Business finance and tax data intelligent analysis and decision-making method and system

Abstract

The invention relates to the technical field of computers, discloses an intelligent business finance and tax data analysis and decision method and system, and aims to solve the problems of weak business finance and tax data integration capability, poor model generalization, insufficient real-time analysis and decision closed loop deficiency in the prior art. The method comprises the following steps: acquiring original business, financial and tax data through an enterprise information system interface; executing standardized cleaning and generating a structured data set; performing cross-system association based on the unified credit code and the timestamp, and constructing a three-dimensional association matrix; multidimensional dynamic characteristics including income acceleration, cost fluctuation, tax deviation and the like are extracted; according to the method, deep fusion of multi-source data is realized by establishing the business finance and tax knowledge graph, the model prediction precision is improved by adopting multi-scale dynamic feature coding, the environmental adaptability is enhanced in combination with an online learning mechanism, the decision response time is shortened through quantitative analysis and visual interaction, and the operation agility and compliance management and control capability of an enterprise are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of computer technology, specifically relating to a method and system for intelligent analysis and decision-making of business, financial and tax data. Background Technology

[0002] As enterprises deepen their digital transformation, efficient collaboration of business, financial, and tax data has become a core element in improving organizational management efficiency. Integrated business, finance, and tax systems, by consolidating various key data generated during business operations, provide a crucial foundation for decision support, compliance control, and resource optimization. In today's complex economic environment, enterprises not only need to process massive amounts of transaction data but also ensure the accuracy of financial accounting and the compliance of tax declarations, which places higher demands on the automation and intelligence of data processing.

[0003] Among them, intelligent analysis technology based on big data management and machine learning is gradually being applied to the field of enterprise-level data governance. This direction aims to build an analysis framework with strong interpretability and timely response by collecting, cleaning and modeling multi-source heterogeneous business data, thereby realizing automated tracking and intelligent judgment of the entire chain from business occurrence to financial accounting and tax calculation. Its core objective is to break down the information silos between business flow, capital flow, invoice flow and tax reporting flow in traditional systems, and improve the overall data flow efficiency and decision accuracy.

[0004] Existing technologies still have significant shortcomings in achieving deep integration of business, finance, and tax data for intelligent decision-making: First, data integration capabilities are weak, making it difficult to effectively integrate structured and unstructured data from ERP, CRM, e-invoice platforms, and tax systems, resulting in an incomplete analytical foundation. Second, model generalization capabilities are insufficient; existing machine learning methods are mostly designed for specific scenarios and lack cross-industry and cross-tax system adaptability, making it difficult to cope with complex and ever-changing policy environments. Third, real-time analysis capabilities are limited; most systems only support post-event statistics and report generation, failing to identify potential financial and tax risks and provide intervention suggestions in real time when transactions occur. Finally, the decision-making closed loop is missing; current solutions generally remain at the "analysis-early warning" level, failing to link with the enterprise's approval processes, accounting engines, and reporting systems, making it difficult to translate intelligent outputs into actual control actions. These problems severely restrict the operational agility and compliance capabilities of enterprises under high regulatory intensity, urgently requiring an intelligent business, finance, and tax analysis and decision-making solution with deep semantic understanding, dynamic learning capabilities, and system-level linkage mechanisms. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies by providing an intelligent analysis and decision-making method and system for business, finance, and tax data, which can effectively solve the problems in the background technology. Currently, in the operation of enterprises, financial, tax, and business data are often fragmented, leading to inconsistent data definitions, delayed information transmission, and difficulties in cross-domain collaboration, hindering integrated real-time insights and scientific decision-making. Traditional data analysis systems only perform static processing on single-dimensional data, lacking the ability to deeply integrate multi-source heterogeneous data. They cannot capture the dynamic correlation between business occurrences and financial and tax results, causing management decisions to rely on experience-based judgments, resulting in slow response speeds and low accuracy. Furthermore, existing systems generally suffer from low data governance efficiency, poor modeling flexibility, and insufficient prediction accuracy when facing massive, high-frequency, and multimodal enterprise operational data, severely restricting the improvement of enterprises' intelligent management level. To overcome these deficiencies, this invention, based on a big data management architecture and machine learning algorithm system, constructs a fully automated analysis framework integrating data fusion, feature engineering, intelligent modeling, and decision output. This framework achieves end-to-end quantitative analysis from business behavior to financial and tax impacts, significantly improving enterprise resource allocation efficiency and risk control capabilities.

[0006] To achieve the above objectives, the present invention provides the following technical solution: On one hand, a business, finance, and tax data intelligent analysis and decision-making system, comprising the following components: a data access and standardization module, used to collect raw business, financial, and tax data from enterprise resource planning systems, financial accounting systems, electronic invoice platforms, and business operation terminals, and to perform missing value imputation, outlier identification, and format unification processing on the raw data according to preset data cleaning rules, generating a structured standard dataset; a data fusion and association mapping module, used to precisely align standard data from different systems according to timestamps and business events based on the enterprise's unique identifier and transaction serial number, and to establish a topological connection relationship between business activity nodes and corresponding financial and tax subjects through graph construction technology, forming a unified business, finance, and tax knowledge graph; and a feature extraction and dynamic encoding module, used to extract multi-dimensional time-series feature vectors from the knowledge graph, including but not limited to revenue growth rate and cost fluctuation. The system includes modules for dynamic indicators such as growth rate, tax burden change trend, cash flow cycle indicators, and related-party transaction density. It also utilizes a sliding window mechanism to calculate the ratio of the short-term moving average to the long-term benchmark value for each feature, generating dynamic feature representations with spatiotemporal awareness. A model training and adaptive optimization module deploys an integrated learning model and a deep neural network hybrid architecture. Using the dynamic feature representations as input, it is trained through supervised learning to predict future changes in financial and tax indicators, identify potential compliance risks, and recommend optimal business strategies. An online learning mechanism is introduced to periodically update the model parameters using newly generated real-world data, ensuring the model maintains consistently high predictive accuracy. A decision generation and visualization output module automatically generates actionable management decision reports based on the model's risk score, trend prediction results, and strategy recommendations. An interactive dashboard presents the evolution path of key indicators, the distribution of risk hotspots, and simulations of strategy implementation effects, supporting rapid response and dynamic adjustments by management.

[0007] Preferably, when performing data cleaning, the data access and standardization module uses a dual-threshold discrimination method based on statistical distribution characteristics to detect anomalies in numerical fields. Specifically, for any continuous variable, the mean μ and standard deviation σ within the sample period are first calculated, and a lower threshold threshold of μ-3σ and an upper threshold threshold of μ+3σ are set. If a data point exceeds this range, it is marked as a suspected outlier. Subsequently, the suspected outlier is further verified using a business logic rule base. If the contradictory conditions defined in the rule base are met simultaneously, it is determined to be a real anomaly and is corrected or removed. This process ensures data quality while avoiding misjudgments caused by purely mathematical criteria.

[0008] Furthermore, the data fusion and association mapping module employs a three-level index structure to accelerate entity matching when constructing the business, finance, and tax knowledge graph: the first level is the organization code index, used to quickly locate all subsystem data under the same legal entity; the second level is the transaction date and time sequence number combined index, used to arrange business events in chronological order within the same entity; and the third level is the summary keyword hash index, used to identify transaction descriptions with the same economic substance but slightly different expressions, thereby improving the coverage and accuracy of cross-system data links.

[0009] Furthermore, when generating dynamic feature representations, the feature extraction and dynamic encoding module introduces a multi-scale time aggregation strategy, setting four sliding windows of granularity: 1 day, 7 days, 30 days, and 90 days. For each feature variable, its statistics within each window are calculated independently, including mean, variance, maximum value, minimum value, and slope of change. These multi-granular features are then concatenated into a high-dimensional feature vector, enabling the model to simultaneously capture short-term fluctuation signals and long-term trend directions.

[0010] Preferably, in the hybrid architecture adopted by the model training and adaptive optimization module, the ensemble learning part consists of a gradient boosting tree model, which is responsible for handling classification and regression tasks with obvious nonlinear relationships and is particularly good at identifying sparse risk signals; the deep neural network part is composed of a long short-term memory network and an attention mechanism, which is dedicated to modeling sequence dependencies over long time spans. By assigning higher weights to key time nodes in historical data, it enhances the responsiveness to major policy adjustments or market changes.

[0011] Furthermore, when performing incremental updates, the model training and adaptive optimization module uses a sliding historical window to retain the actual observation data of the most recent 12 months as the retraining set. After each update cycle is triggered, the data in the window is automatically extracted to retrain the model. At the same time, a model performance monitoring unit is set up to compare the decrease in prediction error of the new and old models on the validation set. If the improvement is less than 0.5%, the original model is maintained to prevent system disturbances caused by invalid iterations.

[0012] In addition, when generating management decision reports, the decision generation and visualization output module sorts multiple strategy suggestions according to preset priority rules. The sorting criteria include four dimensions: expected return amount, implementation difficulty coefficient, compliance risk level, and execution cycle. The suggestion with the highest comprehensive weighted score is displayed at the top to help managers focus on key action items.

[0013] On the other hand, a smart analysis and decision-making method for business, finance, and tax data includes the following steps: Step S110, batch acquisition of raw business data, financial data, and tax declaration data through the enterprise's internal information system interface. The raw data includes sales orders, purchase contracts, bank statements, accounting vouchers, value-added tax invoices, and income tax settlement forms; Step S120, execution of a standardized cleaning process on the raw data, including field type conversion, null value filling, duplicate record deduplication, and illegal character filtering, outputting a standard dataset that meets the requirements of a unified data model; Step S130, cross-system association of data records belonging to the same economic event in the standard dataset based on the enterprise's unified social credit code and transaction timestamp, constructing a three-dimensional association matrix containing business events, capital flows, and tax calculation paths; Step S140, extraction of multi-dimensional dynamic features reflecting the enterprise's operating status from the three-dimensional association matrix, the dynamic features including at least the year-on-year growth rate of monthly operating revenue. The system includes: 1) Speed, unit cost month-on-month change rate, degree of deviation of comprehensive tax burden rate from historical average, fluctuation range of accounts receivable turnover days, and trend of related-party transaction proportion; 2) Inputting the multi-dimensional dynamic features into a pre-trained machine learning prediction model, which learns a mapping function between business changes and subsequent financial and tax results based on historical data, and outputs predicted values ​​for total profit, tax payable, and cash flow status for the next three accounting periods; 3) Comparing the prediction results with preset warning thresholds, triggering a risk warning mechanism when any prediction indicator deviates by more than ±15%, and simultaneously generating a risk handling plan including cause analysis and response measures suggestions; 4) Based on the optimal strategy recommendation results output by the prediction model, combined with constraints such as current inventory level, customer credit rating, and supplier performance capability, solving for the optimal resource allocation scheme, and displaying the simulated effect of changes in key performance indicators before and after strategy implementation in the form of visual charts.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] By establishing a unified business, finance and tax knowledge graph, deep data integration across systems, departments and time dimensions has been achieved, solving the problem of information silos in the traditional model and improving data integration efficiency by more than 80%.

[0016] By employing a multi-scale dynamic feature encoding method, the ability to represent nonlinear and time-varying features in complex business environments is enhanced, reducing the mean absolute percentage error (MAPE) of the prediction model to 6.2%, which is significantly better than the 13.8% of the conventional statistical model.

[0017] An online learning mechanism is introduced to drive adaptive optimization of the model, ensuring that the system can maintain high-precision prediction capabilities even under changes in the external environment (such as tax policy adjustments), and the model effectiveness decay period is extended to 2.3 times the original.

[0018] The decision generation process integrates quantitative analysis and visual interaction, transforming abstract data into actionable management instructions, reducing the response time for high-level decisions to one-third of the original time, and significantly improving the company's agile operational capabilities.

[0019] The overall system has good scalability and universal adaptability, and is suitable for various industries such as manufacturing, retail and service. It has been deployed and applied in several large group enterprises, and has avoided 12 major tax compliance risks. The average annual tax burden has been optimized to 0.7% of total revenue. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the overall technical solution architecture of the [Intelligent Analysis and Decision-Making Method and System for Business, Finance and Tax Data] proposed in this invention;

[0021] Figure 2 This is a schematic diagram of the core principle framework of the [adaptive fusion algorithm and online learning-driven hybrid prediction model] in this invention. Detailed Implementation

[0022] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0023] Example 1

[0024] Please refer to Figure 1 and Figure 2 In a digital management upgrade project of a large chain retail group, the company faced a serious disconnect between sales data, financial accounting data, and tax invoice declaration data. Each store generates over 500,000 transaction records daily, scattered across POS systems, ERP systems, and e-invoice platforms. Due to the lack of a unified data semantic model, the average monthly closing cycle was extended to 7 working days, and tax warnings were repeatedly triggered due to inconsistencies between revenue recognition and VAT payment obligations. To address this, this invention provides an intelligent analysis and decision-making method and system for business, financial, and tax data, deployed in the group's private cloud environment, building a fully automated analysis engine based on a big data architecture.

[0025] After the system starts up, the data access and standardization module first pulls raw business data from the POS terminals of each store in real time through the API interface, including product SKU codes, sales quantity, transaction amount, cashier ID, and transaction timestamp; at the same time, it retrieves accounting voucher data in batches from the headquarters ERP system, covering general ledger account number, debit / credit direction, accounting date, and auxiliary accounting items; and through the digital certificate authentication channel of the State Taxation Administration, it synchronizes the issuance and red-inking records of value-added tax invoices on a regular basis, including invoice code, invoice date, tax numbers of the buyer and seller, tax rate, and amount excluding tax. After all raw data enters the system buffer, a standardized cleaning process is immediately executed: Field type conversion forces the string-type amount field output by the POS system to be converted to a 64-bit floating-point format and verifies its compliance with the IEEE 754 standard; Null value filling adopts a combination of forward filling and interpolation strategies. For three or more consecutive missing sales records, linear interpolation is used to fill in the missing records using the historical average of the same store and the same time period; Duplicate record deduplication is performed based on a hash comparison of the five-tuple (store number, cash register number, transaction number, transaction time, total amount). If duplicates are found, the record with the earliest timestamp is retained and marked as an anomaly log; Illegal character filtering removes all text fields containing Unicode control characters or SQL injection characteristics to prevent parsing errors in subsequent processing.

[0026] After cleaning, the data is organized into a standard dataset conforming to a unified data model. Each row contains 38 fields with a fixed structure, including 12 primary key fields, 16 numerical indicator fields, and 10 category label fields. This standard dataset is then transmitted to the data fusion and association mapping module to perform cross-system data alignment. Specifically, the system uses the enterprise's unified social credit code as the global primary key and establishes an index table at the database level to quickly locate all subsystem data sources under the same legal entity. Then, it performs millisecond-level alignment based on transaction timestamps, setting a time tolerance window of ±30 seconds to group POS sales records, bank receipts, and VAT invoice issuances occurring within the same economic transaction into a single logical event unit. Finally, it uses a hash index of digest keywords to match transaction description text, mapping different expressions such as "member points redemption," "gift card consumption," and "prepaid card recharge" to the same "non-cash payment" business type node, thereby resolving semantic ambiguity caused by differences in input habits. Based on this, the system uses Neo4j graph database technology to construct a three-dimensional association matrix. Each node represents a business event, fund flow, or tax calculation path. Edge relationships are defined by three semantic types: "trigger," "corresponding," and "belonging," forming a business, finance, and tax knowledge graph with a topological structure.

[0027] This knowledge graph contains over 230 million entity nodes and 470 million relationship edges, covering all operational data from the past 24 months. The feature extraction and dynamic encoding module extracts multi-dimensional time-series feature vectors, performing incremental calculations every hour. Taking the year-on-year growth rate of monthly operating revenue as an example, the system first aggregates the sales revenue of each store by calendar month, generating a 24-month time series; then, it calculates the relative growth rate of the current month compared to the same period last year by sliding a 12-month window, using the following formula:

[0028]

[0029] in Indicates the first Total monthly income To avoid the denominator being zero, the unit cost month-on-month change rate is obtained by applying a moving weighted average to the purchase price series; the deviation of the comprehensive tax burden rate is measured by comparing the current actual tax burden with the Z-score of the historical average over the past 36 months; the fluctuation range of accounts receivable turnover days is quantified using the range normalization method; and the trend of related-party transaction proportions is calculated by binary labeling based on whether the counterparty exists in the group's related-party list and then summing the results. These five basic features are further processed using a multi-scale time aggregation strategy, with four sliding windows of 1 day, 7 days, 30 days, and 90 days. For each feature, its statistics within each window are calculated independently—including mean, variance, maximum, minimum, and linear regression slope—and finally concatenated into a high-dimensional dynamic feature vector of 5×4×5=100, which serves as the model input.

[0030] The model training and adaptive optimization module deploys a hybrid architecture of ensemble learning and deep neural networks. The gradient boosting tree sub-model, implemented using the XGBoost framework, comprises 500 regression trees, each with a maximum depth of 8 and a fixed learning rate of 0.05. L1 regularization (α=1.0) is used to suppress overfitting. This part is specifically designed to identify sparsely distributed risk signals, such as outliers like large single-transaction discount reversals or sudden increases in abnormal return rates. The Long Short-Term Memory (LSTM) network sub-model consists of two bidirectional LSTM layers with 128 hidden layers. An attention mechanism dynamically allocates weights, enabling the model to focus on the impact of significant policy adjustments at key time points, such as changes in tax exemption policies during the pandemic or system fluctuations during the initial implementation of the Golden Tax System Phase IV. The entire hybrid model is trained using supervised learning, with target variables being the total profit, tax payable, and net cash flow from operating activities for the first, second, and third future accounting periods, resulting in a total of nine predictive output channels. The training dataset is derived from actual observations over the past 18 months and is divided into training, validation, and test sets in an 8:1:1 ratio. The HuberLoss loss function is used to combine the advantages of MAE and MSE. A complete training cycle on a GPU cluster takes approximately 4.2 hours.

[0031] The online learning mechanism is set to automatically trigger an incremental update cycle at 2:00 AM daily. The system maintains a sliding history window, retaining only the actual observation data from the most recent 12 months as the retraining set. Each update involves retraining the model parameters using all samples within this window. To prevent system disturbances caused by invalid iterations, a model performance monitoring unit continuously tracks changes in prediction error: it calculates the decrease in the mean absolute percentage error (MAPE) of the new and old models on the independent validation set. If the decrease is less than 0.5%, the update is considered to have no significant improvement, and the system automatically rolls back to the original version and records an alarm log. Furthermore, the model has a concept drift detection function. When the KL divergence of the input feature distribution exceeds the threshold of 0.15 for three consecutive days, a full retraining process is initiated to ensure adaptability to drastic changes in the external environment (such as tax rate adjustments or changes in accounting standards).

[0032] After receiving the model output, the decision generation and visualization output module performs multi-level decision derivation. The risk warning mechanism establishes a three-tiered early warning system: Level 1 warnings correspond to prediction deviations between ±10% and ±15%, providing only internal alerts; Level 2 warnings correspond to deviations exceeding ±15%, triggering email and SMS notifications; Level 3 warnings correspond to deviations exceeding ±25% and persisting for more than two cycles, automatically generating a risk management plan. The plan includes a cause analysis module, which uses the SHAP interpretability algorithm to trace the contribution of key features leading to prediction deviations; and a response suggestion module with a built-in rule engine, outputting specific action instructions based on a preset logic chain, such as "suspending credit lines to Category A customers" or "initiating a review of supplier B's fulfillment capabilities." The strategy recommendation stage introduces a linear programming solver, maximizing the expected revenue objective function and outputting an optimal resource allocation solution under constraints such as inventory levels not falling below a safety threshold, customer credit ratings above BBB, and supplier fulfillment rates greater than 95% in the past three months.

[0033] Ultimately, all analysis results are presented through an interactive dashboard. Management can access the visualization interface via the web to view six types of chart components, including key indicator evolution path curves, risk hotspot geographic distribution heat maps, and KPI comparison bar charts before and after strategy implementation. The dashboard supports drill-down operations, allowing users to click on a branch icon to access granular operational data for that store; it also supports simulation and projection functions, allowing users to adjust assumptions (such as promotional intensity and pricing strategies) and refresh prediction results in real time; and it supports one-click export of management decision reports in PDF format, with strategy recommendations in the report automatically sorted according to priority rules: the comprehensive weighted score is jointly determined by expected revenue (40% weight), implementation difficulty coefficient (20% weight), compliance risk level (30% weight), and execution cycle (10% weight), with the highest score displayed at the top. Since the system went live, the retail group's monthly closing cycle has been shortened to 2 working days, quarterly tax self-inspection efficiency has increased by 85%, and an average of 3.6 potential compliance risks have been identified and avoided annually, with tax burden optimization contributing 0.72% to total revenue.

[0034] Example 2

[0035] For manufacturing enterprises with unique scenarios involving production planning, material consumption tracking, and input tax deduction, this embodiment proposes a modified structure for an intelligent analysis and decision-making system of business, finance, and tax data, suitable for the discrete manufacturing industry. Unlike the previous embodiment, which focused on sales-end data integration, this embodiment emphasizes strengthening the data collection capabilities of the production process and reconstructs feature engineering and predictive modeling logic to adapt to cost aggregation at the BOM (Bill of Materials) level and multi-stage tax compliance requirements.

[0036] The system hardware architecture adds industrial IoT gateway devices, deployed next to the PLC control systems in each production workshop. These gateways collect real-time operational status data from production equipment via the OPC UA protocol, including 17 types of sensor signals such as equipment start / stop times, energy consumption readings, output counts, and fault alarm codes. This data is synchronously uploaded to the central data platform along with work order execution progress, WIP (Work in Process) status information, and quality inspection records from the MES system. The data access and standardization module has been expanded to support unstructured log file parsing. A regular expression template library is used to perform structured parsing of automatically generated ASCII text logs from the equipment, extracting valid event entries and supplementing them with timestamps and unique equipment codes. The cleaning process incorporates a dual-threshold discrimination method based on statistical distribution characteristics to handle numerical fields. Taking the spindle speed of a CNC machine tool as an example, the system calculates the sample mean μ=1245rpm and standard deviation σ=86rpm for this model of equipment over the past 30 days. Based on this, the lower limit for anomalies is set as μ-3σ=987rpm, and the upper limit as μ+3σ=1403rpm. If the collected value exceeds this range at a certain moment, it is marked as a suspected anomaly. Subsequently, a secondary verification is performed by calling the business logic rule base. The rule base defines "short-term overspeed operation is allowed when the coolant flow rate is lower than the threshold and the spindle temperature is higher than 85℃". If this condition is met, the anomaly mark is removed; otherwise, it is determined to be a real anomaly, and a shutdown inspection process is initiated. This dual verification mechanism ensures data integrity and avoids false alarms caused by mechanical threshold judgment.

[0037] During the data fusion phase, the system constructs a specialized business, financial, and tax knowledge graph for manufacturing enterprises. Its core innovation lies in introducing a three-dimensional mapping mechanism of "process route - cost center - tax attribute." Each production order serves as the root node, decomposing into multiple process nodes. Each process is bound to a specific cost center number and applicable VAT rate category (such as a basic tax rate of 13%, a lower tax rate of 9%, or tax-exempt items). Raw material input data is precisely matched with the material requisition form and BOM version number in the ERP system, forming a material flow chain of "raw materials → semi-finished products → finished products." Labor hours data is provided by the HR attendance system and recorded in the corresponding cost center according to the work group. Manufacturing expenses are allocated to each product line according to the proportion of machine hours. All this information is connected through graph edge relationships, forming a complete cost collection path, and forming a closed-loop verification with the product tax rate information in the final sales invoice.

[0038] The feature extraction strategy has been adjusted accordingly. In addition to general dynamic features, four new manufacturing-specific features have been added: 1) Unit product energy consumption volatility, calculated as the standard deviation of the ratio of actual energy consumption to standard quota for the current shift; 2) Yield rate trend slope, obtained by fitting a linear regression line to the daily first-inspection pass rate sequence; 3) Input tax deduction timeliness index, which calculates the proportion of certified invoices to required invoices for the current month; and 4) Capacity utilization deviation, which measures the percentage difference between actual output and theoretical maximum capacity. These features have also undergone multi-scale sliding window processing, but the time granularity has been adjusted to 8 hours (shift), 24 hours (day), 7 days (week), and 30 days (month) to match the typical production scheduling cycle rhythm of the manufacturing industry.

[0039] In terms of model training, the gradient boosting tree component in the hybrid architecture enhances the modeling capability for discrete events, paying particular attention to the impact path of disruptive factors such as sudden equipment failures and raw material supply disruptions on subsequent fiscal and tax indicators. The LSTM network structure has been adjusted to a stacked three-layer structure to enhance its ability to remember dependencies on long-cycle production plans. For example, a certain type of high-end equipment requires 45 days of continuous processing from material input to completion, and the model needs to accurately capture the transmission effect of early process delays on the timing of later revenue recognition. The attention mechanism is specially designed as a "critical process focus" mode, giving higher attention weights to bottleneck processes to improve prediction accuracy. The online learning mechanism maintains a daily update frequency, but the retraining set time window is extended to the most recent 18 months to accommodate longer product lifecycle data.

[0040] The decision output module is adapted to the manufacturing management process, generating management decision reports that include not only financial and tax recommendations but also integrated production scheduling instructions. For example, when the model predicts that the gross profit margin of a product line will fall below the break-even point in the next three months, the system automatically generates three linked actions: the finance side suggests applying for high-tech enterprise tax incentives; the procurement side suggests switching to alternative suppliers to reduce raw material costs; and the production side suggests adjusting the production schedule to prioritize high-margin orders. The visualization dashboard adds a Gantt chart view, showing the matching between the production plan and the expected invoicing time, supporting drag-and-drop scheduling adjustments and real-time feedback on the impact on cash flow forecasts. This embodiment has been successfully deployed in two automotive parts manufacturers, achieving a 60% reduction in production anomaly response time, an increase in input tax deduction rate to 99.2%, and annual comprehensive cost savings of 1.05% of revenue.

[0041] Example 3

[0042] For the business, finance, taxation, and regulatory compliance scenarios of financial service institutions, this embodiment constructs a highly sensitive risk-driven analysis system. Its technical differences lie in: completely reconstructing the data fusion logic, adopting an event-driven architecture (EDA) to replace the traditional batch processing mode, and introducing streaming feature calculation and micro-batch model inference mechanisms to meet the stringent requirements of the financial industry for real-time anti-fraud and dynamic capital adequacy monitoring.

[0043] The system's underlying infrastructure uses Apache Kafka as its core message bus. All raw data is no longer imported in batches, but instead published as independent event streams to topic queues. Every transaction—whether it's a loan approval at the counter, a fund transfer initiated by online banking, or interest settlement in the automated clearing system—is packaged into a standardized JSON message body, containing fields such as event type, occurrence time, participating entities, amount, and context tags. The data access and standardization module becomes the first operator in the stream processing topology, driven by the Flink engine, performing millisecond-level real-time cleaning: field type conversion is done directly in memory; null value filling uses the local mean within the session window; duplicate detection is based on event fingerprint hashing, with an anti-duplicate window set to 10 seconds; illegal character filtering is embedded in the serialization and deserialization pipeline to ensure data purity.

[0044] The data fusion and association mapping module abandons static graph construction and instead adopts dynamic entity parsing technology. Whenever a new event arrives, the system instantly queries the existing entity registry. If a new customer identity or trading counterparty is found, a temporary placeholder node is created; if a record already exists, it is appended to the entity's behavioral trajectory chain according to the timestamp order. Association rules are no longer predefined fixed templates, but are controlled by a set of dynamically loadable DSL (Domain-Specific Language) scripts, such as suspicious patterns like "the interval between opening accounts at different branches with the same ID is less than 24 hours" or "the number of interbank transfers in a single day exceeds 5 times and the cumulative amount is greater than 500,000 yuan." Once a match is successful, a temporary edge relationship of "suspected related transaction" or "potential money laundering path" is immediately generated and submitted to the risk control engine for evaluation.

[0045] Feature extraction has entered the streaming computing paradigm. The traditional sliding window has been replaced by a hybrid mechanism of session window and rolling window: the session window captures the continuous operational behavior of a single customer, with a timeout threshold set to 15 minutes; the rolling window continuously outputs aggregated features at granularities of 1 minute, 5 minutes, 30 minutes, and 2 hours. The dynamic feature set has been significantly expanded to 12 items, including high-frequency transaction density, account balance mutation rate, related transaction network density, cross-border transaction ratio, credit approval rejection rate trend, and reserve provision change slope. These features do not require persistent storage and are directly pushed as feature vectors to downstream model services.

[0046] The model training and adaptive optimization modules employ completely different technical approaches: Ensemble learning models are abandoned in favor of Deep Temporal Convolutional Networks (TCNs), which offer advantages such as constant receptive field and parallel computing, making them more suitable for low-latency inference scenarios. The TCN backbone consists of eight stacked dilated causal convolutional layers with an exponentially increasing dilation factor (1, 2, 4, ..., 128), ensuring effective modeling of historical behavioral sequences lasting several hours. The output layer connects to a sigmoid activation function, generating a risk score between 0 and 1. Model training is still performed offline based on historical data, but the deployment method is changed to a dual-channel A / B testing architecture: the main channel runs a stable version, while the secondary channel tries a new model. Both receive the same event stream in parallel, and the output results are compared by an arbitrator. Traffic switching only occurs when the new model improves key metrics (such as F1-score) by more than 2%.

[0047] The online learning mechanism has been upgraded to true real-time incremental learning. The system is equipped with a dedicated parameter server cluster, triggering a micro-batch gradient update every 1000 new samples, using the Adam optimizer to adjust the weight parameters of the last layer of the TCN. To prevent catastrophic forgetting, an Elastic Weight Consolidation (EWC) algorithm is introduced, adding a regularization term to the loss function to protect important parameters. The model performance monitoring unit samples the area under the ROC curve (AUC) every 5 minutes; if two consecutive sampling values ​​decrease by more than 0.01, it automatically rolls back to the previous stable version.

[0048] The decision-making process is fully integrated with the internal approval workflow of financial institutions. Risk alerts are no longer limited to notifications but directly generate pending task orders, push them to the compliance specialist's console, and lock certain functional permissions for relevant accounts. The strategy suggestion module incorporates a game theory model to assess the combined impact of different intervention measures on customer churn rate and the probability of regulatory penalties, recommending the optimal balance point solution. The visualization output uses a real-time situational awareness dashboard, displaying suspicious fund flows in a dynamic network diagram. Node size reflects account activity, edge thickness represents transaction frequency, and color changes indicate risk level transitions. This implementation has been applied to two national commercial banks, reducing suspicious transaction identification response time from hours to seconds, improving anti-money laundering investigation efficiency by 90%, and decreasing the regulatory reporting error rate to 0.03%.

[0049] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for intelligent analysis and decision of industry-finance-tax data, characterized in that, The method comprises the following steps: Batch acquisition of original business data, financial data and tax data through enterprise resource planning systems, financial accounting systems, electronic invoice platforms and business operation terminals; Standardized cleaning processing of the original data to generate a standard data set conforming to a unified data model structure; Cross-system association of data records in the standard data sets from different systems that belong to the same economic transaction based on enterprise unique identification codes and transaction time stamps, semantic normalization of transaction descriptions that express differences but have the same economic essence by combining abstract keyword hash matching technology, and construction of a three-dimensional association matrix; Extraction of multi-dimensional dynamic characteristics reflecting the operating status of the enterprise from the three-dimensional association matrix; Sliding window statistical calculation of each dynamic characteristic at multiple time granularities using a multi-scale time aggregation strategy to obtain multi-dimensional feature components including mean, variance, maximum, minimum and change slope, and splicing of all feature components into a high-dimensional dynamic feature vector; Input of the high-dimensional dynamic feature vector into a pre-trained hybrid machine learning prediction model to output a prediction result; Deviation comparison between the prediction result and a preset threshold, triggering of a risk warning mechanism when the prediction deviation of any index exceeds the set range, and generation of a risk disposal plan containing key influencing factor tracing and countermeasure suggestions; Based on the strategy recommendation result output by the prediction model, solving of an optimal resource allocation scheme under the constraints of inventory level, customer credit rating and supplier performance capability, and display of the change simulation effect of key performance indicators before and after strategy implementation through visual charts. 2.The method of claim 1, wherein, In the process of performing standardized cleaning processing on the original data, a double-threshold discrimination method based on statistical distribution characteristics is used for anomaly detection on numerical fields, specifically: the mean μ and standard deviation σ of continuous variables in the sample period are calculated, the lower threshold is set to μ-3σ, and the upper threshold is set to μ+3σ, if a data point exceeds this interval, it is marked as a suspected abnormal value; then a pre-set business logic rule library is called to perform secondary verification on the suspected abnormal value, if the defined contradiction conditions in the rule library are met at the same time, it is determined as a real abnormal value and is modified or excluded. 3.The method of claim 1, wherein, When constructing the three-dimensional association matrix, a three-level index structure is used to accelerate the entity matching process, the first level is the organization code index, which is used to quickly locate all subsystem data under the same legal entity; the second level is the transaction date and time sequence number joint index, which is used to arrange business events in time sequence within the same entity; the third level is the abstract keyword hash index, which is used to identify transaction descriptions with the same economic essence but different text expressions.

4. The method of claim 1, wherein, The time granularities set in the multi-scale time aggregation strategy include four levels of sliding windows of 1 day, 7 days, 30 days and 90 days, the statistics of each dynamic characteristic variable in each window are calculated independently, and the results are spliced to form a dynamic characteristic representation with space-time perception ability.

5. The method of claim 1, wherein, The gradient boosting tree sub-model in the mixed machine learning prediction model is used to process nonlinear classification and regression tasks, and is specially used to identify sparse distributed risk signals; the deep neural network sub-model gives higher weight to key time nodes in the historical sequence through the attention mechanism, and enhances the response ability to external environment mutations.

6. The method of claim 1, wherein, The model adaptive optimization step also includes: maintaining a sliding historical window that only retains actual observation data of the last 12 months as a retraining set, periodically intercepting the data in the window to incrementally update the mixed machine learning prediction model; setting a model performance monitoring unit to compare the prediction error change amplitude of the new and old models on the validation set, and if the improvement does not meet the preset judgment standard, the original model version is maintained.

7. The method of claim 1, wherein, The generated risk disposal plan includes calling an explainability algorithm to trace the key feature contribution degree ranking that causes the prediction deviation, and outputting specific action instructions according to the preset logic chain through the built-in rule engine. 8.The method of claim 1, wherein, When solving the optimal resource allocation scheme, a linear programming solver is introduced to maximize the expected revenue objective function under the premise of meeting multiple business constraint conditions, and output the optimal resource allocation ratio and execution priority.

9. The method of claim 1, wherein, The strategy implementation effect is displayed through a visual chart, including an interactive dashboard presenting key indicator evolution path curve graphs, risk hotspot geographical distribution heat maps, and strategy before and after KPI comparison bar charts, supporting users to drill into fine-grained data and adjust the assumed parameters to refresh the prediction results in real time.

10. A system for intelligent analysis and decision of industry-finance-tax data, characterized in that, The following modules are included: a data access and standardization module for collecting raw business, financial, and tax data from enterprise resource planning systems, financial accounting systems, electronic invoice platforms, and business operation terminals, and performing field type conversion, missing value filling, duplicate record deduplication, and illegal character filtering according to preset rules to generate structured standard data sets; A data fusion and correlation mapping module is used to accurately align standard data from different sources based on enterprise unique identification codes and transaction timestamps, and to implement semantic normalization of transaction descriptions using summary keyword hash matching technology to construct a three-dimensional correlation matrix containing business events, fund flows, and tax calculation paths; a feature extraction and dynamic coding module is used to extract multi-dimensional time series features from the three-dimensional correlation matrix and vectorize them, and to calculate the statistics of each feature at multiple time granularities using a multi-scale sliding window strategy to form high-dimensional dynamic feature vectors; a model training and adaptive optimization module is used to deploy a mixed architecture containing gradient boosting tree sub-models and deep neural network sub-models combined with attention mechanisms, to train their ability to predict future financial and tax indicators, identify compliance risks, and recommend business strategies using dynamic feature vectors as input, and to incrementally update model parameters using new data through a sliding historical window; a decision generation and visualization output module is used to generate executable management decision reports based on the risk score, trend prediction results, and strategy recommendations output by the model, and to present key indicator evolution paths, risk hotspot distributions, and strategy implementation effect simulations through an interactive dashboard to support management to quickly respond and dynamically adjust.

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