the four types of routing rules are as follows: 2600 cases were prioritized for local processing, 1200 were distributed by business type, 800 were high-priority expedited cases, and 400 were cases with unknown attribution. The trigger rates were: 52.0% for priority local processing, 24.0% for distribution by business type, 16.0% for high-priority expedited cases, and 8.0% for cases with unknown attribution. Performance impact was assessed using end-to-end processing time and latency as observation indicators. Using the baseline processing time of 2.9 seconds and latency of 11 seconds without any rules triggered as a reference, the average difference after triggering was calculated. After triggering cases with unknown attribution, the processing time increased to 3.6 seconds and the latency increased to 15 seconds, with a difference in processing time Δt = 0.7 seconds and latency Δd = 4 seconds, both of which were negative impacts.

[0061] Combining trigger rate and impact magnitude, during the normal stable period, the trigger rate is low and the impact is limited due to the handling of unknown attribution. The contribution of resource-related disturbances in the aforementioned analysis is 0.1643, process-related disturbances are 0.2555, and rule-related disturbances are 0.3103. Therefore, the dominant dimension is the rule-related disturbance. Further segmentation by business unit size and type: large group subsidiaries that are production-oriented are categorized as scenario G1, medium-sized regional companies that are sales-oriented as G2, and small offices that are R&D-oriented as G3. Repeated contribution analysis is performed in G1, G2, and G3 respectively. In G1, the weights are rule-related disturbances 0.38, process-related disturbances 0.29, and resource-related disturbances 0.33; in G2, rule-related disturbances 0.25, process-related disturbances 0.40, and resource-related disturbances 0.35; and in G3, rule-related disturbances 0.22, process-related disturbances 0.28, and resource-related disturbances 0.50. This shows that G3 is most sensitive to the stability of resource supply, and the dominant dimension is the resource-related disturbance.

[0062] The system integrates to form a multi-dimensional dominant graph. The stage axis includes the normal stable period, policy transition period, system upgrade period, and high-incidence period of inspections. The scenario axis is a combination of sub-scenarios of scale and type. The parameter disturbance axis records the distribution of three types of weights. The routing disturbance axis records the trigger rate and performance impact direction of each rule. In this way, the dominant disturbance dimension and rule adaptability issues can be quickly located in specific scenarios at different stages, providing a quantitative basis for the formulation of differentiated operation and maintenance strategies.

[0063] S3 borrows the idea of ​​Taylor deconvolution in signal processing to separate the source components of composite waveforms. It decomposes the statistically significant dominant parameter disturbance into three physical drivers that correspond to the actual business mechanism: poor process connection, rule adaptation deviation, and insufficient resource supply. It also accurately locates the root cause of routing anomalies.

[0064] Specifically, relative weight analysis yielded the contribution ratios of process-related, rule-related, and resource-related parameter perturbations to performance fluctuations. However, these categories remain statistically aggregated concepts and require further mapping to executable physical drivers. Drawing on the processing logic of Taylor deconvolution, the composite perturbation effect is viewed as a superposition of responses from several fundamental drivers. Through inverse analysis, the independent action of each individual driver is separated, and its contribution ratio is quantified. Based on historical cases and expert knowledge, response patterns for three major physical drivers are defined: poor process integration manifests as long waiting times between nodes, high frequency of data handover, and redundancy in serial links, which often occurs when interface protocols are inconsistent or buffer nodes are lacking; rule adaptation deviation manifests as rule matching errors, conflicts between old and new rules, and delayed hot updates, which often occur when tax policies are updated or routing logic changes are made; and insufficient resource supply manifests as CPU / memory / bandwidth saturation and manpower scheduling shortages, which often occur during business peaks or system upgrade windows.

[0065] Each driver needs to establish a corresponding performance indicator change characteristic template (e.g., under the same parameter change range, poor process integration mainly increases latency, and insufficient resources mainly decrease data consistency and accuracy). The parameter disturbance set obtained by S2 is mapped to the basis function library according to time sections and business units. Pattern matching algorithms (such as dynamic time warping or feature vector similarity calculation) are used to determine the main driver of each disturbance event. The composite disturbance curve is then restored by weighted superposition, thereby separating the disturbance component of a single driver.

[0066] For the separated driving force components, calculate their proportion in the total disturbance energy (which can be measured by the efficiency index, the sum of squared deviations), and obtain the quantitative contribution ratios of process inconsistency, rule adaptation deviation, and insufficient resource supply.

[0067] The routing anomaly set has been extracted, and its manifestations may include loop suppression failure, unknown attribution handling overload, etc. This step requires penetrating the symptom level to locate the specific logical root cause of the anomaly: Check the matching logic of the routing mapping table to confirm whether there is a missing timestamp verification. That is, when determining whether a task has been looping on the same node, the time information of the task flow is not included in the matching condition, which causes the same task to be mistakenly judged as a new task and enter the loop because the identifier is the same but the time is different. Analyze the sequence of task ID and node reception time through log backtracking to verify whether the same ID enters the same node multiple times in a short period of time.

[0068] Analyze the input features and training sample coverage of the pre-classification model to confirm whether there are any model inaccuracies, such as incomplete feature extraction for new business documents or a lack of similar cases in the training set, leading to a large number of requests being misclassified as having unknown attribution. Statistically analyze the confidence distribution of the model output; if a high proportion of low confidence scores directly flow into the unknown attribution handling process, it verifies that the overload originates from classification failure. Similarly, this can be extended to issues such as mis-triggering of targeted broadcasts (possibly due to incorrect attribution identifier resolution) and rampant default full-range distribution (possibly due to improper routing priority configuration), all of which require verification through configuration auditing and runtime data.

[0069] In a specific implementation, the contribution ratios of process-type, rule-type, and resource-type disturbances obtained from relative weight analysis are further mapped to physical drivers.

[0070] Three driving factors are defined: poor process integration, rule adaptation deviation, and insufficient resource supply. A template for the characteristics of performance indicator changes is established. Poor process integration mainly increases the delay time L_d, rule adaptation deviation mainly reduces the data consistency accuracy A_c, and insufficient resource supply simultaneously increases the end-to-end processing time T_e and increases the anomaly rollback rate R_f.

[0071] Taking an event from the parameter disturbance set obtained by S2, at a time point during the normal stable period under routing rule A, CPU utilization increased from 60% to 95% and tax policy rule matching accuracy decreased from 92% to 85%, causing end-to-end timeliness to increase from 3.01 seconds to 3.45 seconds, latency to increase from 12 seconds to 17 seconds, accuracy to decrease from 97.85% to 96.1%, and rollback rate to increase from 0.59% to 1.05%. This event is mapped to a base function library according to time segments and business units. The similarity score with the three types of driver templates is calculated using the dynamic time warping algorithm. The similarity score is 0.62 for poor process connection, 0.83 for rule adaptation deviation, and 0.55 for insufficient resource supply. The dominant driver is determined to be rule adaptation deviation.

[0072] All disturbance events are attributed and weighted using this method to reconstruct the composite disturbance curve, separating the individual driving force components. The total disturbance energy E_total = ∑(ΔT_e) is calculated. 2 +(ΔL_d) 2 +(ΔA_c) 2 +(ΔR_f) 2 Where ΔA_c and ΔR_f are percentage point differences, for an event ΔT_e=0.44, ΔL_d=5, ΔA_c=-1.75, ΔR_f=0.46, we get E_event=0.44. 2 +5 2 +(-1.75) 2 +0.46 2=0.1936+25+3.0625+0.2116=28.4677. The contribution of the rule adaptation deviation component to ΔA_c and ΔR_f in E_event is 3.0625+0.2116=3.2741, accounting for η_r=3.2741 / 28.4677=0.1150, or 11.5%. Similarly, the proportion of process inefficiency η_p=25 / 28.4677=0.8784, or 87.8%, and the proportion of insufficient resource supply η_s=0.1936 / 28.4677=0.0068, or 0.68%, indicating that the disturbance of this event is mainly caused by process inefficiency. For the set of routing anomalies, the routing mapping table matching logic was checked. It was found that timestamp verification was not introduced when the task loop was judged. Log backtracking showed that task IDX123 entered node N5 three times at 10:00:01, 10:00:03, and 10:00:05. The interval was less than the threshold of 2 seconds, which was confirmed as the root cause of loop suppression failure.

[0073] Analyzing the input features of the pre-classification model revealed that 38% of the outputs had low confidence levels, with 72% of these flowing into the unknown attribution handling process. This verified that the overload on this path stemmed from incomplete feature extraction of new business documents and insufficient training sample coverage. Configuration auditing revealed that the default full-range distribution switch for routing priority was enabled, which was associated with accidental activation of targeted broadcasting. This allowed for the identification of the root cause from the observed phenomenon, providing a basis for targeted corrections.

[0074] Specifically, for different physical drivers and root causes of routing issues, formulate and implement actionable, coordinated optimization measures to ensure that driver-based governance and routing layer repair work synergistically. Optimization of rule adaptation bias: When tax policies are updated or routing logic is changed, the latest rule package is automatically retrieved and the old version is replaced to reduce delays caused by manual intervention. The new rules are applied to low-traffic business units or simulated datasets, and performance indicators are monitored for changes. The full rollout is only pushed after confirming that there are no anomalies.

[0075] Optimization of insufficient resource supply: When resource bottlenecks are anticipated during peak business periods or system upgrade windows, server instances, network bandwidth, or storage capacity should be added in advance. Dynamic load balancing algorithms should be used to distribute resource-intensive tasks to idle nodes, and task priority queues should be set up to avoid blocking critical processes.

[0076] Optimization of workflow inconsistencies: Unify the data exchange format and handshake mechanism between different systems to reduce parsing errors and retries; add temporary queues or asynchronous processing channels between critical serial links to smooth out sudden traffic surges and reduce latency.

[0077] Routing layer tuning: Add timestamps and business scenario identifiers to the routing mapping table to prevent loop suppression from failing; optimize the feature engineering and training strategy of the pre-classification model to improve the accuracy of attribution judgment and reduce invalid inflows of unknown attribution handling; dynamically adjust the triggering order of routing rules according to the business stage, for example, during policy switching periods, prioritize the accuracy of targeted broadcasts rather than coverage.

[0078] After implementing optimization measures, it is necessary to re-collect end-to-end processing time, abnormal rollback rate, dwell time, and newly added routing quality indicators (such as route matching accuracy, false trigger rate, and loop occurrence rate), and compare them with the baseline before optimization: Using the S1 financial and tax integration efficiency diagnosis model, under the same parameter-rule combination and business scenario, optimized efficiency data is collected and the mean and distribution are calculated. Efficiency deviation extremes (maximum positive / negative deviation from the ideal steady-state baseline after optimization) and routing risk extremes (such as maximum number of loops, highest proportion of unknown attribution handling) are calculated. These extremes are compared with preset risk control thresholds (such as an abnormal rollback rate not exceeding 2%, and a delay increase not exceeding 10% of the steady-state value) and efficiency improvement targets (such as end-to-end timeliness reduced by more than 15%). Based on the threshold comparison results, a compliance determination is made; if the conditions are not met, a backtracking iteration is initiated. If the extreme values ​​of performance deviation and routing risk after optimization are both within the threshold range, and the key performance indicators are significantly improved compared to before optimization, then the solution is deemed to meet the requirements, the configuration can be solidified and incorporated into the daily operation and maintenance baseline; if a certain indicator does not meet the requirements, return to S2 to re-analyze the dominant components in the current stage and scenario (which may be due to changes in the parameter disturbance structure caused by optimization), update the multidimensional dominant graph, and re-execute the driving force decomposition and root cause location in S3 to carry out a new round of coordinated optimization.

[0079] In a specific implementation, adjustments are made to address rule adaptation deviations. After tax policy updates, the latest rule package is automatically retrieved to replace the old version. The new rules are first deployed to the low-traffic environment of the sales business units of medium-sized regional companies. Changes in end-to-end processing timeliness and data consistency accuracy are monitored. Once no anomalies are confirmed, the rules are pushed to the full system.

[0080] To address resource supply shortages, during anticipated peak business periods of policy transitions, server instances were expanded to 1.5 times their original number, network bandwidth was increased by 30%, and a dynamic load balancing algorithm was implemented to migrate tasks with CPU usage exceeding 80% to idle nodes. A task priority queue was also established to ensure the execution of critical processes. To address workflow inefficiencies, cross-system data exchange was standardized to JSON format, and the handshake protocol was standardized. A temporary queue was added between the application acceptance and review processes, reducing the peak-period delay increase from 15% to 4%. For routing layer optimization, timestamps and business scenario identifiers were added to the mapping table to prevent loop suppression failure. Pre-classification model features were optimized, and new document samples were added, improving the attribution accuracy from 82% to 94% and reducing the influx of cases with unknown attribution. During policy transitions, the routing trigger order was adjusted to prioritize the accuracy of targeted broadcasts.

[0081] After optimization, performance data was collected under the same parameter-rule combination and the normal stable period routing rule A. The average end-to-end timeout was 2.53 seconds, the average dwell time was 9.8 seconds, and the average abnormal rollback rate was 0.41%. The route matching accuracy reached 96%, the false trigger rate was 1.2%, and the loop occurrence rate was 0. Using the S1 model to calculate the extreme values ​​of performance deviation, the maximum negative deviation of timeout was |2.53-3.01|=0.48 seconds, the maximum negative deviation of dwell time was |9.8-12.0|=2.2 seconds, and the maximum positive deviation of abnormal rollback rate was 0.41%-0.59%=-0.18 percentage points. The extreme values ​​of routing risk were 0 loop occurrences and a decrease in the proportion of unknown attribution handling from 8.0% to 2.5%. The risk control thresholds are set as follows: abnormal rollback rate not exceeding 2%, dwell time increase not exceeding 10% of steady-state value (i.e., ≤13.2 seconds), and end-to-end time reduction exceeding 15% (target ≤2.56 seconds). The efficiency improvement calculation formula is η_t=(T_baseline-T_opt) / T_baseline×100%, which gives η_t=(3.01-2.53) / 3.01×100%=15.95%. If both the efficiency deviation extreme value and the routing risk extreme value are within the threshold, the key indicators are significantly improved, and the time reduction exceeds 15%, the solution is deemed to meet the standards, and the configuration is solidified into the daily operation and maintenance baseline. If any indicator does not meet the standards, the process returns to S2 to reanalyze the dominant components and update the multidimensional dominant graph, and then re-executes S3 for driver decomposition and root cause location to enter the next round of coordinated optimization.

[0082] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0083] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An enterprise finance and tax integration method, characterized in that: The integration method includes the following steps: S1: Combining different business drive types and internal and external interference sources, extract key parameters from multi-source data to form a combined dataset. Relying on the financial and tax integration efficiency diagnosis model, calculate the end-to-end indicators of each business unit under different combinations of parameters and rules, and establish a baseline response surface. S2: Average the baseline response surface data over time to obtain the ideal steady-state baseline performance value. Extract the parameter disturbance set and routing anomaly set by separating the low-frequency trend term, identify the dominant influence dimension at different stages, and segment the scene according to the scale and type of business unit to form a multi-dimensional dominant map. S3: Using the Taylor deconvolution algorithm, the perturbation of the dominant parameter in the multidimensional dominant graph is decomposed into physical causes, and the contribution ratio of each is quantified. The root cause of routing anomalies is located, and the linkage optimization is implemented based on the causes and root causes.

2. The enterprise finance and tax integration method of claim 1, wherein: The baseline response surface data is averaged over time to obtain the ideal steady-state baseline performance value, including the following steps: Based on the characteristics of the business cycle, an averaging window is set. For a certain set of fixed parameter values ​​and routing rules, the performance index observations of all business units within the averaging window are collected. Robust statistical methods are used within the window to identify outliers that deviate from the majority value. The arithmetic mean of the remaining valid samples is calculated for each indicator, forming the steady-state performance vector of the averaged window. The steady-state efficiency vectors for all time periods are averaged again to obtain the ideal steady-state baseline efficiency value.

3. The enterprise finance and tax integration method of claim 2, wherein: By separating the low-frequency trend term to extract the parameter disturbance set and the routing anomaly set, the dominant influencing dimensions at different stages are identified, including the following steps: The measured values ​​of the performance indicators of each business unit within a continuous observation period are arranged into a time series at a fixed sampling frequency, and the corresponding ideal steady-state baseline value is subtracted to obtain the deviation series; The deviation sequence is filtered to remove low-frequency components below the cutoff frequency, and the high-frequency part is retained as the disturbance signal. The source of the filtered disturbance signal is traced and matched with the time point of parameter change or routing rule trigger to obtain the parameter disturbance set. After obtaining the parameter perturbation set, determine the relative impact weight of the parameter perturbation on performance fluctuations; Statistical analysis of routing rule triggering rates and their impact on performance; identification of dominant influencing dimensions at different stages. Data is segmented according to business stages, the number of times routing rules are triggered and the total number of requests are counted, and the trigger rate is calculated. The performance indicators before and after each rule trigger are differentially calculated, and the average impact is calculated by grouping according to the rule. By combining the trigger rate with the average direction / magnitude of influence, the dominant dimension at a certain stage can be determined.

4. The enterprise finance and tax integrated integration method according to claim 3, characterized in that: Segmenting scenarios based on the scale and type of business units to form a multi-dimensional dominant graph includes the following steps: Business units are divided into two dimensions based on scale and type, forming multiple sub-scenarios. The aforementioned contribution and dominant dimension analysis is repeated in each sub-scenarios, and the differences are compared to obtain the perturbation weight distribution and dominant dimension of the sub-scenarios. By summarizing the patterns of disturbance sensitivity and dominant dimensions of different scale-type combinations, we can provide a basis for differentiated operation and maintenance and form a multi-dimensional dominant map.

5. The integrated enterprise finance and taxation method according to claim 4, characterized in that: After obtaining the parameter perturbation set, the relative impact weights of the parameter perturbations on performance fluctuations are determined, including the following steps: Construct a perturbation impact matrix, where rows represent observed perturbation events and columns represent the perturbation intensity of parameters, and include the performance fluctuation amplitude as the target variable; Stepwise regression is used to find the contribution of each item. A separate regression model is built for each type of parameter to predict the performance fluctuation and record the coefficient of determination. The parameters are then included in the regression model and the overall coefficient of determination is recorded. The explained variance of each parameter model is proportionally allocated to the total explained variance to obtain its relative weight in the total fluctuation. The parameters are then sorted from high to low weight to identify the dominant parameter categories. If the weight of a rule class is higher than that of other classes in a certain stage, it indicates that the instability of the routing logic in that stage is the main cause of the performance bottleneck.

6. The integrated enterprise finance and taxation method according to claim 3, characterized in that: The filtered disturbance signal is traced back to its source, matched with the time point of parameter change or routing rule triggering, to obtain the parameter disturbance set, including the following steps: If a disturbance occurs synchronously with a change in a key parameter, it is included in the parameter disturbance set. If the disturbances are concentrated in the activation of routing rules and accompanied by abnormal performance offsets, they are classified into the routing anomaly set. The disturbance event record is formed by comprehensively measuring the amplitude and duration of the disturbance signal.

7. The integrated enterprise finance and taxation method according to claim 4, characterized in that: The multidimensional dominant graph structure dimensions include the stage axis, parameter perturbation axis, scene axis, and routing perturbation axis.

8. The integrated enterprise finance and taxation method according to claim 1, characterized in that: Using the Taylor deconvolution algorithm, the perturbations of the dominant parameters in the multidimensional dominant spectrum are decomposed into physical drivers, and their respective contribution ratios are quantified, including the following steps: The contribution ratio of parameter perturbation to performance fluctuation is obtained by relative weight analysis. The composite perturbation effect is regarded as the superposition of responses of several basic driving forces. The independent action of a single driving force is separated by inverse analysis and its contribution ratio is quantified. For each driver, a corresponding performance indicator change characteristic template is established. The parameter disturbance set is mapped to the basis function library according to the time section. The pattern matching algorithm is used to determine the main driver of each disturbance event. The composite disturbance curve is restored by weighted superposition, and the disturbance component of the single driver is separated. For the separated dynamic components, calculate their proportion in the total disturbance energy to obtain a quantitative contribution ratio.

9. The integrated enterprise finance and taxation method according to claim 8, characterized in that: Root cause analysis of routing anomalies, followed by coordinated optimization based on the underlying causes and causes, including the following steps: Check the matching logic of the routing mapping table to determine if there are any missing timestamp verifications; By analyzing the sequence of task ID and node reception time through log backtracking, we can verify whether the same ID enters the same node multiple times. For different physical drivers and routing root causes, generate coordinated optimization measures, including optimization of rule adaptation deviations, optimization of insufficient resource supply, optimization of process connection problems, and optimization of routing layer.

10. An integrated enterprise financial and tax system, used to implement the integration method according to any one of claims 1-9, characterized in that: include: Surface creation module: Combining different business drive types and internal and external interference sources, key parameters are extracted from multi-source data to form a combined dataset. Based on the financial and tax integration efficiency diagnosis model, end-to-end indicators of each business unit under different combinations of parameters and rules are calculated, and a baseline response surface is established. Image generation module: Averages the baseline response surface data over time to obtain the ideal steady-state baseline performance value. Extracts parameter disturbance set and routing anomaly set by separating low-frequency trend terms, identifies the dominant influence dimension at different stages, and segments the scene according to the scale and type of business unit to form a multi-dimensional dominant map. Tuning Module: Using the Taylor deconvolution algorithm, the perturbation of the dominant parameters in the multidimensional dominant graph is decomposed into physical causes, and the contribution ratio of each is quantified. The root cause of routing anomalies is located, and linkage tuning is implemented based on the causes and root causes.

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